Evidence Report/Technology Assessment 

Number 74

The Effect of Health Care 

Working Conditions on 

Patient Safety

Agency for Healthcare Research and Quality
On December 6, 1999, under Public Law 106-129, the Agency for Health Care Policy and 

Research (AHCPR) was reauthorized and renamed the Agency for Healthcare Research and 

Quality (AHRQ). The law authorizes AHRQ to continue its research on the cost, quality, and 

outcomes of health care and expands its role to improve patient safety and address medical 

errors.

This report may be used, in whole or in part, as the basis for development of clinical practice 

guidelines and other quality enhancement tools, or a basis for reimbursement and coverage 

policies. AHRQ or U.S. Department of Health and Human Services endorsement of such 

derivative products may not be stated or implied.
Evidence Report/Technology Assessment

Number 74

The Effect of Health Care Working 

Conditions on Patient Safety

Prepared for:

Agency for Healthcare Research and Quality

U.S. Department of Health and Human Services

http://www.ahrq.gov

Contract Number: 290-97-0018, Task Order No. 10

Prepared by:

Oregon Health & Science University Evidence-based Practice Center

Portland, Oregon

David H. Hickam, MD, MPH, Principal Investigator

Susan Severance, MPH

Adrianne Feldstein, MD, MS

Leslie Ray, PhD, RN

Paul Gorman, MD

Sherrie Schuldheis, PhD, RN

William R. Hersh, MD

Kathryn Pyle Krages, AMLS, MA

Mark Helfand, MD, MS, EPC Director

Drs . Hickham, Schuldheis, and Helfand are affiliated with the VA Medical Center, Portland, 

Oregon. Dr. Feldstein is also affiliated with the Kaiser Permanente Center for Health Research, 

Portland, Oregon.

AHRQ Publication No. 03-E031

May 2003
This document is in the public domain and may be used and reprinted without permission except 

those copyrighted materials noted, for which further reproduction is prohibited without specific 

permission of the copyright holders.

Suggested Citation:

Hickam DH, Severance S, Feldstein A, et al. The Effect of Health Care Working Conditions on 

Patient Safety. Evidence Report/Technology Assessment Number 74. (Prepared by Oregon 

Health & Science University under Contract No. 290-97-0018.) AHRQ Publication No. 03-E???

Rockville, MD: Agency for Healthcare Research and Quality. April 2003.
iii v

Preface

The Agency for Healthcare Research and Quality (AHRQ), through its Evidence-based 

Practice Centers (EPCs), sponsors the development of evidence reports and technology 

assessments to assist public- and private-sector organizations in their efforts to improve the 

quality of health care in the United States. The reports and assessments provide organizations 

with comprehensive, science-based information on common, costly medical conditions and new 

health care technologies. The EPCs systematically review the relevant scientific literature on

topics assigned to them by AHRQ and conduct additional analyses when appropriate prior to 

developing their reports and assessments.

To bring the broadest range of experts into the development of evidence reports and health 

technology assessments, AHRQ encourages the EPCs to form partnerships and enter into 

collaborations with other medical and research organizations. The EPCs work with these 

partner organizations to ensure that the evidence reports and technology assessments they 

produce will become building blocks for health care quality improvement projects throughout 

the Nation. The reports undergo peer review prior to their release.

AHRQ expects that the EPC evidence reports and technology assessments will inform 

individual health plans, providers, and purchasers as well as the health care system as a whole 

by providing important information to help improve health care quality.

We welcome written comments on this evidence report. They may be sent to: Director, 

Center for Practice and Technology Assessment, Agency for Healthcare Research and Quality, 

6010 Executive Blvd., Suite 300, Rockville, MD 20852.

Carolyn Clancy, M.D. Robert Graham, M.D. 

Director Director, Center for Practice and 

Agency for Healthcare Research Technology Assessment

 and Quality Agency for Healthcare Research and Quality

The authors of this report are responsible for its content. Statements in the report should not be 

construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. 

Department of Health and Human Services of a particular drug, device, test, treatment, or other 

clinical service.
vi iv

Acknowledgments

Valuable assistance for this project was provided by several groups of individuals, including 

an expert panel, a technical advisory panel, and external peer reviewers. The names of these 

individuals are included in Appendix A, and the research team would like to acknowledge their 

helpful contributions to the project. We also acknowledge the efforts of the Evidence-based 

Practice Center support staff, specifically Patty Davies, MS, Maria Shaffer, Crystal Swails, and 

Susan Wingenfeld. Our Task Order Officer from the Agency for Healthcare Research and 

Quality, Ernestine Murray, RN, MAS, provided invaluable assistance. We also acknowledge 

the assistance of Eric Coleman, MD, University of Colorado, Denver, CO; Harvey J. Murff, 

MD, Harvard Medical School, Boston, MA; Dean Sittig, PhD, Kaiser Center for Health 

Research, Portland, OR; and Robert M. Wachter, MD, University of California, San Francisco, 

CA, for advice during the course of the project.
v

Structured Abstract

Objectives. The purpose of this report is to compile and summarize existing evidence on 

the aspects of the working environment that impact patient safety. Five categories of 

working conditions were evaluated: workforce staffing, workflow design, personal/social

issues, physical environment, and organizational factors. 

Search Strategy. Five bibliographic databases were searched; the databases were chosen 

to include citations from both the healthcare and non-healthcare literature. The databases 

included MEDLINE (with HealthSTAR), CINAHL, PsycINFO, EBSCO, and the 

Campbell Collaboration. Searches were conducted back to 1980 for MEDLINE and 

EBSCO, back to 1982 for CINAHL, and back to 1984 for PsycINFO. Additional studies 

were identified through hand searches of reference lists and selected tables of contents. 

Unpublished studies were identified through discussions with content experts.

Selection Criteria. The criterion for inclusion in the literature review was that the article 

addressed patient safety or human performance, together with predetermined definitions 

of working conditions. Studies with no original data were excluded unless they were 

systematic literature reviews. Selection criteria were tested through dual reviews by a 

second investigator.

Data Collection and Analysis. The articles included in the general literature review 

guided the definition of subcategories within the five main categories of working 

conditions. The quality of the evidence in individual studies was assessed through 

separate ratings of study design and execution. For each of the working-condition 

categories, six key questions were addressed to classify the nature of the evidence linking 

the working condition to aspects of patient safety. 

Main Results. The strongest evidence linking working conditions to aspects of patient 

safety is in the areas of workforce staffing and workflow design. Specific working 

conditions in these two categories affect both rates of medical errors and the incidence of

patient outcomes related to patient safety. The patient outcomes affected include 

hospital-acquired infections, decubitis ulcers, and patient falls. There is not consistent 

evidence that working conditions affect the rates of preventable deaths in hospitals. 

Conclusions. The available evidence supports the recommendation that healthcare 

systems initiate demonstration projects and translational research to modify working 

conditions with the goal of improving patient safety. Specific areas in which such efforts 

are likely to be successful include: changes in nursing staffing, channeling high-risk 

technical procedures to high-volume physicians, avoidance of distractions in the 

healthcare workplace, and processes to improve information exchange between hospital 

and non-hospital settings. In addition, previous suggestive-but-inconclusive research 

indicates that limited investigations of workplace stress, lighting conditions, and 

organizational factors will clarify whether these additional working conditions affect 

patient safety.
Overview

Do the working conditions of health care

personnel contribute to the incidence of medical

errors? This question is often raised during public

discussion of ways to improve patient safety. How

much do issues of nurse staffing and doctors’

hours, for example, contribute to the estimated

44,000 to 98,000 deaths per year in hospitals due

to medical errors? 

The objective of this report is to identify and

summarize evidence from the scientific literature

on the effects of health care working conditions on

patient safety. The report also identifies relevant

information from industries outside of heath care. 

Working conditions were classified into five

categories: workforce staffing, workflow design,

personal/social factors, physical environment, and

organizational factors. The classification system for

working conditions was derived from existing

literature and advice from an expert panel. It is

consistent with human factors research in multiple

disciplines and industries such as aviation and

nuclear power. Workforce staffing refers to job

assignments and includes four principal aspects of

job duties: the volume of work assigned to

individuals, the professional skills required for

particular job assignments, the duration of

experience in a particular job category, and work

schedules. Workflow design focuses on the job

activities of health care workers, including

interactions among workers and the nature and

scope of the work as tasks are completed.

Personal/social factors refer to individual and

group factors such as stress, job satisfaction, and

professionalism. Physical environment includes

aspects of the health care workplace such as light,

aesthetics, and sound. Organizational factors are

structural and process aspects of the organization

as a whole, such as use of teams, division of labor,

and shared beliefs.

The researchers developed an analytic

framework to define how working conditions are

related to patient safety. Antecedent conditions,

which are external factors such as personal

characteristics of workers and fixed structural

characteristics of the system (e.g., geographic

location, regulations, and legislation), can affect

the impact of working conditions on patient

safety. Working conditions are viewed either as

resources that improve work quality or as demands

that impede work quality. Working conditions

potentially affect patient safety, which leads to

patient outcomes. 

The researchers also developed a model of

patient safety to help frame the key questions and

provide a way to synthesize data reported in

studies. The model is drawn from injury analysis

and incorporates elements of both processes and

outcomes. It is based on the relationships between

medical errors (defined as the failure of a planned

action to be completed as intended, or the use of a

wrong plan) and adverse outcomes (injuries caused

by health care rather than underlying disease).

Reporting the Evidence

The key questions derive directly from the

analytic framework. Each key question applies to

all five categories of working conditions; specific

working conditions are inserted into the key

questions for each of the five categories. The key

questions permitted the research team to classify

the entire body of evidence for each category and

to derive a judgment about the strength of

evidence regarding the contribution of the

Evidence Report/Technology Assessment

Number 74

The Effect of Health Care Working Conditions on

Patient Safety

Summary

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES • Public Health Service

Agency for Healthcare Research and Quality
working condition categories to overall patient safety. The six

key questions are:

1. Do working conditions affect patient outcomes that are

related to patient safety?

2. Do working conditions affect the rate of medical errors?

3. Do working conditions affect the rate of recognition of

medical errors after they occur?

4. Do working conditions affect the probability that adverse

events will occur following detected or undetected medical

errors?

5. Does the complexity of the plan of care influence whether

working conditions affect patient outcomes that are

related to patient safety?

6. Do working conditions affect measures of service quality

in industries other than health care?

The populations of interest for this report include health care

workers, patients, and workers in industries other than health

care. The outcomes considered are defined in the analytic

framework and model of patient safety and hence the key

questions. They include patient outcomes, medical errors, and

adverse events.

Methodology

To identify relevant literature the researchers searched five

databases: MEDLINE® (with HealthSTAR), CINAHL®,

PsycINFO, EBSCO, and the Campbell Collaboration. The

Campbell Collaboration is an international effort modeled on

the Cochrane Collaboration. The Campbell Collaboration

prepares, maintains, and disseminates systematic reviews of the

effectiveness of social and educational policies and practices. Its

Social, Psychological, Educational and Criminological Trials

Register (C2-SPECTR) is a registry of randomized and possibly

randomized trials in education, social work and welfare, and

criminal justice. The researchers searched MEDLINE (1980 to

2002) and CINAHL (1982 to 2002) to capture the health care

literature, and they searched PsycINFO (1984 to 2002) and

EBSCO (1980 to 2002) to capture literature outside of health

care. The searches were limited to the years 1980 to 2002

because most contemporary quality management and

accreditation systems have been implemented since 1980.

Searches were performed separately for each of the five

categories of working conditions (workforce staffing, workflow

design, personal/social factors, physical environment, and

organizational factors). Search strategies were developed by the

lead investigator for each working condition category, using

MeSH® terms where possible. Searches were limited to human

studies and those in the English language or with English

abstracts. The searches resulted in a total of 23,179 citations.

The lead investigator for each working condition category

applied a set of inclusion/ exclusion criteria to the

titles/abstracts in their area. To assess the interobserver

reliability of this process, dual reviews were performed on

random samples of citations. Full-text papers were retrieved for

studies judged to be possibly relevant and assessed again for

relevance using the same inclusion/exclusion criteria.

Studies were then abstracted using data-abstraction

guidelines, and quality ratings were applied. The researchers

rated design suitability and quality of study execution. They

constructed evidence tables, and a second investigator reviewed

the studies to verify the accuracy of the summary information

and quality ratings. 

Findings

After the investigators had reviewed all citations for possible

relevance, over 1,000 papers were retrieved and read; of these,

912 papers were excluded from further review. Of the excluded

papers, 730 were health care related and 182 focused on

industries outside of health care. The bibliography includes the

excluded studies. 

A total of 115 studies were found to have evidence relevant

for answering the key questions and were included in evidence

tables. In some cases, additional studies were found to provide

evidence that was indirectly related to key questions. The

volume of available evidence varied considerably among the

categories of working conditions, which reflects extensive

variability in the amount of research conducted in these

domains.

With the exception of Key Question 4 (regarding impact on

the probability of adverse events), valid evidence was found for

all key questions. The largest amount of available evidence

applied to Key Question 1, and there was sufficient evidence to

conclude that several different specific working conditions

affect outcomes that are related to patient safety. There also was

sufficient evidence to conclude that some working conditions

affect rates of medical errors (Key Question 2).

The results of studies of factory and office workers are

generally consistent with similar studies performed in health

care settings (Key Question 6). These findings suggest that

studies of working conditions in other industries are relevant to

health care and can be used to expand the fund of knowledge

about working conditions in health care.

The systematic literature review provided sufficient evidence

to make specific recommendations about strategies for

improving patient safety. These recommendations can be

summarized as follows:

• Strategies to increase staffing levels of licensed and

unlicensed nurses in both acute-care hospitals and nursing

homes will likely lead to improved patient outcomes.

• Preventable complications are lower when complex

technical procedures are performed by physicians who

conduct them frequently (i.e., high-volume physicians).

• Duration of experience of the health professional is

associated with better patient outcomes for some types of

clinical care.

2
• Systems to reduce interruptions and distractions will likely

reduce the incidence of medical errors.

• Systems to improve information exchange, transfer of

responsibility, and continuity of care between hospital and

nonhospital settings (“hand offs”) decrease medication

errors and, in some settings, hospital re-admissions.

• Levels of ambient noise in health care settings do not

adversely affect patient safety.

Future Research

For several specific working conditions, there is evidence that

the working condition affects patient safety, but the evidence

comes from few studies and is insufficient to draw clear

conclusions. Further research to clarify and confirm the

findings from existing studies will permit judgments to be

made about the importance of these working conditions. The

areas in which such targeted research is indicated include

workplace stress, workplace lighting conditions, and several

aspects of organizational factors.

With the exception of selected work processes pertaining to

workflow design, most of the evidence on the relationship of

working conditions to patient safety is derived from nonexperimental studies. Thus, there remain unanswered questions

about the magnitude of improvement in patient safety that can

be achieved by improving working conditions. There is a need

for significant future research that evaluates how specific

workplace interventions will affect patient outcomes. Such

research could be conducted as clinical trials or as carefully

designed demonstration projects and program evaluation

studies.

Availability of the Full Report

The full evidence report from which this summary was

derived was prepared for AHRQ by the Oregon Health and

Science University Evidence-based Practice Center under

contract number 290-97-0018. It is expected to be available in

spring 2003. Printed copies may be obtained free of charge

from the AHRQ Publications Clearinghouse by calling 800-

358-9295. Requesters should ask for Evidence

Report/Technology Assessment No. 74, The Effect of Health

Care Working Conditions on Patient Safety. When available,

Internet users will be able to access the report online through

AHRQ’s Web site at: www.ahrq.gov.

3
AHRQ Pub. No. 03-E024

March 2003

ISSN 1530-440X
Evidence Report

7

Chapter 1. Introduction

Patient Safety and Medical Errors

The healthcare delivery system includes a broad range of technical resources and personnel. 

Healthcare services expose patients to the risk of unintentional injuries that can range from trivial 

and nondisabling to severe permanent disability or death. Efforts to minimize these injuries have 

led to the patient safety movement, and the generally accepted definition of patient safety is the 

prevention and amelioration of adverse outcomes or injuries stemming from the processes of 

health care.1

In order for health care to be safe, efficacious, and of high quality, it is essential that there be

optimal coordination of the structural and cultural elements of the system.2 Because this 

requirement is not always met, medical errors occur, and patient safety is threatened. The sources 

of error are diverse, including failure of process safeguards, faults in equipment, or lack of 

teamwork. Patient safety is thereby dependent on the optimal interactions of the components of 

the healthcare system, with errors being minimized. A medical error is defined as the failure of a 

planned action to be completed as intended ("error of execution") or the use of a wrong plan to 

achieve an aim ("error of planning").1 Because of the complexity of the healthcare system, errors 

may occur in hospitals, outpatient clinics, nursing homes, pharmacies, urgent care centers, and 

patients' homes. 

Distinguishing between active and latent error provides insight into the need to understand a 

work process and its weaknesses.3 Active errors occur at the level of the frontline operator, and 

their effects are felt almost immediately. Latent errors are removed from the direct control of the 

operator and include poor system design and poorly structured organizations. Latent errors pose 

the greatest threat to safety, because they often are unrecognized and have the capacity to result 

in multiple types of active errors. A focus on active errors lets the latent failures remain in the 

system, making the system even more prone to future failure. The key to reducing errors is to 

focus on improving the systems of delivery of care as well as the performance of individual 

workers. 

Clinicians and managers accept that efforts should be made to reduce errors, but the best 

strategies for error reduction have not always been well understood. In recent years, systematic 

efforts to improve quality have become widespread,4 and there has been progress in 

understanding how the processes and outcomes of care are related.5, 6 The progress in quality 

improvement in health care has led to an environment of proactive approaches to recording 

information about the processes of clinical care that makes investigations of medical errors more 

feasible. 

Contemporary research on patient safety has its origin in the examination of adverse 

outcomes of care. Adverse outcomes are defined as injuries caused by health care rather than by 

underlying disease. While not all adverse events are preventable, some are due to preventable 

errors. Adverse outcomes were first systematically measured in a study conducted by the 

California Medical Association in the 1970s.7 The study found that significant adverse events 

occurred in 4.65 percent of hospitalizations. Two subsequent large-scale studies reported 

adverse event rates of 3.7 percent8 and 2.9 percent.9 By pooling the data from multiple studies, it 

has been estimated that significant adverse events occur in 3 to 4 percent of hospitalizations.10

Patients undergoing surgical procedures account for two-thirds of all adverse events.11 There has 
8

been less examination of the incidence of adverse events among outpatients. One study reported 

the rate to be less than one per 10,000 clinic visits, but this is undoubtedly an underestimate.12

While the earlier studies focused on negligence as a cause of adverse events, other research 

examined errors due to faulty implementation of systems of clinical care. Optimal clinical 

systems feature safeguards to compensate for simple mistakes made by individual clinicians. In 

one study, failures at the system level were responsible for 75 percent of adverse drug events.13 

System improvements have also been shown to reduce error rates and improve the quality of 

health care. The Agency for Healthcare Research and Quality (AHRQ) recently published an 

evidence report on patient safety practices, defined as processes or structures whose application 

reduces the probability of adverse events resulting from exposure to the health care system. 

When applying this definition, the report focused on a broad range of specific health care 

practices, most of which are applicable only to the inpatient setting.14

Purpose and Scope of this Report

The purpose of the current report is to compile and summarize existing evidence on the 

aspects of the work environment that impact patient safety. While much of the relevant evidence 

comes from research conducted in healthcare settings, this report also has compiled evidence 

from the large body of human factors research conducted in the non-healthcare setting. This 

non-healthcare evidence provides insight into how work environment changes may improve 

patient safety.

Theoretical and empirical research from industries such as aviation and nuclear power 

provide a basis for a classification system of working conditions. This work allows us to 

organize the evidence derived from research conducted in healthcare and non-healthcare settings. 

The classification system used for this report is consistent with definitions used in the human 

factors research field.

We convened a panel of experts to define the working conditions addressed in this report. 

The panel identified five distinct categories of working conditions, as follows:

• Workforce staffing

• Workflow design

• Personal/social

• Physical environment

• Organizational factors 

A common feature of these five categories is that the working conditions are potentially 

amenable to change. However, the resources needed to bring about change can vary greatly 

across the categories. For example, changing physical environment working conditions usually 

requires control over the mechanical and aesthetic aspects of healthcare facilities. Alternatively, 

changing staffing conditions requires access to financial resources and deployment of workers 

across organizational levels.

The audience for this report includes clinicians, health system managers, policy makers, and 

health services researchers. Individual clinicians can use the information in this report to 

improve their understanding about how features of their jobs affect their professional 

performance. This understanding can then be used to evaluate whether any changes in their own 
9

job procedures are indicated. Health system managers can use the information to evaluate how 

changes in the organization of clinical delivery systems can achieve improvement in patient 

outcomes. Policy makers can use the information to guide decisions about resource 

commitments for system changes and research priorities. Health services researchers can use the 

information to identify gaps in knowledge that can guide new research initiatives.

Definitions of Working Conditions

Because the definitions of working conditions formed the basis for literature search strategies 

used in this project, a systematic process was used to solidify the definitions at the beginning of 

the project. A panel of experts on working conditions was identified and convened for a one-day 

meeting in November 2001 in Portland, Oregon. The members of the panel, listed in Appendix 

A, included healthcare researchers and human factors researchers in other fields such as aviation. 

The charge to the expert panel was to gain consensus on a classification system for working 

conditions that was broad enough to include all-important research relevant to patient safety. 

The panel was also asked to help with the conceptualization of the problem and definition of key 

questions to be addressed in the report. The panel was provided with background information on 

patient safety and a framework for understanding how patient safety is related to healthcare 

delivery.

The expert panel provided guidance on the categorization of working conditions that would 

permit sufficient coverage of all pertinent existing literature. Working definitions of the five 

categories then needed to be finalized before initiating the literature searches. These definitions 

were further refined during the search process and following review of all search results. 

Because the relevant literature needed to be assigned to single categories for reporting the 

evidence, the definitions were constrained so as to avoid overlap across categories. Specific 

factors (such as seasonal effects) that could reasonably be assigned to multiple categories were 

assigned to the category that was the closest fit. The final definitions of each category are 

summarized in the following sections.

Workforce Staffing

Workforce staffing refers to the job assignments of healthcare workers. It includes four 

principal aspects of job duties:

1. The volume of work assigned to individuals. This has been defined in different ways 

depending on the nature of the job assignment. For pharmacists it has been defined as the 

number of prescription orders filled per day. For nursing staff, it has been defined as the number 

of patients cared for during a work shift. For physicians, it has been defined as the number of a 

certain procedure (such as coronary arteriography or resection of a gastric carcinoma) performed 

per year. The most common hypothesis is that higher workload is associated with a larger rate of 

errors and/or adverse outcomes. However, most research on physician performance has been 

based on the hypothesis that higher workload is associated with a lower error rate, due to 

differences in the unit of measurement and nature of clinical tasks.

2. The professional skills required for particular job assignments. This has usually been 

defined as attainment of advanced academic degrees or specialty certifications. However, some 

research has examined the effects of focused training programs for existing staff members. The 

usual hypothesis has been that higher levels of prior training are associated with lower error 
10

rates. Current concerns over the demographic trends toward a shrinking workforce for some 

professional areas (particularly nursing) has also led to research on the effects of shifting some 

job duties to less highly trained personnel (such as using unlicensed personnel for performance 

of nursing tasks and using pharmacy technicians to provide pharmacy services as allowed by 

state/federal law).

3. The duration of experience in a particular job category. Duration is usually measured as 

the number of years an individual has worked in a particular job category. Some studies of 

physicians in academic settings have used faculty rank as a measure of experience. The most 

common hypothesis is that longer experience is associated with lower error rates.

4. Effects of work schedules, including length of shift, days of the week worked, and 

temporal cycle effects (such as influence of time of week or season of year). A common 

hypothesis is that longer work shifts are associated with a greater incidence of errors. 

Workflow Design

Workflow design focuses on the process of delivering health care. Healthcare facilities are 

complex collections of simpler units organized to support the workflow to deliver patient care.15

Workflow design encompasses the interactions among workers and also between workers and 

the workplace. It also includes the nature and scope of the work as tasks are completed. In health 

care, as in other industries, hazards to workers and patients can be evaluated by examining 

specific work processes.15 This allows for the analysis of risks in the system and the impact of 

those risks on the worker and patient. A useful framework for analyzing workflow design 

integrates approaches from several disciplines, including organizational psychology, industrial 

engineering, biomechanics and ergonomics.16 For evaluating patient safety, workflow design 

includes task design and workplace design relevant to accomplishing the tasks. Task design 

includes such job characteristics as redundancy, complexity, distractions, and transfer of 

information and responsibility to others (“hand-offs”). Workplace design considers worker 

ergonomics for technology and equipment.

Personal/Social

This category of working conditions is concerned with the personal, professional, and social 

aspects of the healthcare work environment. The personal factors include stress, burnout, 

dissatisfaction, motivation, and control over work. Social factors include interrelationships 

among workers, such as collectivism, role ambiguity, discord, and support. Professionalism 

includes the values that are cultivated within professional disciplines such as nursing or clinical 

pharmacy.

Physical Environment

Physical environment working conditions include direct physical characteristics such as light, 

aesthetics, noise, air quality, toxic exposures, temperature, and humidity. This category also 

includes basic workplace design features, such as obstacles, physical layout, and distance from 

nursing stations. 
11

Organizational Factors

Organizational factors are structural and process aspects of the organization as a whole. For 

example, work structures such as the use of teams and the division of labor are organizational 

factors with potential influences on patient safety. Other organization-level factors include size, 

funding mechanisms (e.g., profit, not-for-profit), hospital type (e.g., teaching, private), and 

culture. Some aspects of the organization, such as size and funding base, are difficult to change. 

Other aspects, such as the use of team structures and culture, are more amenable to change. 

Organizational culture is what employees throughout an organization perceive and how this 

perception creates a pattern of beliefs, values, and expectations. Specific characteristics of 

organizational culture include managerial style, evaluation and reward systems, economic 

effects, hierarchy, accountability, decision latitude, and employee feedback. 

Analytic Framework

Our goals in constructing an analytic framework were to elucidate the major concepts 

pertinent to the work environment and the theoretical linkages between concepts. Our analytic 

framework is based on a model that characterizes working conditions as factors that can either 

improve work quality (referred to as resources) or impede work quality (referred to as 

demands).17-19 The quality of work in turn affects patient safety and patient outcomes.

Antecedent variables can also affect human performance. Antecedent factors can potentially 

moderate the effect of working conditions on patient safety, which in turn influences patient 

outcomes. These variables include personal characteristics of workers and structural 

characteristics of the system that cannot be changed. They include worker age, gender, personal 

health, job commitment, geographic location, and regulations and legislation that affect the 

healthcare system (Figure 1). Some research has been conducted on the influence of these 

factors upon the job performance of health professionals. For example, professional impairment 

due to chemical dependency, psychiatric impairments, or other medical conditions upon job 

performance has been an important area of inquiry.20-23 Since these antecedent variables were not 

classified as working conditions, research in these domains was not reviewed for this report. 

In order to synthesize data reported in studies, it also is necessary to have a model of patient 

safety. We have extended a model originally developed by the federal Quality Interagency 

Coordination Task Force.24 Our model (Figure 2) uses Haddon’s system of analyzing injuries, 

with pre-injury, injury, and post-injury phases.25 It also incorporates Reason’s model of 

accidents based on system processes and human errors.3 Because it includes elements of both 

processes and outcomes, this model provides a framework for classifying and tabulating the 

types of data reported in studies of working conditions in health care.

This report does not consider evidence about other effects of working conditions on health 

professionals, such as injuries, turnover, or absenteeism (though these are obviously important in 

their own right), except where they are included with more direct measures of patient safety or 

harm. Similarly, this report does not consider evidence about other effects of working 

conditions on clinical practices, such as measures of the relative desirability or quality of care, 

except where the focus is on errors or patient safety. Finally, the relationship between clinical 

practices and patient outcomes is often neither direct nor certain. Even when clinical practices 

are ideal, patients may or may not improve, and may even deteriorate (Figure 2). Likewise, 

when errors do occur, patient outcomes may or may not reflect this (a “close call” is an error that 
12

does not lead directly to an adverse event). Further complicating this picture is the substantial 

disagreement that exists in expert judgments about whether errors have occurred and whether, 

when they have occurred, adverse outcomes have been the result.26, 27

Key Questions

Our set of key questions was derived directly from the analytic framework. The core group 

of key questions applies to all five categories of working conditions. The key questions 

permitted the research team to classify the entire body of evidence for each category and to 

derive a judgment about the strength of evidence regarding the contribution of the working 

condition categories to overall patient safety. The six key questions are:

1. Do working conditions affect patient outcomes that are related to patient safety?

2. Do working conditions affect the rate of medical errors?

3. Do working conditions affect the rate of recognition of medical errors after they occur?

4. Do working conditions affect the probability that adverse events will occur following 

detected or undetected medical errors?

5. Does the complexity of the plan of care influence whether working conditions affect 

patient outcomes that are related to patient safety?

6. Do working conditions affect measures of service quality in industries other than health 

care?

The Evidence-based Approach 

An evidence report focuses attention on the strength and limits of evidence from published 

studies about the delivery of health care. The development of an evidence report begins with a 

careful formulation of the problem. In this phase, a preliminary review of the literature and input 

from experts, stakeholders, and patients may be used to identify the patient populations, 

interventions, health outcomes, and harms. An evidence report also emphasizes the quality of 

the evidence, giving more weight to studies that meet high methodological standards that reduce 

the likelihood of biased results. An evidence report pays particular attention to the 

generalizability of studies performed in controlled or academic settings. Studies that reflect 

actual clinical effectiveness in unselected patients and community practice settings are 

highlighted. 

In the context of developing clinical guidelines, evidence reports are useful because they 

define the limits of the evidence, clarifying when the assertions about the value of the 

intervention are based on strong evidence from clinical studies. The quality of the evidence on 

effectiveness is a key component, but not the only component, in making decisions about clinical 

policies. Additional criteria include acceptability to physicians or patients, the potential for 

unrecognized harms, and cost-effectiveness.

Previous Systematic Reviews

In conducting the literature reviews that formed the basis of this report, previously performed 

systematic reviews were identified and are summarized where pertinent in the following 
13

chapters. When possible, we avoided duplicating work reviewed in other good-quality evidence 

reports and systematic reviews. Of particular note is AHRQ Evidence Report/Technology 

Assessment Number 43 (Making Health Care Safer: A Critical Analysis of Patient Safety 

Practices), published in 2001.14 That report summarizes evidence on a variety of patient safety 

practices and includes information on several types of workflow design working conditions and 

also on some other categories of working conditions. Another notable systematic review 

examined the relation of hospital volume to quality.28
17

Chapter 2. Methodology

Literature Search and Selection Methods

Sources

To identify relevant literature we searched five databases: MEDLINE (with HealthSTAR), 

CINAHL, PsycINFO, EBSCO, and the Campbell Collaboration. PsycINFO indexes 

dissertations as well as published articles in a wide range of journals not included in MEDLINE. 

Within EBSCO we searched Health Source: Nursing/Academic Edition, Academic Search Elite, 

Business Source Premier, and MasterFILE Premier. The Campbell Collaboration is an 

international effort modeled on the Cochrane Collaboration whose mission is to prepare, 

maintain, and disseminate systematic reviews of the effectiveness of social and educational 

policies and practices. Its Social, Psychological, Educational and Criminological Trials Register 

(C2-SPECTR, http://128.91.198.137/) is a registry of studies in education, social work and 

welfare, and criminal justice.29 C2-SPECTR contains over 10,000 reports. It was constructed by 

searching three bibliographic databases that included Educational Research Information 

Clearinghouse (ERIC), Sociological Abstracts, and Criminal Justice Abstracts as well as hand 

searching 48 journals in sociology, psychology, education, criminology and other fields and 

other specialized reference lists, bibliographies, collections of individuals, and other sources. 

Only randomized and possibly randomized trials are included in the C2-SPECTR database.

We searched MEDLINE (back to 1980) and CINAHL (back to 1982) to capture the 

healthcare literature, and we searched PsycINFO (back to 1984) and EBSCO (back to 1980) to 

capture all available industry literature. We searched MEDLINE, CINAHL, and PsycINFO 

again during the course of the project, with the final search in August 2002. Following the 

searches of literature databases, additional published studies were identified through hand 

searches of reference lists and selected tables of contents. Unpublished studies and government 

reports were identified through discussions with content experts and electronic mail lists. 

Search Methods

Each search was organized by a lead investigator and was based on the set of six key 

questions; each of the five working conditions (workforce staffing, workflow design, 

personal/social, physical environment, and organizational factors) was searched separately. We 

defined the timeframe of the searches to be 1980 to the present, because most current quality 

management and accreditation systems have been implemented since 1980. Some of the 

databases did not go back as far as 1980, so these were searched in their entirety. Searches were 

limited to human studies and the English language but included foreign articles with an English 

abstract. Additionally, MEDLINE and CINAHL searches included a set of search terms that 

addressed medical errors, safety, and quality, and were constrained to articles pertaining to health 

care. Each search strategy was complemented by terms specific for one of the working condition 

categories. PsycINFO searches were not constrained to a specific industry and addressed 

performance measures and errors. As mentioned above, each search strategy was complemented 

by terms specific for one of the working condition categories. The search strategy for EBSCO 

was also not constrained to a specific industry. The search terms were working conditions and 
18

productivity, human performance, and employee productivity and performance. The search 

strings for the other databases (MEDLINE, CINAHL, PsycINFO, and the Campbell 

Collaboration) are provided in Appendix B. We entered retrieved titles/abstracts into an 

EndNote database, except for the EBSCO and Campbell Collaboration databases, where only 

included studies were entered.

 

Selection Processes

The searches resulted in a total of 23,179 citations. The lead investigator for each of the 

working condition categories reviewed the titles/abstracts for the citations that fell into their 

working condition category. Table 1 lists the eligibility criteria that were applied during the 

title/abstract review. For the non-healthcare industries, studies which met the criteria in Table 1 

were considered for inclusion if they reported any measure of work results or productivity. 

Table 1. Inclusion/exclusion criteria for judging titles/abstracts and full text papers

Code # Justification for codes

Inclusions

1 IN: For this category of working conditions

2 IN: For another category of working conditions

Indicate which one:

 A. Physical Environment

 B. Workflow Design

 C. Workforce Staffing

 D. Organizational Factors

 E. Personal/Social

3 IN: Good review or background article

Exclusions

4 OUT: Does not address any key question

5 OUT: Does not report original data

6 OUT: Wrong population (animal study, etc.)

7 OUT: <specific to topic> Write justification below
19

Due to budgetary constraints, citations that required inter-library loans or foreign language 

translation were assessed again by each lead investigator to determine if they were key citations 

to obtain. The full texts of citations that met inclusion criteria from the title/abstract review were 

then obtained and assessed again for inclusion/exclusion using the same criteria (Table 1). The 

search and selection of citations is depicted in Appendix C. The EBSCO and Campbell 

Collaboration databases were not searched independently for each of the five working condition 

topics and are indicated as such in Appendix C. 

Prior to the review of citations for inclusion, we conducted a process to ensure consistency in 

application of the review criteria. A dual review by the lead investigator for each category 

paired randomly with one of the other investigators was conducted on a random sample of the 

citations for each of the topics. Kappa values were calculated to assess inter-rater reliability.30 

The kappa values ranged from 0.10 to 0.56. The investigators met to review the disagreements 

and the inclusion/exclusion criteria. Consensus was achieved on the points that studies of human 

performance, staff turnover rates, and quality be included for the appropriate working condition 

so long as there were original data. Studies focusing on patient satisfaction and communication 

(except for technology) were excluded, in that they did not address any key question. Studies of 

providers’ perceptions of health care were included.

A second dual review was initiated on a separate random sample to reassess inter-rater 

reliability. This review was conducted after the lead investigators had completed their reviews 

of approximately half of the search result sets. The kappa va lues were similar among four of the 

topic areas and ranged from 0.41 to 0.48. Based on this outcome, a single review of the 

titles/abstracts by lead investigator was deemed acceptable for these four categories. 

The kappa value for the second dual review of one topic (organizational factors) was lower at 

0.14. The source of disagreement between the two reviewers for this working condition category 

was related to the rates of studies judged relevant. The lead investigator rated 5 percent of the 

studies relevant, while the second reviewer rated 15 percent as relevant. For the other four 

working condition categories, the rates of relevance judgments ranged from 11 percent to 20 

percent for all reviewers. A third investigator then reviewed a random sample of 200 of the 

citations in this category. The three investigators then met to review and revise the definition of 

organizational culture. Disagreements among the three investigators were found to be due to 

differences in how they defined the scope of organizational culture. The investigators gained 

consensus that the definition should be broadened (as reflected in the definition included in 

Chapter 1). The lead investigator then re-reviewed the entire set of citations. 

Data Abstraction and Synthesis

Data Abstraction

All studies rated as relevant on the basis of review of titles and abstracts were retrieved, 

photocopied, and reviewed by the lead investigator for each working condition category. 

Studies judged to have evidence about a key question were then abstracted, with the details of 

methods and results recorded on data abstraction tables. The ratings of quality of study design 

and execution were assigned at this time (see further details in next section). To provide 
20

guidelines for extraction of information into the data abstraction tables, the investigative team 

developed data abstraction guidelines (Appendix D).

Assessment of Study Quality

Our system for rating the quality of individual studies was based on previously published 

methods. Several approaches to evaluating quality were examined. We reviewed concepts from 

the U.S. Preventive Services Task Force, the Guide to Community Preventive Services, the 

Oxford Centre for Evidence-based Medicine, and AHRQ Evidence Report/Technology 

Assessment Number 47 Systems to Rate the Strength of Scientific Evidence.

31-34

The approach that was best suited to the types of studies included in this report is based on 

the system used by the Task Force on Community Preventive Services.32 This approach differs 

from that used in a previous evidence report on patient safety practices.14 Each study was 

assessed for quality using two ratings. The first, suitability of study design, was a three-tier 

approach (greatest, moderate, and least) relating the strength of the study design to threats of 

internal validity. We developed a modified version of design suitability for our topic. We rated 

studies greatest, moderate, or least based on comparison group status and measures of other 

factors affecting outcomes (Table 2). The second measure of quality was the quality of study 

execution. Here, six areas of threats to validity have been described,32 with the ratings of good, 

fair, or limited corresponding to 0-1, 2-4, or 5 or more limitations. We used a similar approach 

and rated study execution as good, fair, or poor based on internal and external validity. Internal 

validity was assessed by considering such factors as comparability of groups, differential loss to 

followup, measurement/instrumentation issues, maturation/pre-testing effects, and whether there 

was a clear description of interventions. External validity was assessed by considering such 

factors as a sensitized or pre-tested population, specialized/atypical population, selection biases 

(non-random subject selection), reactive effects of experimental settings, and multiple 

interventions. 

Table 2. Design suitability

Greatest Concurrent comparison groups and sufficient measures for other 

factors affecting outcomes

Moderate Non-concurrent comparison groups or insufficient measures for 

other factors affecting outcomes

Least Non-concurrent comparison or no comparison groups and 

insufficient measures for other factors affecting outcomes

 The overall strength of the evidence per topic area was assessed based on the criteria 

outlined by the Task Force on Community Preventive Services.32 The quality and quantity of the 

studies and size and consistency of the results were used to grade the overall strength of the 
21

evidence. Where evidence was available, the body of evidence for a topic area was rated as 

strong, sufficient, or insufficient according to the parameters outlined in Table 3.

Table 3. Assessment of strength of evidence

Strong At least two studies having greatest design suitability, good 

execution, and consistent results; or at least five studies having 

greatest design suitability, good or fair execution, and consistent 

results

Sufficient At least one study with greatest design suitability and good 

execution; or at least three studies having moderate or better 

design suitability, fair or better execution, and consistent results

Insufficient Too few studies to meet definition of sufficient evidence; or 

inconsistent results among multiple studies having some design 

or execution flaws

Methodologic Limitations

In this report we have adapted the methods of the systematic review35 to collect, evaluate, 

and synthesize the best available evidence that addresses the key questions. However, these 

methods were not developed for the domain of inquiry in which we are applying them, and this 

has implications for the conclusions that can be reached and the degree of certainty with which 

they can be stated. The methods and assumptions of the systematic review of health 

interventions, as practiced by organizations such as the AHRQ Evidence-based Practice Centers 

and the Cochrane Collaboration36 are not entirely applicable to a broad and diverse domain of 

inquiry such as that addressed in this report. Differences include those relating to a) underlying 

research traditions and assumptions; b) the search for relevant literature; c) selection of evidence 

for inclusion; d) appraisal of the relative validity and generalizability of studies; and e) 

combination or synthesis of evidence.

For healthcare interventions, the research tradition and assumptions of clinical epidemiology 

serve as the foundation both for original studies and syntheses of evidence. The underlying 

conditions, the interventions used to identify or treat them, and the outcomes of interest are 

generally well defined, with established and agreed upon means of identifying patients for 

inclusion and measuring outcomes of interest. The research designs used to investigate these 

interventions and conditions are well established and agreed upon, with defined limitations and 

biases and a familiar hierarchy of levels of evidence.33 As a result, studies of a particular 

intervention for a particular condition can usually be compared, results of selected studies can be 

combined, and the result often be expressed in terms of a single numeric estimate of effect, with 

an appropriate and precise estimate of precision.

In contrast, the evidence for this report is drawn from both healthcare and non-healthcare 

fields and includes diverse domains of inquiry, with different research traditions and 

assumptions, including cognitive science, sociology, industrial and human factors engineering, 

and others. In many cases there are no uniform definitions of underlying conditions, 

interventions, or outcome states, and no single commonly accepted means of identifying or 
22

measuring them. Furthermore, these domains of inquiry do not share a common, agreed upon 

hierarchical framework of research designs as in clinical epidemiology. As a result, it is often 

difficult to compare the results of studies, and it may be wholly inappropriate to attempt to 

combine them. 

Controversy continues about whether research from the paradigm of clinical epidemiology 

should be the sole basis for recommendations for action, in particular regarding behavioral, 

organizational, or information interventions.37-41 In view of this ongoing controversy, and the 

limitations of the application of the systematic review methods to the evidence examined herein, 

we have taken a hybrid approach, by summarizing evidence (where similarity of definitions and 

methods permits this) and separately describing individual studies (where summarization is not 

possible).

Synthesis of Evidence

The lead investigator for each category of working condition reviewed all studies included in 

the data abstraction tables and selected studies to be included in evidence tables. When multiple 

studies on a similar topic were available, studies that had the lowest design and execution ratings 

were not included in evidence tables. All studies included in evidence tables were reviewed by a 

second investigator to verify the accuracy of the summary information and quality ratings in the 

evidence tables. All evidence tables were then compiled by the principal investigator, and some 

studies were moved among tables if their results fit better with a different group of studies.

A technical advisory panel was identified, based on nominations from members of the expert 

panel. The technical advisory panel included six members with diverse backgrounds and broad 

knowledge of the field of patient safety (Appendix A). The final set of evidence tables and a 

draft of the narrative of the results chapter was submitted to the technical advisory panel for 

review. The panel then discussed the tables by a telephone conference call. The purpose of this 

review was to identify gaps in coverage of evidence from the entire domain of working 

conditions and to initiate supplementary literature searches based on the identified gaps.
23

Chapter 3. Results

Evidence on the Effects of Working Conditions

A total of 115 studies were found to have evidence relevant for answering the key questions 

and were included in evidence tables. In some cases, additional studies were found to provide 

evidence that was indirectly related to key questions. These studies are referred to and cited in 

the text but not included in evidence tables. The volume of available evidence also varied 

considerably among the categories of working conditions, which reflects extensive variability in 

the amount of research conducted in these domains. Three studies that provided evidence on 

more than one category of working conditions were included in more than one evidence table.42-

44

Over 1,000 papers were retrieved and read, and of these, 912 papers were excluded. Of the 

excluded papers, 730 were healthcare related and 182 focused on industries outside of health 

care. The bibliography includes the excluded studies. 

Evidence about the effects of working conditions on patient safety was derived from research 

conducted in both healthcare and non-healthcare settings. Generally, evidence from nonhealthcare sources was consistent with that from health care, and we found no examples of 

significantly divergent findings between the two sources. Trends found in other industries 

tended to be replicated when evaluated in healthcare settings. For example, studies of ambient 

noise conducted in factory settings had results very similar to subsequent studies of noise 

exposure conducted among dispensing pharmacists. Nevertheless, much of human factors 

research conducted outside of health care has not been replicated or successfully adapted to the 

healthcare environment. For example, although there is an extensive body of research on crew 

resource management for aviation, this approach has had only limited success when applied to 

medical settings.45

Evidence applicable to the key questions is summarized in the following sections. The 

sufficiency of evidence to answer the key questions was greatest for the categories of workforce 

staffing and workflow design and least for the personal/social category. For some sub-categories 

of workforce staffing and physical environment, evidence from non-healthcare settings 

contributed substantially to answering key questions. However, most of the evidence judged 

sufficient for answering key questions came from research in health care.

Workforce Staffing

The literature searches on workforce staffing yielded the largest number of citations of all the 

working condition categories (Appendix C). More than 80 percent of the identified citations and 

retrieved documents were obtained through searches of MEDLINE. Most studies in this domain 

have been observational, and few clinical trials were identified. However, because similar 

approaches have been used in multiple studies, it is possible to assess consistency of findings 

across multiple international sites. 

Research on workforce staffing in health care has been conducted for more than 25 years, 

and the investigations have addressed questions about workload issues, scheduling and coverage, 

and professional qualifications. Nearly all of the research has focused on nursing and physician 

staff. Thus, few conclusions can be drawn about staffing and scheduling for other health 
24

professionals. Likewise, there has been relatively little research on workforce staffing outside of

health care.

Much of the research about workforce staffing has been based on data from administrative 

data sets. These data sets have been derived from single hospitals, groups of hospitals, statewide 

sources (particularly New York, Maryland, Pennsylvania, and California), or national samples. 

The majority of studies has examined inpatient clinical care and has used adverse events as the 

outcome measure. Most of the adverse outcomes examined in these studies have clinical risk 

factors that affect their incidence, and nearly all the studies providing useful evidence have 

included methods of casemix adjustment. However, the casemix measures have varied 

considerably. Some studies include patients having only a single principal diagnosis, without 

additional severity measures for that chosen disease. Another common approach has been to use 

ICD-9 codes from discharge summaries to calculate diagnosis-based casemix measures. A 

common feature of these approaches is that the data source is clinical information drawn from 

administrative sources. 

The available evidence on workforce staffing falls into three major categories: workload, 

professional qualifications, and work schedules. The concept of workload refers to the amount 

of direct patient contact a healthcare worker experiences over a defined period of time. Studies 

of workload have been conducted examining both nursing staff and physicians. However, the 

reported relationship between workload level and patient safety has been based on different 

conceptua lizations for these two professional groups. For both licensed and unlicensed nurses, 

workload has been defined as the number of patients for which an individual is responsible to 

provide care during a work shift. Higher workload (i.e., a lower nurse to patient ratio) has been 

hypothesized to be associated with poor quality of care, because of time pressures that affect the 

ability to follow ideal clinical practices. For physicians, workload has been defined as the 

number of cases of a technical procedure performed by the physician over a certain time period. 

Higher workload has been hypothesized to be associated with better quality of care, because it 

leads to greater experience (and increased skill) in performing complex technical procedures. 

This difference is not surprising, in that the unit of measurement differs between the two groups. 

Nursing workload is typically measured by the number of patients cared for during an 8-hour 

shift, while physician workload is typically measured by the number of technical procedures 

performed per year.

Nurse Staffing

The evidence on the relationship between nurse staffing levels and measures of patient safety 

is provided by 26 studies summarized in Evidence Table 1.42, 43, 46-69 Twenty-two of these studies 

have been published since 1996, and 21 were cross-sectional studies examining the relationship 

between measures of nurse staffing levels and adverse occurrences. Most commonly, these 

studies have examined in-hospital deaths and non-fatal adverse outcomes in the hospitalized 

setting, including various types of nosocomial infections, decubitus ulcers, and falls. In some 

studies, process errors have been measured, including medication errors. One additional study70

was included in this table because it was based on a similar hypothesis (that higher workload 

leads to a greater number of errors). This study examined physician workload in an experimental 

setting, and its outcome measure was charting errors.

An important methodologic issue in this set of studies is the unit of analysis. All of the 

studies examining nursing workload used nurse-to-patient ratio as the method to estimate 
25

workload. None of the studies examined individual nurses, so they all estimated workload by 

compiling staffing and patient occupancy data. For some studies, this was compiled by 

individual nursing units, while others aggregated these data for entire hospitals. One study63

aggregated these data across groups of hospitals. A particular problem of hospital-level 

aggregation is that divergent nursing units are combined, including pediatric units, adult units, 

and intensive care units. Furthermore, these studies often have estimated hospital staffing levels 

by using payroll data reported to governmental agencies. This source of staffing data has been 

found to be consistently inaccurate, although not systematically biased.68, 71

The studies that aggregated data at the unit level were judged to have better quality than 

studies that aggregated data at the hospital level, due to the elimination of data pooling across 

individual units. Studies comparing similar units (such as studies of only intensive care units) 

were also judged to have higher quality. In one study that aggregated data at the unit level,49

intensive care units were found to have consistently higher rates of all adverse events, regardless 

of staffing levels.

The measures of outcome in studies of nurse staffing are recorded at the patient level, by 

recording incidence of such adverse events as decubitus ulcers. From the patient’s perspective, 

nursing workload can be characterized as the amount of staff time devoted to that particular 

patient. With higher nursing workload, a lower amount of time is available for any single 

patient. Thus, nursing workload has typically been reported using such measures as nursing 

hours per patient per day. A lower value of this ratio denotes a higher level of nursing workload 

for an individual staff member. The term “staffing” also has been used to denote this ratio, and 

higher levels of staffing connote higher nurse-to-patient ratios.

Non-fatal adverse events such as decubitus ulcers and patient falls have a plausible direct 

relationship to the availability of nursing staff. A consistent finding across most of the studies 

summarized in Evidence Table 1 is that lower nurse-to-patient staffing ratios were associated 

with higher rates of non-fatal adverse events. This result was found in studies that aggregated 

data at both the nursing unit and hospital level. While most of these studies used data from 

acute care hospitals, three48, 67, 68 used data from statewide or multi-state samples of nursing 

homes. The findings from the nursing home studies are similar to those for acute care hospitals, 

with higher staffing being associated with lower adverse event rates. All three studies of nursing 

homes examined staffing ratios for both licensed nurses (registered nurses, licensed practical 

nurses, and licensed vocational nurses) and unlicensed nurses (nursing aides). Lower staffing 

ratios for both categories of staff were associated with higher adverse event rates.

Patient mortality conceptually is an imperfect measure of problems attributable to nurse 

staffing. While some patients die as a result of injuries related to health care, others die as a 

result of overwhelming disease. All of the studies evaluated for this report that used patient 

mortality as an outcome measure lacked methods for attributing the cause of death to preventable 

or non-preventable causes. Thus, it is not surprising that there was not agreement among the 

studies on whether lower nurse-to-patient ratios are associated with higher patient mortality 

(measured as either in-hospital mortality or death within 30 days of admission). The strongest 

evidence supporting such a mortality relationship examined patients with AIDS.42 This study 

was conducted in 20 hospitals, aggregated data at the nursing unit level, and had good casemix 

controls. However, there have not been studies demonstrating a relationship between nurse 

staffing levels and patient mortality for other diagnoses. Among studies that did not select 

patients by diagnosis, a study examining a single intensive care unit55 and two nationwide studies 

that aggregated data at the hospital level43, 66 also found that lower nurse-to-patient ratios were 
26

associated with higher patient mortality. Other studies examining multiple intensive care units46, 

50 and hospital-level staffing ratios52, 63 did not find such a relationship.

The most common approach to examining staffing levels has been to record licensed nurse 

staffing and unlicensed nurse staffing as separate variables. In an attempt to provide insight into 

how overall staffing affects patient safety, some studies using data from acute care hospitals have 

examined ratios of registered nurses (RNs) to non-licensed nursing personnel. The best evidence 

that this ratio is important comes from a study that aggregated data at the unit level and had good 

casemix controls. That study found that a higher ratio of RNs to unlicensed nurses was 

associated with lower rates of both medication errors and decubiti.49 Another study that 

aggregated data at the hospital level found that higher ratios of RNs to unlicensed nurses were 

associated with lower mortality rates.64 A study that did not include casemix adjustment found 

no association between RN to unlicensed nurse ratio and non-fatal complications.61 Another 

study that found a higher RN to unlicensed nurse ratio to be associated with higher medication 

error rates probably reported a spurious finding, because it compared intensive care units (ICUs) 

to general nursing units.56 Blegen72 also conducted a study that did not focus on nurse staffing 

levels but examined whether nursing units having higher proportions of registered nurses with 

baccalaureate degrees experienced lower complication rates for inpatients, but that association 

was not found. That study did find that units having lower complication rates tended to be 

staffed by nurses with a greater number of years of nursing experience.

The cumulative evidence on nursing workload is sufficient to provide answers to three of the 

key questions, as follows:

1. Does nursing workload affect patient outcomes that are related to patient safety? There is 

sufficient evidence to conclude that higher nursing workload is associated with higher rates 

of non-fatal adverse outcomes in both inpatient and nursing home settings. Increased staffing 

levels of either licensed nurses or unlicensed nurses was associated with lower rates of nonfatal adverse outcomes. The evidence is not consistent in demonstrating that higher nursing 

workload is associated with higher rates of patient mortality.

2. Does nursing workload affect the rate of medical errors? There is sufficient evidence to 

conclude that higher nursing workload is associated with higher incidence of medication 

errors.

3. Does nursing workload affect the rate of recognition of medical errors after they occur? 

There is insufficient evidence to answer this key question.

4. Does nursing workload affect the probability that adverse events will occur following 

detected or undetected medical errors? There is insufficient evidence to answer this key 

question.

5. Does the complexity of the plan of care affect whether nursing workload affects patient 

outcomes that are related to patient safety? There is sufficient evidence to conclude that the 

magnitude of the effect of nursing workload on patient outcomes differs between ICU and 

non-ICU settings.

6. Do nursing working conditions affect measures of service quality in industries other than 

health care? No studies in other industries were identified that examined measures of 

workload comparable to nurse/patient ratios, so research in non-healthcare settings was not 

found to be relevant to this category.
27

Physician Workload

Studies of physician workload have focused on a different hypothesis than that used in 

studies of nursing workload. The studies of physicians have mostly examined physicians who 

perform technical procedures, such as surgeons and cardiologists. The conceptual model of 

these studies is that repeated practice is necessary to maintain high-level technical skills. This 

leads to the hypothesis that physicians who perform fewer procedures over a defined time period 

will experience a higher rate of adverse events. In contrast to the research on nurses, the 

research on physician volume is not based on the premise that higher volume hinders availability 

to meet patient needs. Physicians have been classified as high volume on the basis of caring for 

as few as 3 patients per year with a particular diagnosis,73 and physicians considered to be high 

volume operators often perform fewer than 100 procedures per year.

Many studies have used the hospital as the unit of aggregation and tested the hypothesis that 

hospitals with higher case volumes experienced lower complication rates. Such studies often 

have no data about individual physicians, so there are no data about working conditions as 

applied to individual physicians. Rather, they address the issue of whether certain procedures 

should be centralized in high-volume hospitals. A recent systematic review found substantial 

evidence that hospitals with higher case volumes experience lower complication rates.28 That 

review also compiled evidence from studies that aggregated data on case volume to the level of 

the individual physician and concluded that physicians performing high rates of technical 

procedures experience lower rates of adverse outcomes. However, some studies that controlled 

for both institutional and physician volume failed to find that physician volume had a significant 

effect, suggesting that improved results may be due to institutional rather than physician-specific 

factors.74, 75 Additional evidence that institutional factors have a major effect on adverse event 

rates comes from a national prospective study of outcomes of surgical procedures conducted in 

Department of Veterans Affairs hospitals.76 In that study, there was no relationship between 

volume of surgical cases and 30-day mortality rates. Institutional factors also may be 

responsible for the finding that case fatality rates decreased over time among cardiac surgery 

patients, independent of surgeon volume.77

Most studies of physician volume have selected cases based on the criterion that a particular 

procedure was performed. Relatively few studies have examined case volumes based on 

diagnoses rather than procedures. Studies based on cases defined by receiving a particular 

invasive technical procedure are inherently subject to the bias that the decision to perform a 

procedure is physician-dependent and may be influenced by past experience. Studies based on 

patients who have a particular condition (such as blunt trauma), rather than having received a 

particular procedure, permit examination of case volume independent of decisions about 

procedures. A recent systematic review78 evaluated published studies examining the effects of 

institutional and physician volume on the outcomes of care for patients with cancer. That review 

found that there have been relatively few studies of patients with nonsurgical cancers and that the 

data from those studies are not sufficient to draw conclusions about individual physicians. 

We found four studies that reported data about physician volume for patients identified by a 

particular diagnosis rather than performance of a procedure. These studies are summarized in 

Evidence Table 2.44, 73, 79, 80 We also included one additional study81 that enrolled patients 

identified on the basis of having undergone coronary angioplasty. This study was included 

because it had complete data about all procedures performed at the five participating centers and 

included detailed and angiography data that permitted correcting for the severity of coronary 
28

artery disease. Three of the five studies found that higher physician volume was associated with 

lower patient mortality rates, and these studies had higher methodologic ratings than the two 

studies with negative findings. One study44 also examined the relationship between outcomes 

and physician case volume in relation to other characteristics of individual physicians. Older 

physicians experienced higher mortality rates, but other physician factors (faculty status and 

location of prior training) were not associated with mortality rates. 

The cumulative evidence on physician workload is sufficient to provide answers to two of the 

key questions, as follows:

1. Does physician workload affect patient outcomes that are related to patient safety? There is 

sufficient evidence to conclude that higher physician workload is associated with lower inhospital mortality rates. This finding has been observed both in studies of patients 

undergoing specific technical procedures and in studies of patients hospitalized for medical 

conditions that may or may not require performance of procedures.

2. Does physician workload affect the rate of medical errors? There is insufficient evidence to 

answer this key question. 

3. Does physician workload affect the rate of recognition of medical errors after they occur? 

There is insufficient evidence to answer this key question.

4. Does physician workload affect the probability that adverse events will occur following 

detected or undetected medical errors? There is insufficient evidence to answer this key 

question.

5. Does the complexity of the plan of care affect whether physician workload affects patient 

outcomes that are related to patient safety? The identified studies have been limited to 

patients hospitalized with serious diseases, and there is little variation in complexity of care. 

There is insufficient evidence to conclude that complexity influences the rates of adverse 

events.

6. Do physician working conditions affect measures of service quality in industries other than 

health care? No studies in other industries were identified that examined measures of 

workload comparable to those used to evaluate physician performance, so research in nonhealthcare settings was not found to be relevant to this category.

Professional Qualifications

Professional qualifications as a working condition affecting patient safety have been 

examined among both physicians and nurses. Many of the studies of physicians have compared 

fully trained physicians to trainees or less experienced trainees to more experienced trainees. We 

did not review these studies because it is widely accepted that medical school and residency 

training programs improve physician skills. 

Studies conducted in pharmacies have found that pharmacists and pharmacy technicians have 

similar performance in the rates of medication errors. A single randomized trial compared 

pharmacists to pharmacy technicians as dispensers of prescriptions for ambulatory patients.82 

That study found that there was no significant difference in medication dispensing errors 

between the two worker groups. A non-experimental study comparing pharmacists to specially 

trained pharmacy technicians in the accuracy for identifying unit dose errors also found similar 

error rates between the two groups.

83
29

For fully trained physicians, specialty certification has been examined in multiple studies. 

These studies have been based on the hypothesis that certain specialists provide better medical 

care than similarly trained physicians without specialty certification. The studies have measured 

both errors (such as inadequate tumor resection) and adverse events as outcomes. These studies 

are summarized in Evidence Table 3.84-88 Four of the five studies examined surgical results and 

are consistent with the conclusion that physicians who have had more prior training on certain 

surgical procedures have better results when performing the procedure. The other study88 found 

that patients with acute myocardial infarction had lower in-hospital mortality when cared for by a 

cardiologist.

The cumulative evidence on physician specialty is sufficient to provide answers to one of the 

key questions, as follows:

1. Does physician specialty affect patient outcomes that are related to patient safety? There is 

sufficient evidence to conclude that physicians with specialty training experience lower rates 

of fatal and non-fatal adverse outcomes for certain procedures and medical conditions.

2. Does physician specialty affect the rate of medical errors? There is insufficient evidence to 

answer this key question.

3. Does physician specialty affect the rate of recognition of medical errors after they occur? 

There is insufficient evidence to answer this key question.

4. Does physician specialty affect the probability that adverse events will occur following 

detected or undetected medical errors? There is insufficient evidence to answer this key 

question.

5. Does the complexity of the plan of care affect whether physician specialty affects patient 

outcomes that are related to patient safety? There is insufficient evidence to answer this key 

question.

6. Does employee specialization affect measures of service quality in industries other than 

health care? No studies were identified that examined professional qualifications similar to 

those for assessing physician performance, so research in non-healthcare settings was not 

found to be relevant to this category.

Experience and Educational Qualifications

The experience and educational qualifications of healthcare professionals has been examined 

in studies conducted in a variety of settings involving both physicians and nurses. The factors 

addressed in this category include the perceived quality of prior training, the duration of 

experience, and the efficacy of targeted training on the patient care skills of health professionals. 

There are 10 studies that provide evidence about the relationship of these factors to measures of 

patient safety (Evidence Table 4). Eight of the studies examined physician characteristics,44, 89-95

one study examined nurses,96 and one study examined physicians, nurses and other clinical 

staff.97 

Of the three studies that examined physician experience,44, 89, 93 none demonstrated that 

greater duration of physician experience with surgical procedures was associated with lower 

rates of post-operative complications. These studies also suggest that participation of trainee 

surgeons in surgical procedures that are supervised by senior surgeons is not associated with 

higher complication rates, but both studies examining this question studied only a small number 

of physicians. A study of physicians’ ability to recognize physical findings associated with HIV 
30

infection94 found that general internists and family practitioners who had greater experience 

caring for HIV patients were better able to identify oral leukoplakia but did not differ from the 

comparison physicians in two other physical examination skills. The one study that examined 

the duration of practice of registered nurses96 found a lower rate of medication errors on patient 

care units having more experienced nurses. That study also found that care by baccalaureateprepared registered nurses was not associated with lower rates of medication errors or patient 

falls, when compared to associate degree registered nurses. 

The available evidence does not permit concluding that the perceived quality of prior 

educational preparation of healthcare professionals affects any aspect of patient safety. For 

length of experience and targeted training, the following conclusions can be drawn:

1. Does professional experience affect patient outcomes that are related to patient safety? 

There is evidence from one study of registered nurses that longer duration of experience is 

associated with lower rates of patient falls. The evidence from this single study is not 

sufficient to conclude that professional experience affects rates of adverse outcomes.

2. Does professional experience affect the rate of medical errors? There is evidence from one 

study of registered nurses that longer clinical experience is associated with lower rates of 

medication errors. There is not sufficient evidence to conclude that professional experience 

affects rates of medical errors. 

3. Does professional experience affect the rate of recognition of medical errors after they 

occur? There is no evidence on this question.

4. Does professional experience affect the probability that adverse events will occur following 

detected or undetected medical errors? There is no evidence on this question.

5. Does the complexity of the plan of care influence whether professional experience affects 

patient outcomes that are related to patient safety? Two studies of the performance of 

surgeons found that greater experience was associated with worse outcomes, which is the 

opposite to the findings of the one study of inpatient nurses. These three studies suggest that 

care complexity influences the effects of professional experience on patient safety, but the 

evidence is not sufficient to draw this conclusion.

6. Does professional experience affect measures of service quality in industries other than 

health care? No evidence was identified on this question.

Temporal Factors

Work schedules and lengths of workshifts have received considerable attention in health 

care, but much of the evidence on these factors comes from research conducted outside of health 

care. There has been a long interest in the effects of fatigue upon airplane pilots, and the federal 

government established mandatory restrictions on pilots’ work schedules in 1964. More 

recently, it has been estimated that 58% of long-haul truck accidents are due to fatigue.98 Work 

schedules, including assignments to evening, night, or rotating shifts, have been recognized as a 

potential contributor to fatigue-related accidents, and research in various industries has provided 

useful information.99, 100

A major focus of fatigue-related problems in health care has been the work schedules of 

trainee physicians.98 A systematic review of the effects of fatigue among resident physicians 

found that there is limited evidence to conclude that fatigue causes higher rates of errors on 

repetitive tasks and tasks requiring prolonged vigilance.101 This evidence has contributed to 
31

regulatory efforts to limit work hours of trainee physicians. Although there have been case 

reports of adverse patient outcomes attributed to resident fatigue,98 we found no controlled 

studies of the effect of physician work schedules on measures of patient safety. 

Temporal factors related to workforce staffing have been examined in nine studies that 

provide evidence about aspects of patient safety (Evidence Table 5).102-110 These studies have 

been conducted in both healthcare and non-healthcare settings and have examined a variety of 

questions including the effects of length of work shift, variation in outcomes during days of the 

week, comparison of day and night shifts, and effects of season of the year. The strongest 

evidence on the effects of shift length comes from a study conducted among workers in natural 

gas utility plants.102 This was a prospective study that examined changes in experimental 

measures of cognitive function and motor skills before and after workers’ changes in shift 

assignments. That study demonstrated that performance was poorer with 12-hour shifts than 8-

hour shifts. A second study examining this question was conducted among German intensive 

care unit physicians.104 That study found no difference in complication rates of patients after the 

physician coverage changed from two 12-hour shifts per day to three 8-hour shifts per day. 

There is limited evidence that work schedules requiring changing shifts affects error rates. A 

survey of registered nurses and licensed vocational nurses examined estimated rates of 

medication errors for nurses who worked fixed or rotating shifts.106 Nurses who rotated among 

shifts reported the highest rate of medication errors. Night shift nurses reported more near miss 

medication errors than day-shift nurses but no more actual medication errors. A study conducted 

among factory workers found that rotating shift workers had higher rates of reported workplace 

injuries.107 A study conducted in a nuclear power plant108 found poorer measures of 

performance among the night-shift workers. However that study was flawed because of a small 

sample size and limited generalizibility to other settings.

Other studies have examined whether period of the week or year may affect measures of 

patient safety. A retrospective cohort study conducted in Canada compared patients admitted on 

weekend days to patients admitted during weekdays.103 That study found that patients who were 

admitted on weekends for a variety of principal diagnoses experienced higher in-hospital 

mortality. However for many other diagnoses, the mortality rates were not different. This study 

has suggestive findings, but it is not adequate to draw conclusions about weekly variation 

because of a poorly developed conceptual model regarding the classification of diagnoses. It 

also is not possible to determine from this study what actual aspects of patient care may be 

responsible for the reported differences.

Booker and Roseman105 examined seasonal variation in medication error rates in a 140-bed 

acute care hospital in Alaska, where the length of darkness ranges from 18.6 hours per day in 

December to 4.5 hours per day in June. These investigators used existing independently collected 

hospital data from 1985 through 1989 on medication errors and nine potentially confounding 

variables, including nursing job vacancies, new nursing hires, amount of overtime, number of 

temporary worker shifts, leave taken by nurses, number of admissions, number of discharges, 

total monthly inpatient days, and monthly patient deaths. The relationship between these 

variables and the monthly average time of darkness per day was examined using Poisson 

regression analysis. After controlling for measures of workload, seasonal variation in medication 

errors persisted. Over the 5 years of data they examined, medication error rates were greatest 

one to two months after the month of greatest darkness. 

The following conclusions can be drawn regarding the influence of temporal factors: 
32

1. Do temporal factors affect patient outcomes that are related to patient safety? There is 

evidence from one study that the shift length of physicians is not associated with differences 

in patient outcomes. The evidence from this one study is insufficient to answer this question. 

2. Do temporal factors affect the rate of medical errors? There is evidence from one study of 

non-healthcare workers and a survey study of nurses that performance errors are higher 

among workers having rotating shift assignments. The studies of 8-hour vs. 12-hour shifts 

have had conflicting results, but studies conducted among resident physicians have found 

adverse effects of longer shifts on technical performance. A single Alaskan study found a 

higher rate of medication errors during the winter season. This evidence is suggestive that 

rotating shift assignments or longer work shifts are associated with higher rates of medical 

errors, but it is not sufficient to reach this conclusion.

3. Do temporal factors affect the rate of recognition of medical errors after they occur? There 

is evidence from one study that near-miss errors are more frequently recognized among 

night-shift nurses, but this evidence is insufficient to answer this question.

4. Do temporal factors affect the probability that adverse events will occur following detected

 or undetected medical errors? There is no evidence on this question.

5. Does the complexity of the plan of care influence whether temporal factors affect patient

 outcomes that are related to patient safety? There is no evidence on this question.

6. Do temporal factors affect measures of service quality in industries other than health care?

 Five of the studies that provided valid evidence on temporal factors were performed in non-

 healthcare settings. 

Workflow Design

Workflow design includes healthcare tasks and workplace design issues relevant to 

accomplishing the tasks. The key words used in the literature search represent potential domains 

or conditions in task design (complexity, redundancy, distraction, hand-offs, monotony, and role 

definition) and in workplace design (shared work space, ergonomics, forced posture, technology, 

information technology, and equipment). We focused our review in areas of workflow design 

that had observational or experimental data and that were not already reviewed in the recently 

published evidence report, Making Health Care Safer: A Critical Analysis of Patient Safety 

Practices.

14 Several important areas of workflow design are “patient safety practices” and were 

comprehensively reviewed in that report. Aspects of workflow design reviewed in that evidence 

report and excluded from this review include information transfer, role definition of pharmacists 

and intensivists, medical devices, computerized physician order entry with clinical decision 

support, adverse drug events detection and alerts, unit-dose drug distribution systems, automated 

medication dispensing devices, bar coding, and clinical decision support systems. Because we 

did not identify data supporting an association between shared workspace or forced posture and 

medical errors or patient safety, those aspects of workflow design are not covered in the present 

report.

Task Complexity

Reducing task complexity has been identified as an important strategy to reduce medical 

errors.4 However, we did not identify direct observational or experimental data in health care or 
33

other industries supporting the association between task complexity and medical errors or patient 

safety. General information is available in the psychology and healthcare literatures to support a 

conceptual framework to explain why task complexity might increase medical errors. The 

framework is described in the "Transitions or Hand-offs During Care" section below. 

Monotony and Redundancy

We did not identify observational or experimental data supporting a connection between 

monotony or redundancy and medical errors or patient safety. This working condition is 

discussed in the aviation and anesthesia literature, where tasks have been described as “hours of 

boredom interspersed by moments of terror.”111 Anesthesia tasks include high workload during 

times of induction and emergence from the anesthetic state. The maintenance period between 

these two tasks is a lower workload state requiring vigilance and monitoring skills. Practicing 

anesthesiologists describe adding other tasks during these periods to avoid boredom and lack of 

vigilance. 111 Other domains of health care that feature task monotony and redundancy include

telemetry monitoring, dialysis, cytology, and transfusion medicine.

Interruptions and Distractions

The management of mental workload or “attentional dynamics” is an important cognitive 

factor that affects the performance of workers in complex settings. An interruption is generally 

defined as an external factor causing the cessation of productive activity, before a current task is 

complete. A distraction is defined as a stimulus from an external source that results in an 

observable response but not the cessation of activity.112 A distraction is alternatively defined as 

preoccupation by one task to the exclusion of others.113 Interruptions and distractions appear to 

affect “prospective memory,” or the ability to remember to do something that must be 

deferred.114 We depend upon prospective memory for tasks for which we do not receive a cue 

to remember. This type of memory is associated with the context in which it was formed, and 

changing the context, or in this case getting distracted, impedes recalling the memory. It may 

take 10-40 seconds to forget to do something upondistraction unless it is strongly connected to a 

cue.113 Reason3 described the phenomenon as belonging to a particular class of errors that he 

called “omissions following interruptions.” Temporary losses of memory, such as when you 

enter a room and forget why you went there, are known as “activation errors” and are thought to 

be due frequently to interruptions.115

Cognitive research supports that a second task processed concurrently with another task can 

lead to “interference” or a bottleneck in human information processing.116, 117 Interference 

occurs when the resources required of each task overlap. Most people can do two things at once

successfully only in very specific task type combinations. Regularly practiced or “automatic” 

tasks can successfully be paired, such as taking a patient history and formulating a differential 

diagnosis. “Consciously processed” tasks require more deliberate and sequential thought. This 

type of information processing is used for more novel or difficult tasks, and these tasks usually 

cannot be paired without risking error.114

Research on interruptions and distractions has been conducted in non-healthcare settings. 

Lapses of attention have been found to contribute to many aviation accidents.113 In an analysis 

of 37 major aviation accidents from 1978-1990, interruptions, distractions, or the preoccupation 

with one task to the exclusion of another have been determined to play a role in nearly one-half 
34

of flight crew incidents.114 Often in aviation, the crew becomes pre-occupied with one task to 

the exclusion of another important one. As an example, Dornheim114 describes a 1972 crash of 

an Eastern Airlines L-1011 after the crew became preoccupied with a landing gear problem and 

failed to notice that the autopilot had become disconnected. In an analysis of 107 reports to 

NASA’s Aviation Safety Reporting System involving competing tasks, radio communication 

among the crew was the largest cause of distraction (68 of 107 incidents). Most discussions 

were relevant to the flight but could have been deferred. The main task that was neglected was 

monitoring the status of the aircraft or the pilot’s flying (69 percent of cases).114

A study in the nuclear power plant industry revealed that in more than 15 percent of plant 

shutdowns, operators had been distracted during execution of the current task.118 In a study 

among commercial telecommunications workers, interruptions to customers’ calls resulted in an 

increase in the processing time required for the current task, a significant effect of temporal 

strain on performance, and an increase in the error rate at the beginning of processing the second 

task.119

The commercial airline industry has developed a multi-faceted approach to deal with 

interruptions, distractions, or the preoccupation with one task to the exclusion of another. The 

industry has developed a concept known as the “sterile cockpit.” Take-off and landing were 

determined to be critical safety sensitive times. Thus, an administrative procedure forbids the 

flight crew from talking about anything unrelated to flying when the plane is below 10,000 

feet.120 Surveillance for violations is possible because everything that is said in the cockpit is 

recorded. The most successful interventions also may not rely upon human memory or behavior. 

Many approaches to human error among aviation crews address cognitive frailty through 

improved design of the technology-human interface, also called human factors engineering. An 

example is the change in equipment that led to the development of a noticeable electronic 

indicator of the disconnection of the autopilot function.114 These practices have been adopted by 

the aviation industry primarily because of their face validity. Although it is difficult to connect 

evidence to a single intervention, the airline industry has an impressive safety record, with only 

about 125 deaths/year from 20x107 passenger boardings.121

Multiple studies in a variety of settings have demonstrated that health professionals 

experience frequent interruptions and distractions in the course of patient care. Surveys of 

residents in pediatrics122 and internal medicine123-125 as well as time-motion observational studies 

of interns124 have documented how frequently pagers interrupt physicians in training in the 

course of patient care. Studies of emergency physicians in multiple hospitals have demonstrated 

high rates of interruptions and competing attentional demands of simultaneous patient care.126-128 

Similarly findings of frequent interruptions and simultaneous demands competing for attention 

have been demonstrated for nurses in the United Kingdom.129 Other studies suggest that the 

frequency of interruptions can be reduced.130

Few well-designed studies have analyzed the extent to which interruptions and distractions 

contribute to medical errors and adverse patient outcomes. We identified six observational 

studies, one with a concurrent control group and greatest design suitability (comparison of 

interrupted and uninterrupted prescription dispenses),112 one with a concurrent control group and 

moderate design suitability,134 and the other four without controls and of the least or moderate 

design suitability (Evidence Table 6).121, 131-133 Peterson131 conducted a cross-sectional survey of 

Tasmanian pharmacists addressing their perceived temporal trend in medication errors and the 

factors that contribute to or minimize the risk of medication dispensing errors. Flynn112

examined medication dispensing using videotaped task analysis and a measure of distractibility, 
35

the group embedded figures test (GEFT), to determine the effect of distractibility and 

interruptions and distractions on medication dispensing errors. Ely133 performed a crosssectional evaluation of a focus group and in-depth interviews with family physicians in Eastern 

Iowa to explore causes of their most memorable errors. Gladstone132 carried out a retrospective 

analysis of drug error incident reports and interviews with nurses and managers. Cooper121

performed a retrospective critical incident analysis of anesthesia errors and equipment failures 

using structured interviews and a voluntary reporting form. The study findings are outlined in 

Evidence Table 6. 

A quasi-experimental study134 conducted at a mid-sized acute care hospital in a city in 

Southeastern Texas me asured the effect of two interventions to decrease nurses’ distractions 

during medication administration. Twenty-four medication administration cycles were observed 

among LPNs and RNs. A control group used customary medication administration procedures. 

One intervention group used a protocol that specified no conversation during medication 

administration, teamwork, and a checklist. The second intervention group used the Medsafe 

intervention, which included the same procedures as the first intervention and added wearing a 

special vest to indicate to others that distractions were not acceptable during medication 

administration. The intervention groups had significantly reduced distractions as compared to 

the control group.

There is sufficient evidence that interruptions and distractions play a role in medication 

dispensing errors.112 The evidence of effectiveness of interventions to reduce medication 

dispensing errors through the reduction of interruptions and distractions is insufficient.134 The 

evidence of the association between interruptions and distractions and errors in other areas of 

medicine is insufficient. This is based upon the limited number of observational studies, three of 

which rely upon the distant memory of errors.121, 132, 133

Interventions to reduce interruptions could be quite simple and inexpensive to implement, as 

exemplified by those described by Pape.134 Others have suggested the use of message boards or 

voice or electronic mail messages for non-urgent messaging, all of which are already commonly 

available in many healthcare environments.122

Transitions or “Hand-Offs” During Care

Individuals with chronic illness often require care from different practitioners in multiple 

settings. For example, in a given month, the same person with chronic illness may receive care 

from his or her primary care physician or a specialist in the ambulatory setting, a hospitalist 

physician and nursing team during an inpatient admission, a different physician and nursing team 

during a brief stay in a skilled nursing facility, and a visiting nurse in the home. Yet during these 

times when these patients are most vulnerable and their informal caregivers are often 

overwhelmed, systems of care may fail them by not ensuring that: (1) the critical elements of 

their care plan developed in one setting are transferred to the next; and (2) the essential steps that 

need to take place before and after transfer are executed. In particular, hospital discharge has 

been identified as a vulnerable time for medical errors and adverse events, and numerous 

programs have attempted to bridge the gap between the inpatient and outpatient setting. 

Transitions between care settings are very common in older adults. In a 2-year study of 

patients aged 65 or older, 18 percent had at least one post-acute or long-term care transition.135 

Twelve percent of transitions had a followup emergency room visit, an “avoidable 

hospitalization,” or both. A transition was defined as a change in location lasting a day or more 
36

with the place of origin or destination a rehabilitation facility, a nursing home, formal home care, 

or other formal care setting. The unplanned readmission rate for elderly patients in the first 

month after discharge is approximately 6 percent, and hospital readmissions of Medicare 

recipients represent at least one quarter of all admissions.136, 137 Readmission rates of elderly 

patients of 25-40 percent within 12 months have been observed.138

The risk of errors during transitions from the inpatient to outpatient setting may have 

increased as the average length of hospital stay for older adults has continued to decline. This 

trend began with the implementation of Medicare’s Prospective Payment System in 1983, and 

the number of elders discharged with unresolved health problems has increased.139, 140 If the 

workflow design does not adequately provide the tools for healthcare workers to exchange 

information and responsibility successfully, early discharge could be expected to result in higher 

rates of medical errors and/or adverse events. 

Considering how common hospital discharge is we found relatively few studies addressing 

the rates of errors and adverse events at hospital discharge. More information was available on 

process gaps. In a secondary analysis of results from a hospital discharge program, barriers to 

care that had to be addressed during hospital discharge included multiple health providers, 

multiple settings, and multiple insurers.

141 In a survey of 70 randomly selected patients seen in 

the general medicine clinic within 2 months of discharge from the inpatient medicine service at 

the Mount Sinai Medical Center in New York,142, 52 percent had at least one medication error, 

13 percent had at least one test followup error, 7 percent had at least one error in “work-up” or 

diagnostic evaluation, and 59 percent had an error in one or more of the three categories. The 

average number of discharge medications among patients with medication errors was 

significantly higher than those without medication errors (7.2 vs. 5.1, p=0.001). In a similar 

study, Diem143 found 10 percent medication and 2.8 percent test scheduling errors at a resident 

discharge clinic 10 days after hospital discharge.

Discontinuities and inconsistencies between inpatient and outpatient medications, errors in 

medication labeling, poor patient understanding, and lack of medication adherence after 

discharge are important sources of errors, especially in the elderly.144-147 A summary of this area 

and a review of inpatient to outpatient pharmacy communication interventions can be found in 

Making Health Care Safer: A Critical Analysis of Patient Safety Practices.

14

 Murff148 noted that 

in elderly patients, hospital providers changed 53 percent of the drugs prescribed in the 

outpatient setting, and 32 percent of medications prescribed at discharge were not being taken 

2 days after discharge. In a Scottish study of elderly patients given a 5-day supply of medication 

at hospital discharge and visited 5 days after discharge, 27 percent had not received continuing 

prescriptions for their medications, and among new prescriptions issued, 19 percent were 

inaccurately labeled.145

Gaps in physician-patient communication at discharge appear to be common. In two studies 

from the US and Canada, 20 percent of patients discharged from the hospital reported that they 

were not told about important medication side effects nor when they could resume normal 

activities.149, 150 Calkins et al.151 surveyed 99 patients who had been recently discharged from an 

academic medical center in Boston and their attending physicians. Physicians reported that 89 

percent of the patients understood the potential side effects of their medications, but only 59 

percent of patients reported understanding (p<.001). Physicians reported that 95 percent of 

patients understood when to resume normal activities, but only 58 percent of patients reported 

that they understood (p<.001).
37

The gaps in processes surrounding hospital discharge may be important to patient outcomes. 

Brook et al.152 evaluated through chart review and patient interview 403 consecutively 

discharged patients from the medical service of a Baltimore teaching hospital and found that 30 

percent received follow-up care rated as inadequate. There was frequent lack of communication 

of hospital records (34 percent of 124 with private physicians), and 25 (6 percent) were not given 

follow-up care. In 39 patients (10 percent), a poorer outcome was judged to be due to inadequate 

care. Inadequate process was associated with poorer outcome (p<0.01).

Patients likely contribute substantially to errors during transition. As trends have moved 

toward shorter hospital stays, increasingly complex medication regimens, and community-based 

long-term care, the opportunity for important miscommunications and gaps in expected followup 

have increased. By default, facilitating successful care transitions becomes the responsibility of 

patients and their caregivers, who may not possess the necessary skills or confidence for this

role. A qualitative study of the needs of the elderly in the first week after hospital discharge 

revealed needs in four major areas: continuing care needs, the need for an available and able 

caregiver, caregiver uncertainty, and the need for information.153

A number of other process gaps have been identified around discharge planning, exchange of 

information, and transfer of responsibility. These include patients being told at the last moment 

that they were being discharged, with subsequent insufficient information about the requirements 

for care; and inadequacy of the discharge plan, patient and caregiver understanding of the plan, 

and essential resources, e.g., money or transportation.

139, 154

Many complex factors have been associated with gaps in planning the transition from the 

inpatient to the outpatient setting. Multiple factors were significantly associated with less 

adequate social worker ratings of discharge plans in 286 older patients with Medicare: financial 

impediments, patient confusion, lack of family availability, difficulties working with families, 

Medicare/ Medicaid guidelines, and team disagreement regarding the patient’s psychosocial 

situation.155 In a random sample of 133 elderly patients with unplanned readmission to a UK 

hospital, factors associated with readmission included relapse of the original condition, 

development of a new problem, caregiver problems, complications of the initial illness, need for 

terminal care, problems with medication, and problems with service.136 In that study, service 

problems significantly associated with readmission included “too early” discharge, in the general 

practitioner’s opinion; no advice given at discharge; and no discharge notice given to the general 

practitioner. Another study of factors associated with hospital readmission of elderly patients 

found unavoidable medical deterioration, inadequate medical management, patient noncompliance, social problems, and inadequate rehabilitation as significant.156

Is discharge planning effective in reducing errors? A recent Cochrane review (last updated in 

August, 2000) reviewed 8 controlled trials of discharge planning involving 4837 patients.157 Four 

trials recruited patients with a particular medical condition and four recruited patients with a mix 

of medical and surgical conditions. There was a small reduction in hospital length of stay for 

elderly medical patients allocated to discharge planning (weighted mean difference -1.01, 95% 

CI -2.06 to 0.05), but overall the results of the trials were mixed.

We extended the findings of the Cochrane review by evaluating interventions that did not 

focus on a particular medical condition and had an outpatient component. We identified 10 

randomized controlled trials and four observational studies of hospital discharge programs for 

the aged that met our criteria (i.e., were not single disease focused and had an outpatient 

component to the intervention). The trials are summarized in Evidence Table 7. All designs 

were rated to have the greatest suitability. The average patient age in all studies was >65 except 
38

for Smith,158 where the average age was 52. The studies were conducted at Veterans Affairs 

hospitals,143, 159 a community hospital in the UK,160, 161 US, 162 or Denmark,163 and academic 

medical centers.141, 158, 164, 165

Lipton162 utilized pharmacists to prevent medication errors. The other interventions were 

delivered by nurses,141, 158, 164 a nurse practitioner and geriatrics team,165 nurses and health care 

assistants,161 a nurse and a primary care physician,159, 163 medical residents,143 or a care 

attendant,160 and provided support for multiple aspects of care, including medication. The 

intervention intensity was variable as outlined in the table, with dedicated staff in six studies141, 

160-162, 164, 165 and staff given additional responsibilities in four studies.143, 158, 159, 163 Length of 

outpatient intervention followup was variable, with 1 week,158, 159 10 days,143 2 weeks,160, 163, 164 4 

weeks,141 6 weeks,161 3 months162 or unclear length of followup.165 

Most studies did not provide detailed information about the types of interventions provided 

by staff. Naylor139 did describe six strategies that were utilized by the advanced practice nurses: 

comparing patient and caregiver data, anticipating outcomes, individualizing care, empowering 

patients and caregivers, crossing barriers, and creating solutions.139 Two-thirds of the nurses’ 

interventions were in the surveillance area, which included initial assessment and ongoing 

monitoring. Teaching, guiding, and counseling comprised 20 percent of the interventions, case 

management 14 percent, and treatments and procedures 1 percent. Clinical outcomes included 

mortality,143, 160, 163, 165 non-elective hospital admission,141, 143, 158-161, 163-165 time to first hospital 

readmission,141, 164 length of hospital stay,164 nursing home admission,161, 163, 165 morale,160

quality of life159, 165 and physical independence.160 Other outcomes included medication errors, 

162 emergency room utilization,143 and satisfaction.159, 165

All ten studies of hospital discharge programs for the aged are rated good for their quality of 

execution, but the evidence for their effectiveness is mixed. This may be in part due to the large 

variation in intervention design and intensity. Six studies utilized dedicated staff, 141, 160, 161, 162, 

164, 165 and four studies utilized nondedicated staff.143, 158, 159, 163 Of the trials with dedicated staff 

for the intervention five of six had a positive impact on readmission rates or a related outcome. 

Of the trials without dedicated staff (i.e., additional tasks for existing staff), one of the four had a 

positive impact and one of the four had an increase in readmissions in the intervention group. 

We also found three observational studies evaluating transitional programs at hospital 

discharge in older adults. In a project that employed a liaison nurse before and after hospital 

discharge in the Dutch Zaandam region, a pre- and post-questionnaire to patients suggested that 

the quality of discharge planning was improved.154 Significant results were achieved in the areas 

of need for aftercare discussed with patients, home health staff being informed, and patients 

taught about proper handling of surgical dressings. In a survey of registered nurses, the absence 

of medication discharge planning for elderly patients with congestive heart failure was 

significantly associated with hospital readmission (p<0.05).166

Patients discharged from a UK teaching hospital who received an informational booklet 

about their care at discharge were more likely to know the medications they were taking and the 

correct dose and frequency than patients who did not receive the booklet.167 Sixty of 165 elderly 

patients consecutively discharged from the hospital that received pharmacist counseling made 

less than one-third of the medication errors made by the uncounseled group.168

This body of evidence is sufficient to conclude that hospital discharge programs employing a 

dedicated staff and having an outpatient component reduce readmission rates and hospital days. 

However, the evidence is not sufficient to conclude that these programs reduce medical errors 

and associated adverse events, because these studies did not report these outcomes. While it is 
39

possible that higher readmission rates are associated with worse patient outcomes, the small 

amount of evidence on this point is insufficient to permit any conclusion. In the one study 

reporting an increase in readmissions159, patients in the intervention group had higher satisfaction 

and no decrement in quality of life relative to controls. Conversely, an intervention that reduced 

readmission rates also had no effect on functional status.141

Weinberger169 evaluated the cost-effectiveness of the Smith158 intervention described above. 

High-risk patients in the intervention group had significantly higher outpatient costs 

($131/month vs. $107/month; p=0.02) but lower inpatient costs ($535/month vs. $800/month; 

p=0.02) than the control group. 

Naylor164 found that 6 weeks after hospital discharge the mean charge for the intervention 

group was 63 percent less than the mean charge for the control group. In the 1999 Naylor 

study,141 at 24 weeks after discharge, total Medicare reimbursements for health services were 

$1.2 million in the control group vs. $.6 million in the intervention group (p<.001).

Other transitions in care may contribute to medical errors and adverse events. We found one 

study of medication dispensing for a random sample of over 50,000 Medicare registrants in 

Quebec in 1990.170 This study concluded that the greater the number of prescribing physicians, 

the greater the risk was that the patient received a potentially inappropriate drug combination. 

Computer Interface Design

Clinical decision support systems have been shown to be effective in reducing nosocomial 

infections,171-173 improving dosing of nephrotoxic medications,171-173 and accelerating recognition 

of serious laboratory abnormalities.171-173 Computerized physician order entry has been shown to 

reduce the incidence of adverse drug events.13 Alerts, reminders, and other decision support 

features have been demonstrated to reduce errors in prescribing,174, 175 improve preventive and 

chronic disease care, and improve physician and patient satisfaction in controlled settings or 

inpatient environments.172, 174-178 A recent review concluded that vigorous application of alerts 

and reminders could substantially reduce mortality for multiple conditions.175

Caution is appropriate, however, before widespread deployment of information systems 

meant to improve the reporting and reduce the occurrence of medical errors.14 Lessons from 

other domains, including aviation and military applications, suggest that information systems 

may sometimes worsen the situation. Problems include such phenomena as “automation 

surprise,” when computer systems designed for typical conditions behave unpredictably under 

conditions of stress or crisis; “automation complacency,” where situation awareness is reduced 

when pilots are removed from the information loop by automation; and “mode confusion,” where 

it is not clear to the human operators which mode of operation the automated system has 

assumed. Extensive research in the military, aviation, and nuclear power industries documents 

such dysfunctional interaction between information systems and the individuals they are meant 

to assist179 and investigations of aviation accidents confirms the difficulty of designing software 

that helps rather than hinders expert decision makers in urgent, complex, uncertain conditions.180

Ongoing research in human performance with complex systems in high reliability domains 

emphasizes how information systems must function as team players that interact effectively with 

other members of a team to collaboratively manage complex situations.181 This collaboration 

requires understanding not only the traditional one-to-one human-computer interaction of 

traditional human factors studies, but also the complex situation that arises when the technology 

is added to existing teams of humans.
40

Two studies have examined computer interface design in health care, and neither evaluated 

medical errors nor patient safety. One study compared nurses’ response time, errors, and 

satisfaction comparing text-based computer interface to prototype graphical interface. The nurses 

had significantly faster response times, fewer user errors and were more satisfied with the 

graphical interface.182 The second paper is a case study of the implementation of a “usercentered” computerized command-and-control system in an ambulance service that concluded 

that there was an improvement in time to ambulance-on-the scene and lower subjective anxiety 

and systolic blood pressure in the workers after system implementation.183

Summary of Key Questions for Workflow Design

1. Do workflow design working conditions affect patient outcomes that are related to patient 

safety? The evidence for the association between workflow design factors and patient safety 

has focused on the incidence of medical errors and such adverse events as re-hospitalization 

but not adverse patient outcomes. There is insufficient evidence to conclude that workflow 

design factors affect the incidence of adverse outcomes.

2. Do workflow design working conditions affect the rate of medical errors? There is sufficient 

evidence to conclude that interruptions and distractions increase the incidence of medication 

dispensing errors. The strength of the body of evidence for the association between hospital 

discharge factors and medical errors is also sufficient. The evidence that pharmacist 

discharge programs can reduce medication errors in the elderly is sufficient. The other areas 

reviewed had insufficient evidence.

3. Do workflow design working conditions affect the rate of recognition of medical errors after 

they occur? There is insufficient evidence to support this in all areas reviewed. 

4. Do workflow design working conditions affect the probability that adverse events will occur 

following detected or undetected medical errors? There is insufficient evidence to support 

this in all areas reviewed. 

5. Does the complexity of the plan of care affect whether workflow design working conditions 

affect patient outcomes that are related to patient safety? There is insufficient evidence to 

support this in all areas reviewed.

6. Do workflow design working conditions affect measures of service quality in industries other 

than health care? There is insufficient evidence to support this in all areas reviewed.

Personal/Social Working Conditions

This section of the report is concerned with the evidence for a relationship between the 

personal, professional, or social aspects of the healthcare work environment and the safety of 

patient care. The underlying assumption is that the ability of health professionals to provide safe 

patient care will be influenced by personal factors such as stress, burnout, and job dissatisfaction; 

social factors such as collectivism or discord within a work group; or professionalism in the form 

of the ethics and values that are cultivated within a professional discipline such as nursing, 

pharmacy, or medicine. Studies focusing on the effects of these conditions at the individual or 

work group level are included in this section, while studies focusing on the broader organization 

and its organizational structure or culture are included in the subsequent section on 

organizational factors. 
41

This report is restricted to direct evidence regarding the impact of these working conditions 

on patient safety or medical error. It is beyond the scope of this report to review the vast 

literature that exists on the effects of these working conditions on other variables, including their 

physiologic, metabolic, and psychological effects in individual workers or their organizational 

effects such as absenteeism or job turnover. By way of background, several of the relevant 

constructs are briefly described below.

Stress is a construct that refers to one’s response to an imbalance between the expectations or 

demands placed on individuals and the resources or capacities available to meet them. Stress has 

been defined in the research literature as a stimulus variable, an intervening variable, and a 

response variable.184 In an extensive review of stress and its physiologic, metabolic, and 

psychologic sequelae, Levine185 notes that a) stress-related decrements in performance have 

been replicated across a wide range of stressors and populations in laboratory tasks; b) 

interventions that increase control and predictability are effective in reducing these effects; c) 

evidence for these performance effects is largely based on laboratory studies, and evidence in 

naturalistic settings is limited; and d) a pervasive finding in the literature is the curvilinear 

(“inverted U”) relationship, whereby arousal is associated with improvements in performance, 

while stress is associated with performance decrements. Jones, however, emphasizes the 

multidimensional nature of the stress-performance relationship, concluding that stress effects are 

situation-specific, individual-specific, and task-specific.186

Burnout has been described as a state characterized by emotional exhaustion, 

depersonalization, and reduced personal accomplishment, especially prevalent in human-service 

related occupations, with consequences on job satisfaction and performance that have been 

evaluated in a wide range of occupations and professions.187 Burnout may be distinguished from 

stress in that burnout refers to a longer lasting, more stable condition while stress is generally, 

though not always, more transitory. Burnout is distinguished from depression in that burnout 

involves a person’s relationship to their work, while depression has pervasive effects on multiple 

aspects of an individual’s life. 

The potential relationship between job satisfaction and job performance has been a subject of 

intense investigation by industrial psychologists for many decades, but the research remains 

inconclusive. In a critical review of this literature, Judge et al. examine seven potential models 

of this relationship and found the literature to be inconsistent, in part due to the piecemeal nature 

of the research, with many models being proposed but no systematic and coherent approach to 

thoroughly testing them.188 Based on a meta-analysis of 312 studies, these authors conclude that 

a relationship does exist and propose a multidimensional model as a basis for future research. 

Social dimensions of the work environment include collectivism, the belief of a group 

member that one’s individual welfare is closely tied to that of the group.189 According to this 

construct, individual behavior and overall group performance are potentially affected by the 

degree of collectivism in a work group. A related concept, work group cohesion, has been

identified as an important determinant of job satisfaction.190 Social support has been identified as 

one of the factors that can mitigate stress-related effects on performance.185

Professionalism may also impact the ability to provide safe patient care. Health professionals 

include themselves among the learned professions, espousing values such as excellence, 

altruism, and personal accountability.191 Teaching these values is incorporated into their training 

and symbolized by recitations of the discipline’s oath at graduation or other ceremonies. 

Maintaining these values is an ongoing activity within the profession, as evidenced by 
42

publications that respond to perceived threats to these values by redefining professionalism 

and/or calling for its renewal.192, 193

To impact patient safety, working conditions must affect health professionals, this effect on 

health professionals must interfere with or alter their clinical work practices, and the altered 

work practices must result in harm to patients. As an example, working conditions might 

produce stress or burnout among clinicians such as physicians, nurses, or pharmacists. 

Clinicians with stress or burnout may thereby be more likely to make medication errors. 

Medication errors, in turn, may result in harm to patients. Steps in this theoretical chain of 

causation are independent, and every step in the chain must be present for an impact on patient 

safety to occur. This allows for the possibility that a working condition can affect health 

professionals without affecting clinical practices, or that clinical practices may be affected 

without necessarily resulting in harm to patients.

Eleven studies provide evidence pertinent to the effects of personal/social working conditions 

and are summarized in Evidence Tables 8 and 9. Five studies reported on the impact of stress on 

patient safety194, 195 (one single publication195 reported results of four distinct, original studies). 

Four studies were concerned with the impact of dissatisfaction and burnout.196-199 Two reported 

on the effects of social aspects of the work environment.189, 200 These studies are grouped 

together by topic and discussed individually. 

Stress in the Hospital Work Environment

Two publications reported five studies related to stress in the hospital work environment.194, 

195 Three of these were cross-sectional studies examining the association of stress levels with 

rates of malpractice claims (Evidence Table 8, top frame). The last two evaluated stress 

reduction interventions (Evidence Table 8, lower frame).

 Dugan et al.194 examined the relationship between stress among hospital nurses, burnout 

indicators, nursing injuries, and patient incidents in a moderate sized hospital in the Midwest 

United States. Stress was measured using reported stress symptoms and a single item measure of 

perceived stress, but response rates to the mailed survey were low. Prospectively collected 

hospital department data were used for burnout indicators (unit-level sick leave, other absences, 

and turnover), nursing injuries (nursing needle-stick and back injuries), and patient incidents 

(medication errors, IV errors, and patient falls). Data were aggregated for comparison at the 

level of hospital units, which included a broad spectrum of hospital nursing care. Correlation 

between the two stress measures was only 0.59, indicating that convergent validity was less than 

expected. Medication errors and patient falls were found to be significantly associated with 

perceived stress but not with reported stress symptoms. Intravenous administration errors were 

not correlated with either measure of stress, nor were nursing injuries or burnout indicators. This 

study provides only weak evidence of a relationship between perceived stress and medication 

errors or patient falls.

Jones et al.195 in a single publication reported the results of four studies examining the 

relationship of job stress and patient safety. Study 1 examined the relationship between the 

malpractice risk of 91 departments of five hospitals and stress in those departments. Stress was 

measured with the Human Factors Inventory, which included subscales for job stress, 

organizational stress, job satisfaction, and personal stress. High malpractice risk departments 

were defined as those having one or more malpractice claims attributed to human error in the 

preceding year or were identified as having unacceptable clinical practices in interviews with at 
43

least two hospital personnel. The 13 high malpractice risk departments had significantly higher 

scores than the 78 low malpractice risk departments for job stress, job dissatisfaction, and 

organizational stress, but not for personal stress. In a repeat analysis comparing the 13 high-risk 

departments with 13 low risk departments matched for the acuity of patient care provided in 

those departments, the finding persisted, suggesting that the findings are independent of the type 

of department or the acuity of care provided therein. These data suggest an association between 

job stress, organizational stress, and job dissatisfaction in a hospital department and the 

malpractice risk experienced by that department, although the direction of causation is uncertain.

The second study in the report by Jones et al.195 examined the relationship between the 

malpractice experience of hospitals and the average level of stress measured among the 

hospital’s employees. Aggregate hospital scores for job stress, organizational stress, and job 

dissatisfaction were highly correlated with one another and with a hospital’s malpractice claims 

experience, while personal stress was not. After controlling for number of hospital beds, job 

stress and organizational stress remained significantly correlated with malpractice claims 

experience, while personal stress and job dissatisfaction were not. These data suggest an 

association between employee stress and the malpractice claims brought against those hospitals. 

High stress could be the cause of clinical practices associated with higher malpractice risk, but 

malpractice claims also could be the cause of higher employee stress.

Two studies of a stress management intervention were also reported by Jones. Based on 

analysis of stress data obtained using the Human Factors Inventory, a five part program was 

implemented, including 1) high level management information and feedback about hospital 

employee stress scores; 2) departmental modifications in communication, organization, and 

personnel policies implemented by managers of high stress departments working with 

consultants; 3) feedback sessions with employees about stress levels and stress management; 4) 

viewing of stress management training videocassettes by hospital employees; and 5) a program 

for employee counseling and assistance. A pilot pre-/post-intervention study (Study 3) 

demonstrated that the frequency of medication errors dropped by half after implementation of the 

program at a single hospital, but did not attempt to document that the intervention reduced 

employee stress levels. The fourth study, a nonrandomized, controlled trial, reported by Jones et 

al.195 then assessed the impact of this same stress management program on hospitals’ malpractice 

claims rates. Hospitals agreeing to participate in the trial (n=22) were compared to control 

hospitals (n=22) matched for size, prior frequency of malpractice claims (the dependent variable 

in the study), location, and the types of patient care services offered. Following implementation 

of the stress management programs rates of malpractice claims fell in the intervention hospitals 

and rose in the control hospitals. The studies found a consistent relationship between employee 

stress and various measures related to patient safety and provide a strong argument for 

recommending further research about stress among health professionals and particularly the 

effects of stress reduction programs conducted in hospitals. The limitations of this trial are (1) 

the main outcome measure, malpractice claims, is a poor surrogate for patient safety; and (2) 

because it was not a randomized trial, it is likely that hospitals that adopted the intervention 

differed from those in the control group.

The limited available evidence on the effects of stress among health professionals does not 

lead to judgments about sufficient evidence to answer any of the key questions, but the available 

evidence does suggest that further research is indicated.

 
44

1. Does stress affect patient outcomes that are related to patient safety? Organizational 

stress and job stress were associated with higher malpractice risk195 and patient falls.194 

Malpractice risk was lower in a controlled trial of a stress management program.195 

Malpractice events are a poor measure of patient safety, so there is insufficient evidence 

to conclude that stress causes increased adverse patient outcomes.

2. Does stress affect the rate of medical errors? Separate studies having relatively weak 

designs found that medication errors were higher on hospital nursing units with high 

stress194 and that medication errors fell following implementation of a stress 

management program.195 However, there is insufficient evidence to conclude that stress 

leads directly to medical errors.

3. Does stress affect the rate of recognition of medical errors after they occur? No studies 

addressed this question.

4. Does stress affect the probability that adverse events will occur following detected or 

undetected medical errors? No studies addressed this question.

5. Does the complexity of the plan of care affect whether stress affects patient outcomes that 

are related to patient safety? No studies addressed this question.

6. Does stress affect measures of service quality in industries other than health care? No 

studies addressed this question.

Dissatisfaction and Burnout

Bond and Raehl196 conducted a survey study to examine the relationships between 

pharmacists’ estimates of the risk of medication dispensing errors and pharmacist worksite, 

workload, workflow, and job satisfaction. A lower estimated risk of dispensing errors was 

correlated with pharmacists’ satisfaction, including satisfaction with career, working conditions, 

work hours, time for clinical activities, level of staffing, level of technician support, and level of 

technician training. Pharmacists estimated a higher risk of dispensing errors under conditions of 

greater workload (prescriptions per hour) and in certain work sites. A lower estimated risk of 

dispensing errors was associated with 1) greater clinical time (consulting with physicians, 

counseling patients, etc.); 2) management time (practice management, committee work, etc.); 3) 

professional society membership and professional development activities; and 4) longer 

experience. No consistent pattern was evident between type of worksite and the other variables 

that were associated with a higher estimated risk of dispensing errors. The strongest predictor of 

low risk of dispensing errors was the time available for dispensing each prescription.

DeVoe et al.197 used a survey to examine whether career dissatisfaction was associated with a 

perceived inability to provide high quality care among family physicians. Nearly 18 percent of 

the respondents reported dissatisfaction with their career, and dissatisfaction was more frequent 

among physicians reporting inadequate time spent with patients, inadequate freedom to make 

decisions that meet patient needs, and overall inability to provide high quality care to patients. 

Firth-Cozens and Greenhalgh198 investigated the relationship between self-reported stress and 

inadequate patient care among physicians practicing in the United Kingdom. These authors 

collected narrative accounts of circumstances “where stress related symptoms have affected your 

patient care” and coded the narratives according to a previously developed schema for causes of 

stress and consequences of stress. Causes of stress in the schema included tiredness, overwork 

pressure, anxiety/depression, alcohol, and boredom. Consequences of stress in the schema 

included irritability and anger, a lowered standard of care, serious nonfatal mistakes, and patient 
45

deaths. Physicians in the sample were members of a cohort being followed since medical school 

in 1984. Of 302 surveys, 255 were returned, 76 of which contained answers to an open-ended 

question about the impact of stress on care, including 82 narratives. Results of coding these 

narratives indicated that tiredness (57.4 percent) and pressure from overwork (27.7 percent) were 

most often cited as causes of inadequate patient care, while depression or anxiety (8.5 percent), 

the effects of alcohol (5.4 percent) and boredom (1.0 percent) were less frequently indicated. 

The effects of stress on patient care were mainly a lowered standard of care (50 percent) and 

irritability and anger (40.2 percent), while serious mistakes (7.4 percent) and patient deaths (2.4 

percent) were less frequently reported. Two patient deaths were reported and were attributed to 

tiredness and pressure from overwork. Six incidents of serious mistakes were reported, four of 

these were attributed to tiredness and two were attributed to tiredness and overwork. The 

authors caution that their data are self-report by informants and may be biased by informant 

perceptions and beliefs, but suggest that their data support a model in which tiredness and 

overwork interact with stress, self-criticism and lowered standards of care in a self-reinforcing 

cycle.

Shanafelt et al.199 examined the relationships among working conditions, resident burnout, 

and patient care practices. An anonymous survey was mailed to 151 resident physicians in a 

multi-hospital university training program in the Northwest United States. Three quarters of the 

respondents met the criteria for burnout. Residents with burnout more frequently reported 

suboptimal patient care practices (defined as errors in medication or treatment, discharge of 

patients to reduce team workload, paying inadequate attention to the social or personal impact of 

illness on a patient, and not fully discussing treatment options or answering a patient’s 

questions). Depersonalization, a subscale of the burnout inventory, exhibited a 'dose-response' 

relationship with self-reported suboptimal care, while the emotional exhaustion and personal 

accomplishment subscales were not significantly associated with suboptimal patient care 

practices. Among residents with burnout, positive depression screen results were more common 

(44/87 vs. 8/28) as was self reported depression (27/87 vs. 3/28) and career dissatisfaction (36/87 

vs. 3/28). Stressors contributing to burnout that were identified most often by residents were 

inadequate sleep (41 percent), frequent shifts over 24 hours in length (42 percent), and 

insufficient leisure time (42 percent). Personal coping strategies identified as most helpful in 

dealing with stress were talking with family (72 percent) or talking with other residents (75 

percent). Residency program features identified as most helpful for managing stress were having 

at least four days off per month (97 percent), the availability of ancillary help for patient care (95 

percent), and the presence of night float call system (64 percent).

Leppa200 examined the relationships among disruption of the nursing unit work group, 

nurses’ job satisfaction, and their perceived quality and safety of patient care. Nurses were 

surveyed to assess job satisfaction and perceived quality and safety of patient care. Nursing unit 

work group disruption was assessed by measures of absenteeism, turnover, and degree of agency 

nurse use. The authors reported a) that nursing unit work group cohesion was most strongly 

correlated with job satisfaction; b) that nursing unit work group cohesion was inversely related to 

short-term disruption, indicated by the use of agency nurses, but not to long-term disruption, 

indicated by new hires, terminations, and transfers; and finally that c) the degree of agency nurse 

use was inversely related to perceived quality of patient care.

Carey189 has characterized job satisfaction using a multi-component model that includes error 

orientation, perceived workload, job control, monotony, collectivism and autonomy. Self-report 

survey instruments were returned by 209 of 710 nurses from a broad spectrum of nursing work 
46

sites and job types in the United States. The survey combined items from extant validated 

instruments and scales for the work variables listed. A panel of eight subject matter experts was 

used to assess nursing work types according to the criticality and pace of care, the expertise 

required, the expected frequency of errors and crises, the amount of stress, and the degree of 

autonomy associated with each nursing work type. Error orientation, a recent construct in 

industrial and organizational psychology, is composed of eight subscales: Error Competence 

(ability to handle errors); Learning from Errors (learning something that can be applied long 

term); Error Risk Taking (flexibility and openness to errors; Error Strain (stress experienced 

when errors are made); Error Anticipation (a pessimistic attitude toward errors; Covering Up 

Errors (seeing errors as a threat within the organization); Error Communication (telling others 

and asking for help in correcting errors); and Thinking About Errors (analysis of errors). 

Error Orientation was found to be correlated with Job Control and Collectivism, but not with 

Workload or Monotony, although overall the amount of variance explained in a regression 

analysis was small. Examining subscales of Error Orientation, Job Control was associated with 

Error Competency (the ability to handle errors when they occur), Learning from Error (learning 

something that can be applied in the long term), and Error Risk Taking (flexibility and openness 

to errors). Collectivism was correlated with Error Communication (telling others and asking for 

help), Thinking About Errors, and Collectivism was inversely correlated with Error Anticipation 

(pessimism about errors). Increased Workload was correlated with Covering Up Errors, while 

Autonomy was associated with Error Risk Taking (flexibility and openness to errors). All data 

in the study were based on self report, and no external or objective measure of either working 

conditions or error behavior was included in this study.

Overall, there is weak and inconclusive evidence that health professional burnout and 

dissatisfaction have a significant impact on patient safety.

1. Do burnout and dissatisfaction affect patient outcomes that are related to patient safety? 

Anecdotal data from qualitative studies suggest that physicians attribute some patient deaths 

to ‘overwork pressure’ and tiredness,198 but the evidence is insufficient to answer this 

question. 

2. Do burnout and dissatisfaction affect the rate of medical errors? Surveys suggest that 

pharmacists’ dissatisfaction is associated with higher rates of dispensing errors,196 that 

burnout among physicians in training is associated with suboptimal patient care practices,199

and that among family physicians the inability to provide high quality care is associated with 

career dissatisfaction. Anecdotal data from qualitative studies suggest that physicians 

attribute some instances of ‘serious mistakes’ and lowered standards of care to ‘overwork 

pressure’ and tiredness.198 These results are suggestive that higher levels of burnout and 

dissatisfaction cause higher rates of medical errors, but the evidence is not sufficient to reach 

this conclusion. 

3. Do burnout and dissatisfaction affect the rate of recognition of medical errors after they 

occur? No studies addressed this question.

4. Do burnout and dissatisfaction affect the probability that adverse events will occur following 

detected or undetected medical errors? No studies addressed this question.

5. Does the complexity of the plan of care affect whether burnout and dissatisfaction affect 

patient outcomes that are related to patient safety? No studies addressed this question.

6. Does stress affect measures of service quality in industries other than health care? No 

studies addressed this question.
47

Physical Environment

Healthcare professionals spend their workdays in a person-made environment that has many 

physical characteristics, including color, visual and auditory stimuli, thermal quality, and 

aesthetics. Research on environmental working conditions has been conducted in a variety of 

industrial settings, and many reports have provided recommendations on workplace design in 

both healthcare and office settings.201-211 These recommendations include a variety of lighting 

and color techniques to improve workers’ efficiency and moods. Other environmental factors 

advocated for the comfort of workers and patients include the provision of indoor plants, fresh 

air, controlled temperatures, and the presence of windows. Windows perform several functions 

including admitting light, ventilation, allowance for vision in and out, and providing aesthetic 

benefits.

Lighting in the workplace has been conceptualized as visual stimuli. Lighting is central to 

the human visual system and is measured by luminous flux, luminous intensity, illuminance, and 

luminance.212 These characteristics directly affect performance of the human visual system, as 

well as affecting circadian rhythm and metabolic functions. Aesthetically, it has been proposed 

that lighting also has an impact on human mood and behavior. However, the exact mechanisms 

by which light characteristics cause psychological and physiological problems are not fully 

understood.

The science of color consists of the technique of measurement of color, known as 

colorimetry, the production of color stimuli, and the visual perception of color. Color consists of 

many dimensions including saturation, value (lightness/darkness), and hues (warm/cool). Color 

is measured in wavelengths, with longer wavelength colors (reds, yellows, and oranges) being 

labeled as “warm colors.” Shorter wavelength colors (blues, greens and purples) are labeled 

“cool colors.” Most research on the effects of color on humans has explored the impact of warm 

and cool colors on human feelings, or affect. Longer wavelength colors are labeled as more 

exciting, while shorter wavelength colors have a calming or quieting effect. However, there has 

been limited research on the effects of environmental colors on gross and fine psychomotor 

activities, physical coordination, and human behaviors. 

Auditory stimuli include both noise and sound. Noise has been defined as a change in 

auditory stimuli that has no relationship to the task that is being performed. On the other hand, 

sound has been defined as auditory stimuli that provide direction to the task at hand. Both noise 

and sound are complex variables that consist of continuous and intermittent auditory stimuli and 

loudness, as measured by decibel levels (dBA). In addition, the control one has over the auditory 

stimuli and the predictability of auditory stimuli can have an influence on how they are 

perceived.213

Thermal stress includes both hot and cold conditions. Measures of thermal stress include air 

temperature, wind speeds, and the submersion of one’s body, or body parts, into cold or hot 

conditions. 

For more than 100 years there has been interest in the impact of the physical environment on 

patients’ ability to heal.214 A recent systematic review addressed the impact of the physical 

environment on patients’ health outcomes.215 Eighty-four studies met inclusion criteria for this 

review. Eleven of the 84 studies examined the impact of auditory stimuli, or noise, on patient 

outcomes. The patient outcomes included subjective and objective measures of sleep, anxiety, 

coping, heart rate and respiratory rhythms. Thirteen of the 84 studies examined the impact of 

light on patient outcomes. Of these 13 studies, the population studied in eleven was neonates. 
48

Outcomes were presence/absence of conjunctivitis, activity levels, heart rate, respiratory rate, 

bilirubin, sleep/wake cycles, presence of retinopathy, cortisol levels, and weight gain. The two 

remaining studies evaluated elderly and psychiatric patients. The first study examined the 

impact of light on serum vitamin D levels. The second study examined the impact of light on 

length of hospital stay for psychiatric patients. One study explored the impact of windows, a 

visual stimulus, on the patient outcome of delirium and depression in an ICU. Thermal stress 

was the focus of 5 of the 84 studies. The population in all of the thermal stress studies was 

neonates. The outcomes of interest included water and heat loss, mortality, body temperature, 

vomiting, edema, weight gain, oxygen consumption, jaundice, and cerebral irritation. None of 

the studies evaluated the relationship of physical environment factors to healthcare working 

conditions.

Thirteen studies were found to have evidence relevant for assessing the effects of physical 

environment characteristics on patient safety (Evidence Table 10).213, 216-227 Three of these 

studies were conducted in healthcare settings, and the rest were conducted in a variety of 

simulated or actual workplaces. One study216 examined an intervention to reduce both ambient 

light and noise levels in a neonatal intensive care unit, while the others examined single 

environmental characteristics. Of the studies examining single factors, two examined effects of 

noise, one examined the effect of cold temperature, and nine examined the effects of light 

intensity, environmental colors, or presence of windows. 

The three studies conducted in healthcare settings all examined medication errors as a 

measure of patient safety, although one of the studies216 also measured adverse outcomes 

(intravenous infiltration, accidental intubation, nosocomial infections, and mortality). The 

Walsh-Sukys study evaluated medication errors by nurses, while the two other studies213, 223

evaluated dispensing errors by pharmacists working in hospital-based pharmacies.

Lighting and Color

Laboratory work conducted prior to the 1960s forms the basis of most current 

recommendations on workplace lighting. This work included studies to determine illumination 

levels that are appropriate for various tasks, quality of illumination such as brightness and glare, 

uniformity of illumination, and quantity of illumination. A review of this older literature by 

Megaw212 found that there is a relationship between lighting and human performance, but it is 

confounded by individual characteristics, such as age, hormonal influences, eye functioning, and 

by task dimensions. 

The intensity of workplace lighting has been found to affect the incidence of dispensing 

errors by pharmacists. Buchanan, et al.223 found that a markedly increased level of workplace 

lighting was associated with a 30 percent reduction in the rate of medication dispensing errors. 

Although only five pharmacists were studied, the benefit of the high lighting level was observed 

for all five of the study subjects. This study also found no difference in error rates between two 

lower levels of workplace lighting. Another recent study216 examined whether reducing levels of 

lighting in a neonatal intensive care unit affected the incidence of medication errors by nurses. 

No significant difference in errors was found between the unit with conventional lighting and the 

unit with lower lighting. These studies suggest that changing lighting levels within the usual 

range has little effect on medication errors but that substantially increased lighting may reduce 

error rates.
49

Five studies in non-healthcare settings examined the effect of the color of the workplace on 

human performance.217, 219-221, 226 All five studies were conducted in simulated office 

environments, and the subjects were college students who were paid or given course credit for 

their participation. Four of the studies manipulated the color of walls, and the fifth217 altered the 

color of the workplace lighting. Two of the studies found significant relationships between color 

and work performance, but the direction of the effect was opposite in these two. While it is 

possible that environmental colors impact healthcare workers differently than office workers, 

these studies suggest that the effects are minor. 

Other visual stimuli that have been proposed to have an impact on human performance are 

plants and windows. No empirical evidence exists to support the popular notion that plants in 

the work place have a positive effect on human performance. Three studies examined the impact 

of windows on human performance.218, 222, 224 Two of the studies were simulated environments, 

on college campuses. Both studies failed to find significant effects of windows on performance. 

One study examined the effect of window shape on human performance.224 This study reported 

a significant difference between square and round window shapes, with a square window 

resulting in less error on an observational task. This well-designed study has limited 

applicability to healthcare settings but may provide insight into such specific tasks as patient 

monitoring. The methodological and theoretical approaches of this study could be adapted to 

future studies in the healthcare setting. 

The cumulative evidence on the effects of workplace lighting is not sufficient to provide 

answers to any of the key questions. The strongest evidence is provided by Buchanan’s study223

that found a positive effect of increased workplace lighting on pharmacists’ dispensing errors, 

but this single study does not provide enough evidence to make a recommendation. However, 

limited further research should clarify whether this finding can be replicated. 

Sound and Noise

Smith228 reviewed the literature between the years 1950-1990 on the impact of noise on 

human performance and accidents and concluded that noise could be one of many contributing 

factors to workplace accidents, but the mechanisms are still not clear. We identified one study 

conducted in a healthcare setting that evaluated the impact of auditory factors on errors 

(Evidence Table 10).213 This observational study was designed to determine the impact of sound 

and noise on pharmacist error. The pharmacists who participated in the study were aware that 

they were being studied. The study found that some types of auditory stimuli appeared to 

decrease error, while other types had no impact. None of the measures of sound or noise were 

found to be increased in the cases having dispensing errors. Another study examined whether 

reducing both light levels and noise reduced medication errors and adverse outcomes in a 

neonatal intensive care unit.216 It found no significant changes in any of the measures of patient 

safety. One additional study examined the impact of noise on human performance that used 

methods comparable to those of studies of working conditions in health care.225 This study used a 

cross-sectional design to examine relationships between accident rates and workplace noise 

levels among male textile workers. No significant differences were found in the incidence, 

frequency, or severity of accidents between low-noise and high-noise work sites.

The studies on workplace noise provide sufficient evidence to answer one key question:
50

1. Do noise levels affect patient outcomes that are related to patient safety? There is 

evidence from one study that noise levels do not affect safety-related outcomes, but this is 

not sufficient to provide a definitive answer to this question. 

2. Do noise levels affect the rate of medical errors? There is sufficient evidence from studies 

conducted in both healthcare and non-healthcare settings to conclude that ambient noise 

levels do not affect the incidence of medication errors.

3. Do noise levels affect the rate of recognition of medical errors after they occur? There is 

insufficient evidence to answer this key question.

4. Do noise levels affect the probability that adverse events will occur following detected or 

undetected medical errors? There is insufficient evidence to answer this key question.

5. Does the complexity of the plan of care affect whether noise levels affect patient outcomes 

that are related to patient safety? There is insufficient evidence to answer this key question. 

Environmental Temperature

The impact of thermal stress on human performance has been of central interest to 

professions that are exposed to extreme conditions, such as those that exist at the South Pole, 

Antarctica, factories, or work conducted in underwater cold conditions. Previous literature 

reviews229-231 have concluded that heat affects performance, but this effect was also influenced 

by the difficulty and complexity of the task being conducted and by individual characteristics, 

such as age, sex, fitness levels, skill levels, experience, and training. Enander232 and Enander and 

Hygge233 reviewed the effects of cold on human performance and found that, while there is 

evidence of adverse effects of cold temperature, further methodological and theoretical work is 

necessary in this area. 

We identified no studies examining high levels of heat on human performance relevant to 

health care, but we identified one study that examined the impact of moderate cold on 

performance.

227 This study found that cold temperatures impair manual dexterity, which could be 

relevant to performance of medical procedures in external environments (such as some aspects of 

the work of emergency medical technicians). 

The cumulative evidence on the effects of environmental temperature is not sufficient to 

provide answers to any of the key questions. The strongest evidence is provided by one study 

that found manual dexterity to be reduced in a cold environment. This study was conducted 

using non-healthcare tasks in a simulated environment and does not provide strong enough 

evidence to make a recommendation. 

Organizational Factors

Organizational factors are structural and process aspects of the organization as a whole. For 

example, work structures such as division of labor or use of teams are organizational factors with 

potential influences on patient safety. Other organization-level factors include facility size, 

funding mechanisms (e.g., profit, not-for-profit), hospital type (e.g., teaching, private), and 

culture. Some organizational factors, such as size and funding base, are difficult to change, 

while others, such as organizational culture, can be changed through systematic initiatives.

In non-healthcare industries such as aviation and nuclear power, organizational factors have 

been identified as important for safety improvement.234-236 Health care has also moved to a 
51

perspective that involves analysis of the organizational system within which errors occur and 

development of system-level responses.237-239 A systems perspective requires a focus upon the 

conditions and processes involved in the delivery of care and examination of how each step in 

the process contributes to both desired and adverse outcomes. This approach employs failure 

analysis strategies such as root cause analysis240, 241 and information management technologies in 

the analysis and solution of error.13, 242 In addition to the view that error results from the 

processes and procedures is the view that considers larger system culture, structure, and the 

context of practice. In this approach organizational culture and professional perceptions are 

emphasized.243

 As health care has focused safety efforts toward the system within which care is delivered 

and away from the individual provider of care, a terminology of organizational factors has 

emerged, as evidenced by such phrases as culture of blame, 244, 245 culture of silence, 246, 247 and 

culture of safety.248-250 These phrases indicate a clear belief that something about the work 

environment and in the context of the work itself influences positively and/or negatively the 

occurrence of errors and adverse events. The non-routine and sophisticated technologies, 

professional staffs, and dynamic environments found in health care necessitate culture 

management. 251 Despite culture’s importance in high reliability systems,252, 253 research into the 

relationship between organizational culture and patient safety remains sparse. 

Early studies of the influence of organizational working conditions on patient safety 

examined magnet hospitals. Spurred by the nursing shortage of the mid 1980s, a number of 

studies of nursing indicated a need for greater involvement by nurses in hospital governance and 

working conditions.254 Concurrently, the American Academy of Nursing examined 

characteristics of 41 hospitals that, despite the national scarcity of professional nurses, were 

successful in recruiting and retaining nurses. These hospitals were termed “magnet hospitals” 

for their ability to attract sufficient nursing staff to maintain high staffing levels.255 Studies of 

these hospitals256, 257 found that several organizational level elements were critical: autonomy, 

participative management, and support of professional development, relatively high 

organizational status of nursing, and collaboration.

Much of the difficulty in examining the role of cultural factors in patient safety results from 

definitional ambiguity. Culture and climate are often used interchangeably and may represent 

different approaches to measurement of the same phenomenon.258 Organizational culture 

definitions are multiple and varied but generally characterize culture as the shared values, norms, 

and tacit assumptions of members within an organization, while others include more tangible 

characteristics such as social practices and capacities in the definition.259

Current studies define climate specifically, such as safety climate or lateness climate.260 

Further definitional ambiguity is added by use of other terms such as work environment, practice 

environment, work context, and job context.261 To add another layer of complexity to the 

understanding of culture and patient safety is the need to consider national, professional, and 

organizational cultures262 in addition to the subcultures within organizations. As workplaces 

become more diverse, these differing (and perhaps conflicting) types of culture become more 

salient. For the purposes of this evidence review, culture is defined broadly. Organizational 

working conditions refer to culture and climate as well as larger organizational influences such 

as structure.

The International Nuclear Safety Advisory Group (INSAG) coined the term safety culture in 

response to the Chernobyl incident and defines it broadly as organizational attitudes and 

structures that place overriding priority on safe plant operations.263 The Veterans Health 
52

Administration has operationalized a culture of safety that reflects the INSAG definition through 

1) public commitments to improving patient safety, 2) resources for special centers, 3) employee 

education, and 4) incentives to promote safety.249 Pizzi264 has identified four factors from the 

literature that characterize a safety culture: 1) recognition of the risk of error in the 

organization’s activities, 2) blame free environment for reporting, 3) collaboration across the 

organization, and 4) organizational resources for safety.

Our literature search yielded 19 studies of organizational factors that influence patient safety, 

and these studies are pertinent to four of the five key questions. Thirteen of the 19 studies 

provide evidence on the influence of organizational culture on patient safety (Evidence Table 

11).42, 260, 262, 265-274 The other six studies provide evidence about various other organizational 

factors, such as hospital ownership, team structure, and division of labor(Evidence Table 11).43, 

275-279

 

Multiple studies have found that measures of organizational culture are related to the 

incidence of adverse patient outcomes. Shortell and colleagues265 examined several 

organizational variables and organizational culture (using a culture instrument) in relation to 

various patient outcomes, including quality of care and risk-adjusted mortality. The study used 

selected items from a standard culture instrument, the Organizational Culture Inventory (OCI), 

and found significant differences across the hospitals sampled in the patient endpoints. 

However, little of the variation was associated with the culture measure. A second study266

compared 39 magnet hospitals with 195 matched controls and observed 4.6 percent lower 

mortality (adjusted for patient composition) for Medicare patients in magnet hospitals as 

compared to nonmagnet hospitals. This supports a conclusion that organizational factors such as 

hospital-level differences in the organization of nursing care have a significant impact on patient 

outcomes. In a study of AIDS inpatient units, Aiken and her colleagues described favorable 

patient outcomes associated with a nursing practice environment characteristic of those found in 

magnet hospitals, including greater responsibility for decision-making and more egalitarian 

interprofessional relationships.42 Jones and Redmond used the OCI in a prospective study of 

organizational redesign in three hospitals.272 In this study the hospital most successful with 

redesign efforts and with higher nurse and patient satisfaction scores had a balanced culture, that 

is, a culture that exhibits characteristics of control and flexibility as well as internal and external 

focus. The other two hospitals had increases in Hierarchical (rule and procedure driven with 

control orientation and internal focus) and Market (outcome and market share driven with 

control orientation and external focus) orientations and concomitant decreases in satisfaction 

scores. However, this study had inadequate measures of patient outcomes. 

A qualitative study268 of accidents and near misses in an emergency department revealed that 

two organizational factors (division of labor and power differentials) contributed to the adverse 

events through poor communication. While these factors or latent conditions existed throughout 

the organization, the authors attributed the adverse effects on patient safety as especially 

problematic in an emergency department. This study is suggestive of the negative impact of 

structural-cultural factors upon patient safety. However it did not examine cases without adverse 

events in relation to the identified organizational factors and cannot be considered adequate 

evidence of the negative impact of these factors.

Two cross-sectional studies43, 279 examined the relationship between organizational factors 

(hospital ownership and whether or not the hospital was a teaching facility) and mortality rates. 

Bond and his colleagues43 found that private ownership (either non-profit or for-profit) was 

associated with lower overall mortality rates. Teaching status per se was not associated with 
53

mortality rates, but size of the medical residency program was inversely related to overall 

mortality rates. Schultz and her colleagues279 also did not find a significant relationship between 

teaching status and mortality rates among patients with acute myocardial infarction. Significant 

differences were found for the financial status of the hospitals, with nonprofit hospitals having a 

lower mortality rate. Both of these studies are limited by having inadequate measures of patient 

casemix, which limits their value as evidence regarding the effect of these organizational factors 

on patient safety. 

A study that measured organizational culture using standard instruments examined the 

relationship of culture to what may be considered intervening variables in a model of patient 

safety. The study274 used the OCI to gather culture data but did not directly measure the 

relationship between culture and outcomes. Culture type was associated with quality 

improvement (QI) implementation, and QI implementation was associated with better patient 

outcomes (scale included errors and inappropriate treatment). Because this study did not 

measure direct relationships, it provides only limited evidence on the effect of organizational 

culture on patient safety measures.

Three studies have examined the recognition and reporting of medication errors in relation to 

organizational culture. A recent survey study271 examined culture in relation to medication error 

reporting. Using a standard measure of culture, the Culture Inventory, there were significant 

differences in cultures among hospitals, with the larger hospitals tending toward more 

hierarchical cultures, which are formalized and highly structured, focusing on rules and 

procedures. Correlations between culture and medication error reporting were non-significant, 

although in the expected direction, with hierarchical cultures negatively associated and group 

cultures (those more people-focused) positively associated with reporting. An earlier study269

used qualitative methods (interview and observation) to assess several aspects of social or unit 

climate and reporting of errors. A unit ranking on “openness” made by the researcher blind to the 

quantitative results was matched with detected unit error rates. In general, lower detected error 

rates occurred on the units with the less open climates. The third study,273 a phenomenological 

exploration of registered nurses’ reporting of wrongdoing, identified several themes linked with 

either professional or organizational culture. Nurses’ reporting was associated with hospital 

policies, consequences, professional ideals, and workplace dynamics. While these three studies 

provide insight into the use of measurement tools for organizational culture, they do not provide 

sufficient evidence to conclude that organizational culture affects reporting of errors.

Organizational factors have been extensively studied in industries other than health care. 

One of the largest studies on the influence of na tional, organizational, and professional cultures 

upon safety in the aviation industry is that of Helmreich and Merritt.262 Using a modification of a 

well-accepted tool for assessing national culture, and expanding existing human factors 

questionnaires to measure organizational culture and attitudes, the researchers found that specific 

aspects of culture had significant impact upon error management. There were extensive data on 

airline crews, especially airline cockpit crews and lesser data on operating rooms. A study of 

utility company work crews showed a negative correlation between both the cooperation and 

quality climate subscales and accidents.260 While using empowerment rather than culture as a 

variable, a study of chemical company work teams found significant correlation between 

empowerment and both safe behaviors and accidents; as empowerment increased, safe behaviors 

increased and accidents decreased.270 In a study of inattention as a cause of railway accidents in 

Australia, Edkins & Pollack276 found that a poor work environment of low morale and other 

organizational factors contributed to railway accidents. They recommended actions towards 
54

improving the safety culture to decrease inattention errors. A meta-analysis of employee 

satisfaction and engagement277 demonstrated a correlation between engagement and safety 

outcomes across 739 business units in 36 companies. However, an Australian study of values 

and safety behavior in hairdressers found no support for an effect of manager and employee 

prevention values on safety behavior.267

Two studies in non-healthcare industries showed a positive effect of the use of teams. The 

first,275 showed that teamwork and goal setting (compared to goal setting alone) had a significant 

positive effect on the quality of ore excavated from a silver mine. The second,278 showed a 

significant inverse relationship between the use of teams and injury rates in small manufacturing 

firms.

The multiple studies of organizational factors have used diverse methodologies and have 

examined a great variety of workplace settings. While they do not provide sufficient evidence to 

answer any of the key questions, they provide direction to further research in this area. 

1. Do organizational working conditions affect patient outcomes that are related to patient 

safety? The studies examining the relationship between organizational culture and the 

incidence of adverse patient outcomes had inconsistent results, and there are too few studies 

of other organizational factors to provide sufficient evidence to conclude that any 

organizational factor has a significant effect on adverse outcomes. Nevertheless, the findings 

of the studies described above provide a useful framework for further studies to answer this 

question.

2. Do organizational working conditions affect the rate of medical errors? There is insufficient 

evidence to answer this question.

3. Do organizational working conditions affect the rate of recognition and reporting of medical 

errors after they occur? Three studies provided evidence suggesting that elements of 

organizational culture affect error-reporting rates, but the cumulative evidence is not 

sufficient to answer this question.

4. Do organizational working conditions affect the probability that adverse events will occur 

after detected or undetected medical errors? No studies were identified that contributed 

evidence for this question.

6. Do organizational working conditions affect service quality in industries other than health 

care? A variety of organizational factors have been studied in multiple industries, and these 

results are highly suggestive that organizational factors affect service quality. However, 

because of the variety of measurement techniques for both organizational factors and 

outcomes, there is not sufficient evidence to identify individual factors that could be applied 

to health care. 
55

Chapter 4. Conclusions

Evidence About the Effects of Healthcare Working 

Conditions

A diverse body of research conducted in both healthcare and other workplace settings 

provides evidence about how working conditions affect processes relevant to patient safety. 

Most of the research has been observational, but there have also been useful experimental studies 

examining some aspects of workflow design and environmental factors. For all categories of 

working conditions examined in this report, there is evidence that provides guidance for patient 

safety improvement. However, as summarized in the individual sections of Chapter 3, the 

sufficiency of evidence to draw clear conclusions varies considerably among the categories. The 

strongest evidence for a direct relationship between working conditions and patient safety is in 

the domains of workforce staffing and workflow design. In the domain of physical environment, 

it has been demonstrated that ambient noise is not a threat to patient safety. The research on 

personal/social and organizational working conditions is insufficient to answer any of the key 

questions, but the available evidence in these domains provides useful guidance for future 

research (discussed further in Chapter 5). 

The field of working conditions encompasses a wide range of specific factors, and it is not 

surprising that the research has tended to focus on certain factors, to the exclusion of others. The 

evidence on how working conditions affect patient safety is limited to certain specific types of 

working conditions and to certain healthcare delivery settings. However, the cumulative 

evidence demonstrates that working conditions are important in influencing patient safety and 

deserve careful attention from healthcare professionals.

In Chapter 3 the evidence was summarized for the specific areas of working conditions 

addressed in this report. In this chapter each key question will be reviewed to summarize the 

overall evidence across all areas of working conditions.

Key Question 1. Do Working Conditions Affect Patient Outcomes that 

are Related to Patient Safety? 

The largest body of available evidence was found to apply to this key question. Many 

observational studies have examined a variety of working conditions and patient outcomes 

related to adverse events, such as in-hospital mortality, nosocomial infections, and decubitus 

ulcers. A common finding across multiple studies examining this question is that the evidence is 

strongest for such non-fatal outcomes as newly acquired infections and short-term rehospitalizations. Evidence for a direct effect of any working condition upon patient death is 

suggestive but relatively weak. For example, in a retrospective review of 1,609 sentinel events 

among hospitalized patients, inadequate nurse staffing was reported to be a contributing factor in 

24 percent.60 However, in multiple studies that have examined nurse to patient ratios and 

mortality rates in inpatient settings, a consistent effect of nurse staffing on patient mortality has 

not been found. Part of this discrepancy is probably due to the fact that mortality is only partly 

related to patient safety problems. For analyzing patient safety, an adverse outcome is 

considered to be an injury caused by health care rather than by the underlying disease. Patient 
56

deaths are often due to overwhelming disease, and casemix measures do not adequately control 

for this mode of causation. Medical errors can cause excess deaths over and above those caused 

by disease, but methods for attributing cause of death have not yet been developed for studies of 

the effects of working conditions on mortality. Although the present available evidence does not 

permit the conclusion that changes in working conditions will lead directly to lower patient 

mortality, the data are sufficient to assert that selected changes in some working conditions are 

likely to lead to lower rates of non-fatal patient outcomes that are related to patient safety.

In studies examining both acute care hospitals and nursing homes, the mechanisms by which 

patient safety is affected appear to be similar across these healthcare settings. However, 

initiatives to improve patient safety need to be based upon data applicable to specific settings. 

For example, guidelines for sufficient nurse to patient ratios will differ greatly among intensive 

care units, non-intensive acute care units, and nursing homes.

Key Question 2. Do Working Conditions Affect the Rate of Medical 

Errors?

The evidence about the relationships between individual working conditions and rates of 

medical errors is diverse. Based on evidence from cross-sectional studies that measured error 

rates and retrospective analyses of reported medical errors, we concluded there is sufficient 

evidence that some working conditions, inc luding patient-to-nurse ratios and workplace 

interruptions, affect rates of certain medical errors. There also is highly suggestive evidence that 

other factors, such as environmental lighting, affect error rates. The types of error studied have 

been limited. Most evidence pertains to medication administration and dispensing errors by 

nurses and pharmacists. There has been very little research conducted on many other important 

areas of error, including breaks in precautions to prevent worker-involved transmission of 

infectious agents among patients and technical errors in operative procedures. Studies of error 

rates among physicians have mostly been conducted in simulated care delivery settings. A 

common feature of nearly all studies of error rates is that the errors involve simple calculation 

and recording tasks. Potentially important but more complex types of error, such as prioritizing 

clinical tasks or developing diagnostic assessments, have not been studied in the context of 

working conditions.

Key Question 3. Do Working Conditions Affect the Rate of 

Recognition of Medical Errors after they Occur?

Recognition and reporting of medical errors have been emphasized as part of patient safety 

improvement programs.262 Our systematic literature review yielded only limited evidence that 

working conditions are related to error recognition and reporting. Some aspects of workforce 

staffing (shift scheduling) and organizational factors were found to affect error reporting. 
57

Key Question 4. Do Working Conditions Affect the Probability that 

Adverse Events will Occur Following Detected or Undetected Medical 

Errors?

The ideal study of working conditions would be a prospective evaluation that examined 

healthcare delivery sites with systematically different working conditions and recorded both 

medical errors and relevant clinical outcomes. Such a study would test whether working 

conditions influenced the relationships among errors and patient outcomes. Unfortunately, no 

such studies have yet been conducted. There have been studies that examined both medical 

errors and adverse outcomes, but they have not provided the detail of results to evaluate whether 

medical errors led directly to adverse outcomes. Thus, there is insufficient evidence to answer 

this key question for any type of working condition at this time.

Key Question 5. Does the Complexity of the Plan of Care Affect 

Whether Working Conditions Affect Patient Outcomes that are 

Related to Patient Safety?

Answering this question requires that research be conducted in a broad enough range of 

healthcare settings to permit synthesis of findings across settings. The body of research on 

working conditions research has been conducted in a wide variety of settings and has examined a 

large number of clinical problems. For selected working conditions, there is sufficient evidence 

to answer this question. Findings about the effect of patient to nurse ratios on adverse event rates 

has been sufficient to conclude that the magnitude of the effect differs between intensive care 

and general inpatient care settings. Studies of physician workload have not demonstrated that 

the complexity of the clinical environment influence the effect of procedural volume on adverse 

event rates, but the range of physician practices examined in these studies has been limited. 

Many types of physician work have not been studied. These findings suggest that extrapolations 

across clinical settings should be made with caution and need to be guided by empirical research.

Key Question 6. Do Working Conditions Affect Measures of Service 

Quality in Industries Other than Health Care?

Research in non-healthcare settings provides useful evidence for evaluating several areas of 

working conditions. Research on the effects of ambient noise in factories has provided evidence 

comparable to that from healthcare settings and has permitted us to judge the evidence to be 

sufficient to conclude that ambient noise does not affect patient safety. Most research on 

workplace colors and aesthetics has been conducted outside of healthcare but is sufficiently 

comparable to conclude that these factors also do not affect patient safety. Research conducted 

on organizational factors outside of health care also has been complementary to healthcare 

studies and contributes to a growing level of knowledge in this domain.

These examples illustrate that research conducted outside of health care is particularly useful 

for answering questions about patient safety that have not been adequately studied in healthcare 

settings. If there is sufficient evidence to answer a key question for a particular healthcare 

working condition, then further evidence from outside health care is interesting but not 

necessary. In contrast, insight into key questions that do not have sufficient evidence from 
58

healthcare settings could be provided by non-healthcare research. Incorporating evidence from 

other industries requires careful analysis of whether the non-healthcare work is similar enough to 

healthcare tasks that generalization of the findings to health care is valid. The similarity of work 

is particularly important for aspects of workflow design. There is a large body of research on 

workflow design in non-healthcare settings such as aviation, but there are not yet well accepted 

criteria for applying much of this work to health care, where work processes are highly 

specialized to meet patient care needs. Thus, we found many areas of working conditions for 

which non-healthcare research could not be applied. 

Key questions 3 and 4 have the lowest level of available evidence from healthcare settings. 

These key questions pertain to the recognition and effects of medical errors. There is inadequate 

research in non-healthcare settings on errors that can be generalized to health care. It is not yet 

understood whether error patterns in settings such as simulated aviation cockpits can be 

generalized to health care. Thus, non-healthcare research did not contribute evidence to answer 

these two key questions.

Clinical and Health Policy Implications of the Findings on the 

Effects of Healthcare Working Conditions

The systematic literature review conducted for this report provided sufficient evidence to 

make specific recommendations about strategies for improving patient safety. These 

recommendations can be summarized as follows.

• Strategies to increase staffing levels of licensed and unlicensed nurses in both acute care 

hospitals and nursing homes will likely lead to improved patient outcomes.

• Preventable complications are lower when complex technical procedures are performed 

by high-volume physicians.

• Duration of experience of the health professional is associated with better patient 

outcomes for some types of clinical care.

• Systems to reduce interruptions and distractions will likely reduce the incidence of 

medical errors.

• Systems to improve information exchange, transfer of responsibility, and continuity of 

care between hospital and non-hospital settings (“hand offs”) decrease medication errors 

and in some settings hospital re-admissions.

• Levels of ambient noise in healthcare settings do not adversely affect patient safety.
59

Chapter 5. Future Research

With the exception of selected work processes pertaining to workflow design, most of the 

evidence on the relationship of working conditions to patient safety is derived from nonexperimental studies. Thus, there remain unanswered questions about the magnitude of 

improvement in patient safety that will be brought by efforts to improve working conditions. 

There is a need for significant future research that evaluates how specific workplace 

interventions will be related to changes in patient outcomes. Such research could be conducted 

as clinical trials or as carefully designed demonstration projects and program evaluation studies.

The available evidence on working conditions also suggests a variety of specific studies that 

will provide further information on how working conditions affect patient safety. Some 

examples of the questions to be addressed in future studies are as follows: 

• How does greater nursing experience affect improved patient outcomes? Is it due to the 

direct patient care or through advice and other collaborative mechanisms among 

experienced nurses and less experienced members of inpatient care teams?

• Previous research has found that both the number of registered nurses and the number of 

total nursing personnel are associated with improved patient safety. What is the 

appropriate mix of registered nurses and unlicensed personnel? What are the most 

effective collaborative models of nursing practice?

• The evidence suggests that rotating shifts and possibly longer shift lengths are a threat to 

patient safety. What scheduling models optimally balance scheduling needs with 

reduction of medical errors?

• Previous research on the relationship of stress and patient safety has used weak measures 

of safety variables. Future research using better measures of patient safety is needed.

• A single study223 has found that increasing the level of workplace lighting decreased error 

rates by dispensing pharmacists. Further research conducted in a variety of work settings 

is needed to evaluate this potential approach to reducing errors that can be implemented 

widely at relatively modest cost.

• Studies of the relationship of organizational factors to patient safety have used only 

limited measures of organizational factors. Further research with valid measures of a 

wide range of organizational factors is needed.

• There is limited evidence that greater experience of health professionals is associated 

with improved patient safety. Further research to examine this association and potential 

benefits of enhanced training programs for less experienced providers is needed.

• Further research is needed to quantify the types, frequency, and severity of interruptions 

and distractions in safety-sensitive areas in health care. Incidents of human error should 

be carefully analyzed for preventable precursors, such as conflicting task requirements, 
60

distractions, and interruptions.280 In addition, studies are needed to elucidate further how 

interruptions and distractions affect medical errors and adverse events. Research is 

needed to assess the effect of interventions on interruptions and distractions, error rates, 

adverse events, and patient and physician satisfaction.

• Research is needed to develop and study hospital discharge programs that focus on the 

root causes of poor outcomes. Further research is needed to determine whether successful 

programs are transferable to other settings. It would also be useful to study existing or 

previously implemented hospital discharge programs to understand how they affect 

medical errors. If existing hospital discharge programs work predominantly through 

active and not latent error reduction, then risk still remains in the system, especially for 

patients not eligible for the program.

• Research in interface design supports matching system performance to operator needs in 

order to improve job performance.281 The human factors community has argued strongly 

for further research in this area for some time.281-284 This community recognizes the need 

for user-driven design, and displays configured to the user’s need to avoid errors.282 

Often, there is more concern with obtaining the most up-to-date technology without 

concern for how to support operators in their tasks.284 Others have supported a research 

effort in interface design in medicine of similar intensity to that seen in other safety 

sensitive industries, such as aviation and nuclear power.111

• There is limited evidence that stress among health professiona ls has an adverse impact on 

patient safety, and further research on measuring stress and interventions to mitigate 

stress in healthcare work environments will help to clarify the importance of this working 

condition. 
61

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Evidence Table 1. Nurse staffing

Author

Year Study Question(s) Design Setting

Category of 

Health 

Professional

Amaravadi 

200046

Is a higher nurse to patient ratio in 

ICUs associated with lower 

preventable complications?

Cross-sectional ICUs in 35 

Maryland 

hospitals

Nurses

Aiken 

199942

Is higher nurse to patient ratio 

associated with lower mortality 

among hospitalized AIDS patients?

Cross-sectional 2 inpatient nursing 

units in each of 20 

U.S. hospitals

Nursing staff

Lichtig 

199942

Are RN staffing levels associated 

with rates of inpatient adverse 

events?

Cross-sectional 462 California and 

229 New York 

acute care 

hospitals

RNs and total 

nursing staff

Anderson 

199848

Do nursing homes with better 

patient outcomes have higher 

levels of nurse staffing?

Cross-sectional 494 Texas nursing 

homes

RNs, LVNs, 

and nursing 

aides 

Shortell 

199450

Is a higher nurse to patient ratio in 

ICUs associated with lower inhospital mortality?

Cross-sectional 1-2 ICUs in each 

of 40 hospitals; 

U.S. nationwide 

sample

Nurses

Aiken 

200266

Is a higher nurse to patient ratio 

associated with lower 30-day 

mortality for patients undergoing 

surgical procedures?

Cross-sectional 168 Pennsylvania 

hospitals

Registered 

nurses

Kovner 

200269

Are staffing levels by RNs and 

LPNs associated with rates of 

adverse events for surgical 

patients?

Cross-sectional 570 US acute care 

hospitals

RNs and LPNs

Needleman 

200252

Are lower staffing levels associated 

with complications?

Cross-sectional 799 hospitals in 

11 U.S. states

RNs, LPNs, 

and nursing 

aides

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

75
Evidence Table 1. Nurse staffing (continued)

Author

Year Working Condition Measure of Safety Casemix Control

Amaravadi 

200046

Estimated ratio of nurses to 

patients during night shifts

Infections, cardiac arrest, reintubation, and in-hospital deathICD-9 codes, age, 

sex

Aiken 

199942

Nurse to patient ratio Mortality within 30 days of 

admission

AIDS severity and 

prognostic measures 

and ADL

Lichtig 

199942

Total nursing hours and 

percentage of hours 

performed by RNs

Decubiti and in-hospital infections ICD-9 codes, nursing 

intensity weights

Anderson 

199848

Ratio of nursing staff hours 

to patient days

Incidence of decubiti, UTIs, and 

fractures

ADLs and selected 

diagnoses

Shortell 

199450

Estimated ratio of nurses to 

patients

In-hospital death APACHE III

Aiken 

200266

Estimated ratio of nurses to 

patients

30-day mortality Demographic 

variables and ICD-9 

codes

Kovner 

200269

Nursing hours per patient 

day

Post-operative thromboses, 

pulmonary complications, and UTIs

Analysis restricted to 

patients with defined 

diagnoses

Needleman 

200252

Nursing hours per patient 

day

Infections, bleeding, shock, cardiac 

arrest, and death

ICD-9 codes, age, 

sex

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

76
Evidence Table 1. Nurse staffing (continued)

Author

Year Main Finding

Design Suitability/ 

Study Execution

Amaravadi 

200046

Units with ratios of more than 2 patients per nurse had higher rates 

of infections but no difference in the rates of the other complications.

Greatest

FAIR

Aiken 

199942

Higher nurse to patient ratio was associated with lower mortality. Greatest

FAIR

Lichtig 

199942

In New York hospitals, higher levels of total nursing and RN hours 

were associated with lower rates of pressure ulcers but not 

nosocomial infection rates. In California hospitals, higher levels of 

total nursing hours were associated with lower rates of pressure 

ulcers and pneumonia. In California hospitals, higher levels of RN 

hours were associated with lower rates of all complications. Total 

nursing hours were not associated with complication rates. Higher 

percentage of RNs was associated with lower rates in California but 

not New York.

Greatest

FAIR

Anderson 

199848

Higher RN and aide staffing was associated with better outcomes. Greatest

FAIR

Shortell 

199450

No association between staffing ratio and mortality. Greatest

FAIR

Aiken 

200266

Patients in hospitals with higher estimated nurse to patient ratios had 

slightly lower 30-day mortality (95% confidence interval of odds ratio 

1.02-1.11).

Moderate 

GOOD

Kovner 

200269

Greater RN hours were significantly associated with lower rates of 

pneumonia but not with rates of the other adverse events. LPN 

hours were not associated with adverse event rates.

Moderate

FAIR

Needleman 

200252

In-hospital complications were lower with higher staffing, but inhospital death did not vary.Moderate 

GOOD

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

77
Evidence Table 1. Nurse staffing (continued)

Author

Year Study Question(s) Design Setting

Category of 

Health 

Professional

Bolton 

200161

Are lower ratios of licensed 

nurse/non-licensed caregivers 

associated with higher rates of 

pressure ulcers and falls?

Cross-sectional Inpatient units of 

38 California 

acute care 

hospitals

Nurses and 

non-licensed 

caregivers

CMS 

200167

Are nursing home staffing ratios 

and staff turnover associated with 

incidence of nosocomial infections, 

pressure ulcers, and weight loss?

Cross-sectional 5,294 nursing 

homes in 10 

states

RNs, LPNs, 

and nursing 

aides

Dimick 

200153

Is a lower nurse to patient ratio in 

the ICU associated with higher 

rates of adverse outcomes after 

hepatectomy?

Cross-sectional 33 Maryland acute 

care hospitals

Nursing staff

Pronovost 

200154

Do hospitals with higher nurse to 

patient ratios in ICUs have lower 

complication rates after surgery for 

abdominal aortic aneurysm?

Cross-sectional 38 Maryland acute 

care hospitals

ICU nursing 

staff

HCFA 

200068

Are nursing home staffing ratios 

associated with incidence of 

pressure ulcers and nosocomial 

infections?

Cross-sectional 1,786 nursing 

homes in three 

states

RNs, LPNs, 

and nursing 

aides

TarnowMordi 

200055

Is in-hospital mortality higher when 

more patients in an ICU have fixed 

nurse staffing?

Time series 

N=1050 

patients

ICU of one 

hospital in 

Scotland

Nursing staff

Bond 

199943

Are staffing levels of RNs, LVNs, 

and pharmacists associated with inhospital mortality rates?

Cross-sectional 3,763 U.S. 

hospitals

Nursing and 

pharmacy staff

Blegen 

199849

Are lower staffing levels associated 

with higher rates of medication 

errors and adverse events?

Cross-sectional 42 inpatient units 

of a single 

university hospital

Nurses and 

non-licensed 

caregivers

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

78
Evidence Table 1. Nurse staffing (continued)

Author

Year Working Condition Measure of Safety Casemix Control

Bolton 

200161

Ratio of licensed to nonlicensed caregiversFall rates and pressure ulcer 

prevalence

None

CMS 

200167

Ratio of each category of 

nursing staff to patients

UTIs, sepsis, pressure ulcers, and 

weight loss

ICD-9 codes, race, 

age, body mass 

index, and ADLs

Dimick 

200153

Nurse to patient ratio 

estimated by ICU director

In-hospital mortality and postoperative pulmonary and infectious 

complications

ICD-9 codes and 

demographic factors

Pronovost 

200154

Nurse to patient ratio 

estimated by ICU director

Multiple post-operative 

complications derived from ICD-9 

codes

Age, sex, race, 

severity of illnes and 

ICD-9 codes

HCFA 

200068

Ratio of each category of 

nursing staff to patients

Respiratory infections, UTIs, sepsis, 

and pressure ulcers

ICD-9 codes

TarnowMordi 

200055

Total number of patients in 

ICU

In-hospital mortality APACHE II

Bond 

199943

Number of full-time RNs, 

LVNs, and pharmacists

In-hospital mortality ICU days, ER visits 

and Medicaid usage

Blegen 

199849

Nursing hours per patient 

day and proportion of 

nursing hours delivered by 

RNs

Rates of medication errors, falls, 

decubiti, in-hospital deaths, and 

urinary or respiratory infections

Patient acuity 

measure

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

79
Evidence Table 1. Nurse staffing (continued)

Author

Year Main Finding

Design Suitability/ 

Study Execution

Bolton 

200161

No difference in fall rates and pressure ulcer prevalence existed 

between hospital units with >70% RN care and units with <50% RN 

care.

Moderate

FAIR

CMS 

200167

Nursing homes with the highest staffing ratios for all three categories 

of nursing staff had the lowest rates of all measures of adverse 

events. Facilities with higher turnover of nursing assistants had 

higher rates of UTIs and pressure ulcers.

Moderate

GOOD

Dimick 

200153

ICU nurse staffing was not consistently associated with complication 

rates.

Moderate

FAIR

Pronovost 

200154

All medical complication rates were higher in hospitals reporting 

lower ICU nurse to patient ratios.

Moderate

FAIR

HCFA 

200068

Nursing homes with the lowest staff to patient ratios had the highest 

rates of all measures of adverse events, for all three categories of 

nursing staff. The relationships were stronger for nursing aides and 

LPNs than for RNs.

Moderate

GOOD

TarnowMordi 

200055

Mortality rates were higher when ICU census was higher. Moderate

FAIR

Bond 

199943

RN and pharmacist staffing were associated with lower mortality. 

LVN staffing was associated with higher mortality.

Moderate

FAIR

Blegen 

199849

Non-intensive care nursing units having a higher ratio of RN care 

had lower rates of medication errors and decubiti. Intensive care 

units had higher rates of these complications and the highest ratios 

of RN care.

Moderate

GOOD

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

80
Evidence Table 1. Nurse staffing (continued)

Author

Year Study Question(s) Design Setting

Category of 

Health 

Professional

Blegen and 

Vaughn 

199856

Are lower staffing levels associated 

with preventable complications?

Cross-sectional 11 U.S. Hospitals Nurses, LPNs, 

and nursing 

assistants

Kovner 

199857

Are RN staffing levels associated 

with rates of in-patient adverse 

events?

Cross-sectional 589 U.S. hospitals 

in 10 states

RNs

American 

Nurses 

Association 

199758

Is nurse staffing lower in hospitals 

having higher rates of decubiti and 

infections?

Cross-sectional 502 hospitals in 

California, 

Massachusetts, 

and New York

RNs, LVNs, 

LPNs, and 

nursing aides

Tutuarima 

199359

Is nursing availability lower for 

patients who fall than for other 

patients?

Case-control 

study of 49 

hospitalized 

stroke patients 

who fell and 49 

who did not 

9 Dutch hospitals Nursing staff

Hartz 

198964

Do hospitals with higher 

percentages of registered nurses 

have lower in-hospital mortality?

Cross-sectional Nationwide HCFA 

data on 3,100 

hospitals

Nursing staff

Flood 

198851

Are adverse events higher on a 

short-staffed inpatient unit?

Cross-sectional 2 inpatient nursing 

units in a single 

U.S. hospital; 

same number of 

beds & equal 

occupancy

RNs, LPNs, 

and nursing 

aides

JCAHO 

200260

Are nurse staffing problems 

identified in reports of sentinel 

events among inpatients?

Case series of 

1,609 sentinel 

events reported 

to JCAHO

U.S. hospitals Nursing staff

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

81
Evidence Table 1. Nurse staffing (continued)

Author

Year Working Condition Measure of Safety Casemix Control

Blegen and 

Vaughn 

199856

Nursing hours per patient 

day

Medication administration errors, 

falls, and cardiopulmonary arrests

Type of nursing unit

Kovner 

199857

Number of RNs per patient 

day

Hospital-acquired infections and 

thromboses

ICD-9 codes

American 

Nurses 

Association 

199758

Nursing hours per patient 

day and RN hours per 

patient day

Decubiti and hospital-acquired 

infections

Nursing intensity 

weights

Tutuarima 

199359

Patient to nurse ratio Inpatient fall Gender, age, and 

clinical severity of 

stroke

Hartz 

198964

Percentage of nurses who 

are registered

In-hospital mortality ICU days

Flood 

198851

Average number of nursing 

staff per month

Nosocomial infections, cardiac 

complications, and gastrointestinal 

disorders

Nursing acuity level

JCAHO 

200260

Nursing staff levels Unanticipated events leading to 

death, injury, or permanent loss of 

function

None

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

82
Evidence Table 1. Nurse staffing (continued)

Author

Year Main Finding

Design Suitability/ 

Study Execution

Blegen and 

Vaughn 

199856

Medication errors were higher with higher staffing levels. Units with 

the highest proportion of RN staffing had higher medication error 

rates. Higher RN staffing levels were associated with fewer falls.

Moderate

FAIR

Kovner 

199857

Higher RN staffing was associated with lower rates of post-operative 

infections.

Moderate

FAIR

American 

Nurses 

Association 

199758

Higher RN hours but not total nursing hours were associated with 

lower decubiti rates in two of the three states. Infection rates were 

not consistently associated with nursing hours.

Moderate

FAIR

Tutuarima 

199359

Nursing ratio was no different between cases and controls. Moderate

POOR

Hartz 

198964

Hospitals with higher ratios of RNs to all nurses had lower average 

in-hospital mortality.

Moderate

FAIR

Flood 

198851

The unit with lower staffing had a higher incidence of complications. Moderate

POOR

JCAHO 

200260

Staffing problems were identified as a factor in 24 percent of sentinel 

event reports.

Least

FAIR

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

83
Evidence Table 1. Nurse staffing (continued)

Author

Year Study Question(s) Design Setting

Category of 

Health 

Professional

Sochalski 

200162

Is higher nursing workload 

associated with higher rates of 

medical errors and adverse 

events?

Survey of 

registered 

nurses

50% sample of all 

licensed 

Pennsylvania 

RNs; 52% 

response rate

9,545 RNs

Byrne

199870

Is a higher intensity of work 

associated with a larger number of 

errors on anesthetic record charts?

Time series British hospital in 

Cambridge

10 trainee 

anesthetists

Hunt 

199863

Do hospitals with lower nurse to 

patient ratios have higher rates of 

adverse events?

Cross-sectional Nationwide study 

of 23 hospital 

trusts in Scotland

Registered 

nurses

Fridkin 

199665

Is nurse staffing lower during an 

outbreak of inpatient catheterassociated bacteremia?

Time series 

study during an 

outbreak of 

infections

Single DVA 

hospital

Registered 

nurses

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

84
Evidence Table 1. Nurse staffing (continued)

Author

Year Working Condition Measure of Safety Casemix Control

Sochalski 

200162

Subjective estimate of 

nursing workload

Subjective estimates of medication 

errors, nosocomial infections, 

patient falls, and incomplete nursing 

tasks

None

Byrne

199870

Workload during simulated 

anesthesia cases

Recording errors on anesthetic 

charts of "patient's" oxygen 

saturation, heart rate, systolic & 

diastolic arterial pressures & endtidal carbon dioxide concentrations

All subjects had the 

same simulated 

case.

Hunt 

199863

Nurse to patient ratio In-hospital mortality and readmission ratesNone

Fridkin 

199665

Patient to nurse ratio in ICU Bacteremia in patients with central 

venous catheter

APACHE-II

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

85
Evidence Table 1. Nurse staffing (continued)

Author

Year Main Finding

Design Suitability/ 

Study Execution

Sochalski 

200162

Indirect association between workload estimates and measure of 

safety.

Least

FAIR

Byrne

199870

Chart recording errors increased with increased workload. Least

FAIR

Hunt 

199863

Lower ratio was associated with higher readmission rate but not with 

mortality.

Least

FAIR

Fridkin 

199665

RN hours in ICU were 20.3/patient-day before outbreak of infections 

and 17.0 during the outbreak.

Least

FAIR

ICU = intensive care unit; AIDS=acquired immuno deficiency syndrome; RN=registered nurse; ICD-9=international 

classification of diseases; ADL=activities of daily living; LVN=licensed vocational nurse; LPN=licensed practical nurse;

CMS=Centers for Medicare and Medicaid Services; HCFA=Health Care Finance Administration; ER=emergency room; 

UTI=urinary tract infection; DVA=Department of Veterans Affairs 

86
Evidence Table 2. Physician volume

Author

Year Study Question(s) Design Setting

Category of 

Health 

Professional

Working 

Condition

Nash 

199979

Does physician caseload 

affect inpatient mortality for 

acute MI?

Crosssectional

n=30,351 

patients

1993 

Pennsylvania 

hospital 

admissions for 

acute MI as 

initial episode 

of care

Cardiologists

Internists

Family 

Practitioners

Volume of 

cases per 

year by 

individual 

physician

Ellis

199781

Does operator volume and 

experience influence the 

procedural outcome of 

percutaneous coronary 

revascularization?

Retrospective 

cohort

n=12,985 

patients

Risk 

adjustment 

analysis

5 hospitals 

with high 

volume 

(>1,000 

procedures per 

year) 

interventional 

cardiology 

programs in 

the US

38 physicians 

with >30 cases 

per year

Number of 

cases per 

year and 

years of 

experience

Margulies 

200180

Does case volume of 

trauma surgeons affect inhospital mortality for 

severely injured patients?

Crosssectional 

n=1754 

patients

California 86 trauma 

surgeons in 

five Level I 

trauma centers

Case 

volume

Hartz

199944

What is the relationship 

between training programs 

and experience and 

adjusted patient mortality 

rates?

Crosssectional

n= 83,547 

patients

3 statewide 

databases 

(New York, 

Pennsylvania 

& Wisconsin)

275 cardiac 

surgeons

Physician 

experience 

and case 

volume

Czaplinski 

199873

Do physicians with three or 

more discharges per year 

for defined diagnoses 

experience lower patient 

mortality than lower volume 

physicians?

Crosssectional 

n=11,316 

patients

Single 

Connecticutt 

hospital

518 physicians Total 

number of 

cases during 

study period

MI = myocardial infarction; ICD-9 = international classification of diseases; CCS = Canadian Cardiovascular 

Society; ACC/AHA = American College of Cardiology/American Heart Association

87
Evidence Table 2. Physician volume (continued)

Author

Year Measure of Safety Casemix Control Main Finding

Design 

Suitability/

Study 

Execution

Nash 

199979

In-hospital mortality ICD-9 codes, severity of 

illness, age, gender, 

secondary logistic 

regression analysis to 

control for referral bias

Patients of physicians with 

higher caseloads for acute MI 

had lower mortality. 

Cardiologists had consistently 

higher caseloads than 

generalists.

Greatest

FAIR

Ellis

199781

In-hospital mortality, 

Q-wave infarction, 

emergency bypass 

surgery

Acute MI, age, CCS 

angina class, 

cardiogenic shock, left 

ventricular ejection 

fraction, modified 

ACC/AHA lesion 

classification score, 

number of diseased 

vessels, prior bypass 

surgery, prior restenosis, 

sex, unstable angina

High volume operators had a 

lower incidence of major 

complications. Years of 

experience were not 

associated with complication 

rates.

Greatest

GOOD

Margulies 

200180

In-hospital mortality Injury severity score, 

Glasgow coma scale, 

age, and type of injury

While institutional case 

volume was weakly related to 

survival rates, physician case 

volume was not.

Moderate

POOR

Hartz

199944

Mortality ratio Predicted mortality rate 

based on patient 

characteristics

Physicians with highest case 

volumes had lowest mortality 

rates. Mortality rates 

increased with higher 

physician age and years of 

experience. Physicians 

trained at more prestigious 

programs or with current 

faculty appointments did not 

have lower mortality rates.

Moderate

FAIR

Czaplinski 

199873

In-hospital mortality Nursing unit on which 

patient was managed

There were no consistent 

mortality differences across 

the diagnostic groups 

between high volume and low 

volume physicians.

Moderate

POOR

MI = myocardial infarction; ICD-9 = international classification of diseases; CCS = Canadian Cardiovascular 

Society; ACC/AHA = American College of Cardiology/American Heart Association

88
Evidence Table 3. Physician specialty

Author

Year Study Question(s) Design

Latif

199884

Do patients with malignant glioma have 

better outcomes if operated on by a 

surgical neuro-oncologist rather than a 

general neurosurgeon?

Cross-sectional

n=236 patients

Gerbert

199685

Do dermatologists have better sensitivity 

and specificity for recommending biopsy 

of skin lesions suspicious for cancer?

Cross-sectional

37 lesions on slides and computer images

25 lesions on patients

Fleischer

200186

How does physician experience and 

specialty affect the quality of surgical 

excision of basal cell carcinoma?

Cross-sectional

n= 1,459 specimens

Dorrance

200087

Are patient outcomes after potentially 

curative colorectal cancer surgery 

different depending on the surgeon's 

specialty?

What factors may help to explain 

differences in outcome among specialty 

groups?

Retrospective cohort

n=378 for resections thought to be curative

n=154 performed by colorectal surgeons

n=126 performed by vascular/

 transplant surgeons

n=98 performed by general surgeons

Nash

199788

Are outcomes for patients with acute 

myocardial infarction different when 

patients are cared for by a cardiology 

specialist versus a generalist?

Cross-sectional

n=40,684 patients

ICD-9 = international classification of diseases

89
Evidence Table 3. Physician specialty (continued)

Author

Year Setting

Category of Health 

Professional

Working 

Condition Measure of Safety

Latif

199884

British university 

medical center

Surgical neurooncologist

general neurosurgeons

Surgical 

specialty

30-day mortality, 

postoperative 

hematoma, and wound 

dehiscence

Gerbert

199685

Dermatology clinic at 

a medical center in 

San Francisco, CA

71 primary care 

residents

15 dermatologists and 

resident dermatologists 

from San Francisco Bay 

area hospitals

Physician 

specialty

Correct 

recommendation of 

biopsy for skin lesions

Fleischer

200186

4 US university 

medical centers

27 dermatologists

9 otolaryngologists

14 plastic surgeons

12 general surgeons

Physician 

experience 

and 

specialty

Tumor-free resection 

margins

Dorrance

200087

A large university 

teaching hospital in 

Glasgow, UK

2 colorectal surgeons

6 vascular/

transplant surgeons

4 general surgeons

Surgeon 

specialty

Cancer recurrence rates

Nash

199788

1993 Pennsylvania 

hospital admissions 

for acute myocardial 

infarction

Cardiologists, internists, 

family practitioners

Expertise 

(generalist/

specialist)

In-hospital mortality

ICD-9 = international classification of diseases

90
Evidence Table 3. Physician specialty (continued)

Author

Year Casemix Control Main Finding

Latif

199884

Tumor type, year 

of treatment and 

prognostic index

Post-operative complication rates were not 

significantly different between the group of 

patients operated on by the neuro-oncologist and 

the group operated on by general neurosurgeons.

Gerbert

199685

Same cases for 

all participants

Dermatologists had sensitivity of .81 and 

specificity of .84 for recommending biopsy. 

Primary care residents had sensitivity of .67 and 

specificity of .73.

Fleischer

200186

Sex, age, tumor 

characteristics

No significant difference was noted for physician 

experience. Otolaryngologists and plastic 

surgeons were more likely to incompletely excise 

tumors when compared to dermatologists. No 

difference was noted between dermatologists and 

general surgeons.

Dorrance

200087

Information on 

demographic and 

operative details 

was collected and 

analyzed

Colorectal surgeons had lower local and overall 

cancer recurrence rates. Differences in local 

recurrence rates seem to be predominantly 

related to the extent of resection.

Nash

199788

ICD-9 codes, 

severity of illness, 

age, gender

Patients cared for by cardiologists, as a group, 

had the lowest risk-adjusted mortality.

ICD-9 = international classification of diseases

91
Design 

Suitability/

Study Execution

Greatest

FAIR

Greatest

FAIR

Moderate

FAIR

Moderate

POOR

Moderate

FAIR
Evidence Table 4. Staff experience

Author

Year Study Question(s) Design

Blegen

200196

What are the relationships between 

quality of patient care and the 

education and experience of the 

nurses providing that care?

Cross-sectional

(Patient care units)

n= 42 units in study 1

n= 39 units in study 2

Goodwin

200189

What is the effect of surgical training 

for coronary artery bypass 

operations on patient mortality?

Cross-sectional

n=2,740 patients

Nelson

200090

What are the residents' 

understanding of drug dose 

calculations and ordering?

What are the short term effects of a 

brief educational intervention on the 

skills required to properly calculate 

dosages and other medications?

Pre-test 

post-test

Hartz

199944

What is the relationship between 

training programs and experience 

and adjusted patient mortality rates?

Cross-sectional

n= 83,547 patients

Stevenson

199991

Does adherence to training 

guidelines for pediatric TEE affect 

the outcome of patients undergoing 

repair of congenital cardiac defects?

Retrospective cohort

n= 219 patients

 TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

92
Setting

Acute-care hospitals

1 large tertiary-care 

hospital in study 1, 11 

hospitals in study 2

Regional cardiothoracic 

surgery unit in 

Cambridge, UK

Urban public hospital in 

New York, NY

3 statewide databases 

(New York, Pennsylvania 

& Wisconsin)

Children's hospital in

Seattle, WA

= human immunodeficiency virus;
Evidence Table 4. Staff experience (continued)

Author

Year

Category of Health 

Professional

Working 

Condition Measure of Safety

Blegen

200196

Nurses Education and 

experience

Medication errors and patient fall 

rates

Goodwin

200189

Senior and trainee 

surgeons

Physician 

composition of 

surgical team

30 day mortality, post-operative 

bleeding and wound infections

Nelson

200090

30 emergency 

medical residents

20=intervention

10=controls

Training Drug dosing errors in simulated 

cases

Hartz

199944

275 cardiac 

surgeons

Physician 

experience and 

case volume

Mortality ratio

Stevenson

199991

8 Physicians Training 

qualification

Diagnoses and hospital deaths

 TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

93
Evidence Table 4. Staff experience (continued)

Author

Year Casemix Control Main Finding

Design 

Suitability/

Study Execution

Blegen

200196

Patient acuity, 

hours of nursing 

care, staff mix

Units with more experienced nurses had lower 

medication errors and lower patient fall rates. 

Units with more baccalaureate-prepared 

nurses were not significantly better.

Moderate

FAIR

Goodwin

200189

Parsonnet score Patients operated on by teams comprised of 

trainees and senior surgeons had the same 

complication rates as patients operated on by 

senior surgeons.

Moderate

FAIR

Nelson

200090

Same questions 

administered to all.

Repeat test had 

similar format.

Training in calculating and executing drug 

ordering is required. A brief educational 

intervention significantly improved short-term 

performance.

Moderate

FAIR

Hartz

199944

Predicted mortality 

rate based on 

patient 

characteristics

Physicians with the highest case volumes had 

the lowest mortality rates. Mortality rates 

increased with higher physician age and years 

of experience. Physicians who had trained at 

more prestigious programs or had current 

faculty appointments did not have lower 

mortality rates.

Moderate

FAIR

Stevenson

199991

None Adherence to training guidelines for pediatric 

TEE favorably affects patient outcomes.

Moderate 

POOR

TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

94
Evidence Table 4. Staff experience (continued)

Author

Year Study Question(s) Design

Pollack

199792

What impact do pediatric critical care 

training programs have on pediatric 

intensive care mortality?

Cross-sectional

n= 2,744 admissions 

(fellowship program)

n= 3,006 

(non-fellowship 

program)

Eldar

199693

How do residents compare to 

qualified surgeons in the 

performance of laparoscopic 

cholecystectomies?

Cross-sectional

n=137 operations and 

n=76 operations

Paauw

199594

What is the ability of primary care 

physicians to recognize physical 

findings associated with HIV 

infection?

Cross-sectional

n= up to 3 cases 

presented

Birnbaum

199497

Does ACLS training affect the 

process and quality of care to 

patients with ischemic heart 

disease?

Pre-test

post-test

n= 869 patients

Pollack

199495

How do ICU size, medical school 

teaching status and specialist status 

affect pediatric ICU mortality?

Cross-sectional

n= 5,415 admissions

n = number; TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

95
Setting

16 pediatric ICUs in the 

U.S.

8 with critical care 

fellowships

Medical center in Israel

University clinic

Seattle, WA

7 rural, community 

hospitals

in Wisconsin

16 pediatric ICUs in the 

U.S.

e unit; HIV = human immunodeficiency virus;
Evidence Table 4. Staff experience (continued)

Author

Year

Category of Health 

Professional

Working 

Condition Measure of Safety

Pollack

199792

Physicians-fellows, 

residents

Training Adjusted mortality rate

Eldar

199693

5 staff surgeons

3 staff/resident 

surgeon teams

Training Post-operative infections and 

other immediate complications

Paauw

199594

134 general 

internists and family 

practitioners

Subjective report 

of physician 

experience with 

HIV patients

Correct diagnosis

Birnbaum

199497

69 Physicians

277 Nurses

115 Other critical 

care staff

Training In-hospital deaths

Pollack

199495

Pediatric intensivists, 

residents

Provider specialty 

training

Adjusted mortality

n = number; TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

96
Evidence Table 4. Staff experience (continued)

Author

Year Casemix Control Main Finding

Design 

Suitability/

Study Execution

Pollack

199792

Severity, diagnosis Overall, adjusted mortality rates were lower in 

pediatric ICUs located in hospitals having 

critical care fellowship programs.

Moderate

FAIR

Eldar

199693

Demographic and 

clinical 

characteristics were 

compared.

Complication rates were no higher in cases 

performed by residents supervised by 

attending surgeons than in cases performed 

by attending surgeons.

Moderate

FAIR

Paauw

199594

Standardized 

patients

The majority of physicians did not detect the 

physical condition and provide correct 

diagnoses. Experience with HIV infection was 

associated with identification of oral hairy 

leukoplakia.

Moderate

FAIR

Birnbaum

199497

Disease severity 

score

ACLS training benefits the ACLS program and 

patient survival.

Moderate

GOOD

Pollack

199495

Physiologic status, 

diagnosis and other 

mortality risk 

factors

Medical school teaching status was related to 

higher mortality and specialist status was 

related to lower mortality. Residents may 

explain the higher mortality seen in teaching 

hospitals. No significant associations were 

noted for ICU size.

Moderate

FAIR

n = number; TEE = transesophageal echocardiography; ICU = intensive care unit; HIV = human immunodeficiency virus;

ACLS = advanced cardiac life support

97
Evidence Table 5. Temporal factors

Author

Year Study Question(s) Design Setting

Rosa 

1993102

Is performance poorer on a 12-hour work 

shift than on an 8-hour shift for utility 

workers?

Time-series study of 

change in work 

schedule

Natural gas utility

Bell 

2001103

Is in-hospital mortality higher among 

patients admitted on weekends?

Retrospective cohort 

of 3.8 million acute 

care patients admitted 

from ERs

All hospitals in 

Ontario, Canada

Bollschweller 

2001104

Is length of work-shift for physicians 

associated with patient complications?

Cross-sectional study 

comparing 8-hour and 

12-hour physician 

shifts

Six German ICUs

Booker 

1995105

Are medication error rates related to 

season or photoperiod?

Time series analysis Alaskan acute 

care hospital

Gold 

1992106

Is the shift assignment of nurses 

associated with incidence of medication 

errors?

Survey of 635 nurses US hospital

Novak 

1988107

Is the shift assignment of factory workers 

associated with rates of on-the-job 

injuries?

Time series analysis Chemical factory

Smith 

1995108

Do night-shift workers perform poorer than 

day-shift workers?

Cross-sectional 22 shiftworkers in 

one nuclear power 

plant

Palinkas 

1986109

Is service in an isolated cold environment 

associated with poorer job performance?

Case-control Navy personnel 

serving in 

Antarctica and 

control personnel

Northrup 

1979110

Is implementation of 12-hour work shifts 

associated with reduced work efficiency or 

increased workplace injuries?

Survey of managers Chemical and 

petroleum plants

ER = emergency room; ICU = intensive care unit; LPN = licensed practical nurse; N/A = not applicable

98
Evidence Table 5. Temporal factors (continued)

Author

Year

Category of Health 

Professional Working Condition Measure of Safety

Rosa 

1993102

Non-medical Length of work shift Computerized battery of 

cognitive and motor skills

Bell 

2001103

N/A Day of week that patient 

received initial care

In-hospital mortality

Bollschweller 

2001104

ICU physicians 8-hour vs. 12-hour workshiftHospital-acquired infections 

and adverse drug reactions

Booker 

1995105

Nursing staff Length of day

time of year

Medication errors

Gold 

1992106

RNs and LPNs Shift assignment Recalled medication errors

Novak 

1988107

Non-medical Rotating shifts vs. fixed 

shifts

Workplace injuries

Smith 

1995108

Non-medical Day-shift vs. night-shift Cognitive function and 

alertness

Palinkas 

1986109

Non-medical Service in isolated 

extreme environment

Desertions, demotions, and 

absences

Northrup 

1979110

Non-medical 8-hour vs. 12-hour shifts Estimated productivity and 

workplace injuries

ER = emergency room; ICU = intensive care unit; LPN = licensed practical nurse; N/A = not applicable

99
Evidence Table 5. Temporal factors (continued)

Author

Year

Casemix 

Control Main Finding

Design 

Suitability/ 

Study Execution

Rosa 

1993102

Some workers 

served as their 

own controls

Workers on 12-hour shifts had poorer 

performance especially at night.

Greatest

FAIR

Bell 

2001103

Age, sex, 

Charlson 

Comorbidity 

Index

Mortality rates were higher for weekend 

admissions for several diagnoses.

Moderate

FAIR

Bollschweller 

2001104

APACHE-II Complication rates were not significantly 

different between ICU's with 8-hour shifts and 

ICU's with 12-hour shifts.

Moderate

FAIR

Booker 

1995105

Nursing 

workload

Medication errors greatest 1-2 months after 

darkest month, after controlling for medication 

error increase with increased temporary worker 

shifts and patient days per month, and decrease 

with increased overtime per month.

Moderate

GOOD

Gold 

1992106

None Reported medication errors were slightly higher 

among nurses who rotated among shifts than 

among nurses who worked the same shift 

compared to day or evening shift nurses. Night 

shift nurses reported more near misses but 

similar rates of medication errors.

Moderate

FAIR

Novak 

1988107

Gender Workers on rotating shifts had higher injury 

rates.

Moderate

FAIR

Smith 

1995108

None Performance was poorer among night-shift 

workers.

Least

FAIR

Palinkas 

1986109

Demographic 

factors

No difference in rates of adverse outcomes 

between groups, but all rates were low.

Least

FAIR

Northrup 

1979110

None No reported increase in accidents or decreased 

operational efficiency.

Least

FAIR

ER = emergency room; ICU = intensive care unit; LPN = licensed practical nurse; N/A = not applicable

100
Evidence Table 6. Interruptions

Author

Year Study Question(s)

Design

Population

Setting

Flynn

1999112

Are workplace 

interruptions 

associated with 

medication dispensing 

errors?

Observational

Task analysis of 

14 pharmacists and 10 pharmacy 

technicians at an ambulatory care 

pharmacy. 

General medical surgical hospital

Alabama

Pape

2002134

Is inpatient nurse use 

of a protocol to reduce 

distractions during 

medication dispensing 

effective?

Quasi-experimental

Comparison of distractions during 

medication dispensing in 2 

intervention and 1 control group.

17 licensed and registered nurses 

during 24 medication dispersing 

cycles.

Medical-surgical nursing unit

520 bed hospital

Southeast Texas

Peterson

1999131

1) Do pharmacists 

believe the risk of 

dispensing errors and 

number of errors are 

increasing?

2) What are the major 

factors contributing to 

dispensing errors?

3) What factors can 

minimize dispensing 

errors?

Observational

Cross-sectional survey 

Mailed to 419 registered 

pharmacists 

Tasmania, Australia

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

101
Working Condition

Interruption - cessation of 

activity due to externally 

imposed reason.

Distraction - observable 

response to externally 

imposed reason (short of 

activity cessation).

Interruptions

Distractions

Interruptions

Workload

Noise

Packaging/

labeling

e interval; NS = not significant
Evidence Table 6. Interruptions (continued)

Author

Year Outcomes Data Collection Analysis

Tools Measures

Flynn

1999112

Association between 

GEFT scores, 

interruptions/

distractions and 

medication dispensing 

errors

GEFT scores, 

work process 

videotapes

Interruptions, 

distractions 

workload; 

dispensing errors 

and types

Analysis of covariance 

using square root 

transformation of the 

number of errors 

(dependent variable) and 

interruptions/distractions 

(independent variables). 

Pape

2002134

Distractions Demographic 

data form

Medication 

Administration 

Distraction 

Observation 

Sheet

Demographics

Nursing education 

and experience

Distractions (total 

and by type)

Descriptive frequencies

One way ANOVA

Multiple and bivariate linear 

progression.

Peterson

1999131

Trend in dispensing 

errors

Factors contributing to 

errors

Factors minimizing 

errors

Survey Response to 

survey questions, 

some with visual 

analogue scale

Frequency of responses 

Comparison of pharmacy 

owners/non-owners.

Correlation between years 

as pharmacist and 

response.

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

102
Evidence Table 6. Interruptions (continued)

Author

Year Results

Design

Suitability/

Study Execution 

Flynn

1999112

164 dispensing errors in 5072 prescriptions

(3.2% rate, CI 3.1-3.4). 

Wrong label error (80%) most common

2022 interruptions during 1143 prescriptions 

6.65% errors in interrupted prescriptions 

5.67% in uninterrupted prescriptions 

Errors increase with interruptions per half hour (p=.004) 

GEFT associated with error rate (p=0.03) 

Relationship between interruptions and errors NS when GEFT 

score used as co-variate.

Greatest

GOOD

Pape

2002134

Significant reduction in distractions during medication 

administration for: 

focused protocol 

(180 distractions p=.014) 

Medsafe © protocol 

(64 distractions, p=.000) 

when compared to control cycles (484 distractions)

Conversation, other personnel interrupting and external noise 

contributed most to distraction.

Moderate

FAIR

Peterson

1999131

50% response rate 

82% felt risk of errors increasing 

47% felt actual errors increasing

Major factors for errors rated as: 

 a) high dispensing volume (84%)

 b) pharmacist overwork (80%)

 c) fatigue (80%)

 d) interruptions to dispensing (76%)

 e) confusing or similar drug names (75%)

Owners rated interruptions as less important than non-owners-

(70% as compared to 80%).

Least

FAIR

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

103
Evidence Table 6. Interruptions (continued)

Author

Year Study Question(s)

Design

Population

Setting

Ely 

1995133

What are family 

physician’s most 

memorable errors and 

their perceived 

causes?

Observational-Cross-sectional 

interviews. 

Random sample of 53 consenting 

family physicians selected from all 

practicing physicians. 

Eastern Iowa

Gladstone

1995132

What are the most 

common risk factors 

for nurses in the 

process of drug 

administration?

Observational-retrospective 

analysis.

79 drug error accident reports

102 nurses

17 nurse managers

District general hospital in 

Southwest England.

Cooper

1984121

What are the types of 

failures, nature of the 

activities during failure, 

and associated factors 

in errors and 

equipment failures in 

anesthesia?

Observational-Critical incident 

analysis-Retrospective structured 

interviews and “instant” voluntary 

reports.

Phase 1: 

48 anesthesiologists

30 residents

13 nurse anesthetists

2 teaching and 2 community 

hospitals 

Boston metro area

Phase 2: 

18 anesthesiologists

21 residents

9 nurse anesthetists 

Teaching hospital 

Boston metro area

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

104
Working Condition

Distractions - preoccupation 

with other 

activities/obligations

Interruptions and 

distractions

Distraction by 1 task to the 

exclusion of others.

Visual restriction

Lack of sleep/fatigue

Supervisor not present 

enough.

Inadequate supervision

Conflicting equipment 

designs.

e interval; NS = not significant
Evidence Table 6. Interruptions (continued)

Author

Year Outcomes Data Collection Analysis

Tools Measures

Ely 

1995133

Categorize recalled 

errors and perceived 

causes

Audiotapes and 

transcripts of a 

focus group 

and in-depth 

interviews 

1) Demographic 

characteristics of 

participants and 

non-participants

2) Classification of 

errors from focus 

group 

3) Causes of error 

rated on 4 point 

scale 

Descriptive statistics

Gladstone

1995132

Ranking of causes of 

drug error and factors 

contributing to error

Drug error 

accident 

reports.

Interviews with 

nurses 

reporting 

errors.

Questionnaires 

to dispensing 

and managing 

nurses.

Type of drug 

error, route of 

administration, 

time of day, type 

of follow-up 

intervention, 

ranking of causes 

of error, nurses' 

feelings and 

manager reaction, 

factors 

contributing to 

error

Descriptive frequencies

Cooper

1984121

Distribution of critical 

incidents by type and 

nature of failure, by 

nature of activity, by 

type of equipment 

involved, and 

associated factors 

cited

Phase 1:

Taped 

interviews, 

summary 

transcripts

Phase 2:

Taped 

interviews, 

summary 

transcripts, 

voluntary 

reporting forms

Data coded in 

content domains, 

fields on reporting 

form

Characteristics categorized 

by two investigator 

consensus, extracted and 

summary frequencies 

provided

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

105
Evidence Table 6. Interruptions (continued)

Author

Year Results

Design

Suitability/

Study Execution 

Ely 

1995133

53/70 (76%) agreed to interview:

53 errors

30 delayed or missed diagnoses

11 surgical mishaps

8 medical treatment mishaps

Causes included

48 (91%) of cases - physician stressors

48 (91%) of cases - process-of- care factors 

38 (72%) of cases - patient-related factors

33 (62%) of cases - physician characteristics 

25 (47%) of cases - physician felt “distracted”

Least

FAIR

Gladstone

1995132

Dose-related

Highest ranked causes:

1) failure to check patient identification

2) prescription illegible

3) nurse distracted

4) miscalculation of dose

5) infusion device set up or adjustment incorrect

The most frequent risk factors cited by nurses were interruptions, 

workload, skill mix and loss of concentration.

Least

FAIR

Cooper

1984121

855 critical incidents 

616 retrospective 

239 “instant” reporting: 

115 (13%) equipment failure

583 (68%) human error

111 (13%) disconnect

46 (5%) other

71 incidents reported "other distractive simultaneous anesthesia 

activities” as an associated factor 

83 visual restriction

55 lack of sleep/fatigue

52 supervisor not present enough

34 inadequate supervisor

34 conflicting equipment designs

Least

FAIR

GEFT = group embedded figures test (for distractibility); CI = confidence interval; NS = not significant

106
Evidence Table 7. Transitions

Author

Year Study Question(s)

Design

Population

Setting

Naylor

1999141

Does a discharge and 4-week 

home follow-up protocol 

implemented by advanced practice 

nurses affect longer term hospital 

readmissions?

RCT

363 patients age > 65 (mean 75) with medical or 

surgical diagnoses admitted from home and at 

high risk for poor discharge outcomes.

2 Philadelphia hospitals

Diem

1996143

What is the effect of an admitting 

resident discharge clinic on house 

staff education and utilization of 

hospital services?

RCT

751 patients discharged to home 

Denver VA medical center

Siu

1996165

Does a nurse practitioner 

intervention begun during 

hospitalization and continued for 3 

visits after discharge improve 

survival and reduce hospital 

readmissions & nursing home 

placement among frail elders?

RCT

354 patients age > 65 discharged

Los Angeles academic medical center

Weinberger

1996159

Does increasing post discharge 

access to primary care reduce 

readmissions and hospital days?

RCT

1396 chronically ill patients from 9 VA medical 

centers.

Throughout US

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

107
Evidence Table 7. Transitions (continued)

Author 

Year Intervention

Naylor

1999141

Discharge planning and 4-week home 

follow-up protocol implemented by 

advanced practice nurses

Diem

1996143

Discharge clinic within 10 days of 

discharge

Siu

1996165

Nurse practitioner assessment prior to 

discharge

Geriatrics team consultation with 

recommendations to primary physician

3 follow-up home visits

Weinberger

1996159

Nurse and primary care physician 

assessment/counseling before 

discharge, nurse call 2nd day post 

discharge, post discharge appointment at 

1 week, phone access (average nurse 

calls 7.5 in 6 mos.)

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

108
Outcome

Hospital readmission

Time to first readmission

Hospital days

Hospital readmissions within 30 days

ER visits

Deaths

Deaths

Readmissions

Nursing home placement

SF-36;(QWB)

Satisfaction

Medication adherence, number of medications

Hospital readmission

Hospital days

s Administration; mos = months; I = intervention; C = control; NS = 

uality of well-being; wks = weeks; hrs = hours; ADL = 
Evidence Table 7. Transitions (continued)

Author 

Year Results

Design 

Suitability/

Study 

Execution

Naylor

1999141

Readmission rate at 24 weeks: I: 36 (20.3%); C: 69 (37.1%) p=<.01.

Time to readmission lower in I than C (p<.001 discharge to 6 weeks, p=.02 

6 to 24 weeks).

No difference between I & C in functional status, depression, and patient 

satisfaction.

Greatest

GOOD

Diem

1996143

Readmissions in 30 days: NS

Mean length of stay: NS

14 (10%) medication errors noted at discharge clinic 5 (3.5%) tests 

 not scheduled as expected.

I group had fewer ER visits than C group (65 [20.8%] vs 123 [28%]; 

 p=.03).

Deaths: NS

Greatest

FAIR

Siu

1996165

At 60 days:

Deaths: NS: 7 (4%) I vs. 8 (4.5%) C

Readmissions: NS: 43 (24%) I vs. 37 (21%) C

Nursing home placement: NS: 7 (4%) I vs. 6 (3.4%) C

SF-36; QWB NS.

I group less satisfied at 30 days (p=.02).

Medication use NS.

Greatest

GOOD

Weinberger

1996159

I group higher monthly readmission than C group (0.19 vs 0.14, 

 p=0.005) and more rehospitalization days (10.2 vs 8.8, p=0.04).

I group more visits to medicine clinic (3.7 vs 2.2, p<0.001).

Quality of life NS at 1 & 6 mos.

I group more satisfied (p<0.001).

Greatest

GOOD

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

109
Evidence Table 7. Transitions (continued)

Author

Year Study Question(s)

Design

Population

Setting

Martin

1994161

Does a hospital discharge team 

for the elderly with 6 weeks of 

support after discharge reduce 

hospital readmissions and length 

of hospital stay?

RCT

54 patients mean age 82

UK hospital and community

Naylor

1994164

Does a discharge and home followup protocol implemented by nurse 

specialists with 2 weeks follow-up 

affect patient and caregiver 

outcomes?

RCT

276 patients age 70+ with 125 caregivers

Admitted to the Hospital of the University of 

Pennsylvania from home with medical or surgical 

cardiac diagnoses

Hansen 

1992163

What is the effect of home visits 

on the elderly after discharge from 

the hospital?

RCT

344 patients >75

Roskilde, Denmark county hospital

Lipton

1992162

Does clinical pharmacist 

consultation to geriatric patients 

and their physicians before and 

after hospital discharge reduce 

clinically significant drug 

problems?

RCT

236 patients >65 discharged on 3 or more 

medications

Community nonteaching hospital in San 

Francisco Bay area

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

110
Evidence Table 7. Transitions (continued)

Author 

Year Intervention

Martin

1994161

Ward team nurse manager assessment

Nurse manager supervising health care 

assistants who visited up to 3 times a day 

for 6 weeks

Naylor

1994164

Discharge planning and home protocol 

implemented by nurse specialists from 

admission to 2 weeks past discharge

Hansen 

1992163

Home visit by district nurse 1 day after 

discharge

General practitioner home visit 15 days 

after discharge

Lipton

1992162

Patient books to record medication; 

medical record review; pharmacist 

consultation to patient and physician at 

and for 3 months post discharge

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

111
Outcome

Place of residence

Hospital readmission

Number of hospital days

Mental test, morale, self-care & domestic 

 ability

Length of stay

Time from discharge to readmission

Hospital readmission

1 year after discharge:

Hospital readmissions

Nursing home admissions

Deaths

Institutional days

Prescribing problems

Appropriateness of prescribing

s Administration; mos = months; I = intervention; C = control; NS = 

uality of well-being; wks = weeks; hrs = hours; ADL = 
Evidence Table 7. Transitions (continued)

Author 

Year Results

Design 

Suitability/

Study 

Execution

Martin

1994161

At six weeks:

Living at home 24 (83%) I vs. 10 (40%) C (p=<0.001).

Readmissions 4 (14%) I vs. 9 (38%) C (p=<0.01).

Living at home still significant 12 weeks and 1 year.

Readmissions: 12 weeks NS

 Alive not readmitted 12 I vs. 4 C; p= <0.05 for 1 year.

Inpatient days lower I vs.C at 12 weeks (p=0.05) but not one year.

No difference mental test.

Greatest

FAIR

Naylor

1994164

Time from discharge to readmission: NS

Medical group: 

 a) Readmissions at 2 wks: I: 3 (4%); C: 11 

 (16%) p=0.02; at 2-6 wks and for 12 wks: 

 NS

 b) Days rehospitalized in I less than C at 2 

 wks (p=.002) & 2-6 wks (p=.01).

Surgical group: NS outcome differences I vs C

Greatest

GOOD

Hansen 

1992163

Readmissions: NS

Nursing home admissions: 16 (9.8%) I vs. 29 (16%) C; p=<0.05

Deaths: 32 (20%) I vs. 43 (24%) C; NS

Group total institutional days: 1950 I vs. 2700 C

Greatest

GOOD

Lipton

1992162

88% patients clinically significant drug problem; 22% patients had 

potentially injurious or life threatening drug problem.

103/123 (84%) I vs. 103/113 (92%) C with at least 1 prescribing problem 

(p=0.05); percent less than optimal medication & wrong dosage lower in I 

than C (p=0.01; p=0.05).

Overall appropriateness of prescribing score .59 I, .76 C (p=0.01).

Greatest

GOOD

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

112
Evidence Table 7. Transitions (continued)

Author

Year Study Question(s)

Design

Population

Setting

Smith 

1988158

Does an intention to increase post 

discharge ambulatory contacts 

reduce non-elective readmissions?

RCT

1001 patients average age 52 consecutively 

discharged from general medicine service with 

chronic disease

University-affiliated hospital in Indianapolis

Townsend

1988160

How does use of a care attendant 

after hospital discharge compare 

with standard aftercare for effects 

on independence, morale and use 

of services?

RCT

903 patients aged >75 with chronic disease

District general hospital & Harrow community, 

UK hospital discharged to home 

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

113
Evidence Table 7. Transitions (continued)

Author 

Year Intervention

Smith 

1988158

Outpatient clinic team nurse phone call to 

patient 1 week post discharge, contact at 

clinic visit, mailing post discharge to 

patient

Townsend

1988160

Care attendant visit before discharge, 

first day home and up to 12 hrs/wk for 2 

weeks

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

114
Outcome

Non-elective hospital admission

Physical independence through ADL

Morale (Philadelphia Geriatric Morale 

 Scale)

Hospital readmissions

Deaths

s Administration; mos = months; I = intervention; C = control; NS = 

uality of well-being; wks = weeks; hrs = hours; ADL = 
Evidence Table 7. Transitions (continued)

Author 

Year Results

Design 

Suitability/

Study 

Execution

Smith 

1988158

Non-elective admissions: NS

 I: .104/patient/month; C: .103/patient/month p=0.4

Readmission days: NS

 I: 7.6% less than C (NS, p=0.5)

 High risk: (31.9% fewer hospital days/patient/month):NS

 I: 1.13 hospital days/patient/month

 C: 1.66 hospital days/patient/month

 p=0.06

Clinic contact increased in intervention:

 I: .53/patient/month; C: .48/patient/month p=.005

Greatest

GOOD

Townsend

1988160

No significant change in independence or morale.

No significant difference in deaths:

 I: 34 (7%); C: 25 (6%)

Difference in total readmissions:

 NS at 3 months:

 I: 105 (23%); C: 102 (23%)

 Significant at 18 months for more than 2 readmissions:

 I: 23 (6.7%); C: 43 (13.9%)

 Days in hospital at 18 months:

 I: 18.2; C: 22.8

Greatest

GOOD

RCT = randomized controlled trial; VA = Veterans Administration; mos = months; I = intervention; C = control; NS = 

non-significant; ER = emergency room; QWB = quality of well-being; wks = weeks; hrs = hours; ADL = 

activities of daily living

115
Evidence Table 8. Stress

Author

Year Study Question(s)

Design/

Population/

Setting

Is stress associated with errors?

Dugan 

1996194

Is stress associated with rates of patient incidents or 

staff turnover?

Cross-sectional

Midwest US acute care hospital

293 of 600 nursing staff on 19 units. 

Response rate 49%.

Jones 

study 1 

1988195

Do hospital departments with high malpractice rates 

have high department-average stress scores?

Cross-sectional

91 departments in five small to medium 

sized general care hospitals across US.

Clinical and non-clinical department staff.

Jones 

study 2 

1988195

Do hospitals with high reported levels of workplace 

and personal stress have higher rates of malpractice 

claims?

Cross-sectional

61 Midwestern hospitals

Does a stress management intervention reduce errors?

Jones 

study 4 

1988195

Is malpractice risk reduced in hospitals employing a 

hospital-wide stress management program, 

compared to matched control hospitals?

Pre-test/Post-test with control group

Jones 

study 3 

1988195

Does a hospital wide stress management program 

reduce the incidence of medication errors?

Time series study of a single US hospital;

676 of 700 employees

IV = intravenous 

116
Evidence Table 8. Stress (continued)

Author

Year Working Condition Measure of Safety

Other respondent 

characteristics measured

Dugan 

1996194

Burnout and self-rated 

stress symptom score; 

stress continuum scale

Patient incidents (medication 

errors, 

IV errors, falls)

Nurse injuries (back injuries, 

needle sticks)

Jones 

study 1 

1988195

Job stress, organizational 

stress

Malpractice claim records, 

error and negligence identified 

by hospital administrative staff

Personal stress, job 

dissatisfaction

Jones 

study 2 

1988195

Job stress, organizational 

stress

Malpractice claims history Personal stress, job 

dissatisfaction

hospital size

Jones 

study 4 

1988195

Stress management 

program

Malpractice claims data one 

year before and one year after 

program implementation

Hospital size, location, and 

malpractice claims history

Jones 

study 3 

1988195

Stress management 

program

Monthly medication error data 

from incident report system for 

8 months prior and 7 months 

after intervention

IV = intravenous 

117
Evidence Table 8. Stress (continued)

Author

Year Main Finding

Design 

Suitability/

Study Execution 

Dugan 

1996194

Patient incident rates were correlated with average perceived nursing 

unit stress (R=0.43,p=0.05) but not with average reported stress 

symptoms (R-0.23, p=0.10).

Nurse turnover was not correlated with perceived stress or reported 

stress symptoms.

Least 

FAIR

Jones 

study 1 

1988195

Departments with higher malpractice risk had higher job stress, 

organizational stress, and job dissatisfaction. Personal stress not 

significantly different.

Least

FAIR

Jones 

study 2 

1988195

Job stress, organizational stress and hospital size were correlated with 

hospital malpractice risk; job dissatisfaction and personal stress were 

not.

Least

FAIR

Jones 

study 4 

1988195

Malpractice claims rate was significantly lower following intervention; no 

reduction in control hospitals.

Greatest

FAIR

Jones 

study 3 

1988195

Medication error rates were significantly less frequent following 

establishment of the stress management program.

Least

FAIR

IV = intravenous 

118
Evidence Table 9. Job satisfaction

Author

Year Study question(s)

Design/

Population/

Setting

DeVoe 

2002197

Is career dissatisfaction associated with family 

physicians’ ability to provide high quality patient 

care?

Survey of 3,166 US family 

physicians 

(~65% response rate)

Shanafelt 

2002199

Are suboptimal patient care practices reported 

more frequently by physicians reporting symptoms 

of burnout?

Survey

Multi-hospital program in NW US

115/151 internal medicine 

residents

Bond 

2001196

Is the risk of pharmacist medication error related to 

workplace, workload, and pharmacist satisfaction?

Survey of 2,437/7,298 Texas 

pharmacists; response rate 33%.

Carey 

2001189

Do relationships exist between collectivism, error 

orientation, and workload, control, and monotony 

among nurses?

Survey of 209/702 RNs in 11 US 

cities; response rate 30%.

Firth-Cozens 

1997198

What factors lead to stress that impacts patient 

care?

Qualitative study open-ended 

questions included in survey of 82 

British physicians who reported 

incidents of adverse care.

302 questionnaires sent out, 225 

returned, 82 responses used.

Leppa 

1996200

Are interpersonal relationships related to job 

satisfaction? 

Is satisfaction with relationships related to 

workgroup disruption? 

Is workgroup disruption related to quality and safety 

of patient care?

Survey of 908 RNs in 4 US 

tertiary care hospitals. Response 

rates in each hospital ranged from 

61% to 75%.

RN = registered nurse 119
Evidence Table 9. Job satisfaction (continued)

Author

Year Working condition Measure of safety

Other respondent 

characteristics 

measured

DeVoe 

2002197

Career satisfaction Subjective assessment of ability to 

provide quality patient care

Personal, professional, 

practice characteristics

Shanafelt 

2002199

Stressors including 

sleep, shift length, leisure 

time; burnout (Maslach 

Burnout Inventory)

Suboptimal patient care practices; 

suboptimal patient care attitudes

Self-reported 

depression and 

substance use

Bond 

2001196

Workload and workflow 

variables, worksite type, 

satisfaction with practice

Subjective estimate of rate of 

medication errors.

Demographic variables

Carey 

2001189

Workload, Job Control, 

Monotony, Collectivism 

and Autonomy; nursing 

work type

Error Orientation, including 

subscales of error competency, risk 

taking, communication, learning 

from and covering up error

Firth-Cozens 

1997198

Tiredness, overwork 

pressure, 

anxiety/depression, 

alcohol, boredom

Irritability/anger, lowered standard 

of care, serious nonfatal mistakes, 

patient death

Coping, work attitudes, 

career choice, alcohol 

use

Leppa 

1996200

Work group cohesion 

(satisfaction with 

relationships, workgroup 

disruption)

2 items on perceived quality of care 12 month data on 

absenteeism, agency 

use, personnel flux, 

controlled for unit size

RN = registered nurse 120
Evidence Table 9. Job satisfaction (continued)

Author

Year Main finding

Design 

suitability/

Study 

execution 

DeVoe 

2002197

18% were dissatisfied with career in medicine.

Inability to provide quality care to patients was associated with higher 

dissatisfaction.

Least 

FAIR

Shanafelt 

2002199

76% of residents had burnout and reported more frequent suboptimal 

patient care practices (errors in medication or treatment or discharge of 

patients to reduce team workload). 

Stressors were: 

 inadequate sleep (41%)

 frequent shifts of over 24 hrs (42%)

 insufficient leisure time (42%)

Coping strategies were: 

 talking with family (72%)

 other residents (75%) 

Program features helpful for stress were: 

 at least four days off/month (97%)

 ancillary help (95%) 

 night float call system (64%)

Least 

FAIR

Bond 

2001196

Higher dispensing error risk was associated with lower pharmacist 

satisfaction, higher workload, and lower professional involvement.

Least 

FAIR

Carey 

2001189

Collectivism score was associated with error communication. Least 

GOOD

Firth-Cozens 

1997198

Causes most often cited: 

 tiredness (57.4%)

 pressure from overwork (27.7%)

Most common consequences cited: 

 lowered standard of care (50%)

 irritability/anger (40.2%) 

2 deaths and 6 serious mistakes were reported.

Least 

GOOD

Leppa 

1996200

Absenteeism and agency use were associated w/ lower satisfaction; 

quality of nursing interaction was associated with perceived quality and 

safety of care; agency use was associated with lower perceived quality 

and safety of care.

Least

FAIR

RN = registered nurse 121
Evidence Table 10. Physical environment 

Author

Year Study Question(s)

Design/

Population/

Setting

Flynn

1996213

What is the association 

between ambient sounds 

and pharmacists' 

prescription filling errors in 

a pharmacy?

Case-control comparison of prescriptions with dispensing 

errors to prescriptions by the same pharmacist without errors. 

Sample of cases was selected from a prospective study of 

prescription dispensing errors.

13 licensed pharmacists, age 26-51, hearing test within 

normal range, working in 451 bed, not-for-profit medical 

center

WalshSukys 

2001216

Do physical modifications 

to reduce light and sound 

levels in a neonatal 

intensive care unit affect 

patient safety?

Cross-sectional comparison of one remodeled unit and one 

traditional unit in a single hospital

126 patient admissions

Knez

1998217

What are the effects of 

office lighting on mood 

and cognitive 

performance?

Randomized trial of two different lighting conditions for 

performing cognitive tasks

n=80; 40 males, 40 females; age 18-55

Volunteer subjects

Stone

1998218

What is the impact of 

windows on worker 

performance?

Randomized trial comparing cognitive tasks in rooms with or 

without windows

Undergraduate students, Midwestern University n=120

Kwallek

1997219

What is the impact of three 

different color schemes on 

worker performance?

Non-random comparison of office tasks between groups of 

office workers matched for office skills and demographic 

factors

3 different colored office spaces the same size: 8ft. 8in. wide, 

11ft. 6.5in. long, 8ft. high

Kwallek

1996220

What is the effect of 

interior office colors on 

clerical tasks?

Randomized trial comparing clerical task performance in nine 

differently colored offices

675 students; 341 males, 334 females; age16-37; colorblinded students excluded 

122
Evidence Table 10. Physical environment (continued)

Author 

Year Outcomes Results

Design 

Suitability/

Study 

Execution 

Flynn

1996213

Uncontrolled and controlled 

sound; predictable and 

unpredictable sound; noise; 

loudness

31 cases of prescription errors were 

compared to 31 error-free cases by the 

same pharmacist. Ambient sound 

levels were not associated with error 

occurrence.

Greatest

FAIR

WalshSukys 

2001216

Measures of patient safety 

included medication errors, 

unplanned extubations, IV 

infiltrations, nosocomial 

infections, and mortality. No 

casemix controls.

Lighting and sound levels were 

significantly lower in the remodeled 

unit, but there were no differences in 

any of the measures of patient safety.

Moderate

GOOD

Knez

1998217

Cognitive performance: 3 

memory tasks, 1 problem-solving 

task, 1 judgment task

No effect of lighting on cognitive 

performance.

Moderate

FAIR

Stone

1998218

Performance: filing, 

computational, or creative task

Windows did not have an effect on 

performance.

Moderate

FAIR

Kwallek

1997219

Performance: Minnesota Clerical 

Test: number comparison task, 

names comparison tasks 

No difference for color schemes alone. 

Individual differences accounted for 

differences in impact of color scheme 

on performance.

Moderate

FAIR

Kwallek

1996220

Human performance: Minnesota 

Clerical Test measures speed 

and accuracy of clerical tasks

There were significantly more 

proofreading errors in white offices vs. 

red or blue offices.

Moderate

FAIR

123
Evidence Table 10. Physical environment (continued)

Author

Year Study Question

Design/

Population/

Setting

Ainsworth

1993221

What is the effect of office 

color on performance?

Randomized trial of typing performance in three office colors

45 female college students

Single office space, 15 ft. by 10 ft., 11 ft. ceiling

Stone 

1993222

What is the impact of 

windows on worker 

performance?

Randomized trial of simulated office and managerial rooms 

with or without windows

Undergraduate students, Creighton University n=40

Buchanan 

1991223

Does illumination level 

affect dispensing errors in 

a high-volume outpatient 

pharmacy?

Time-series analysis of cohort of five pharmacists working in 

outpatient pharmacy in which lighting levels were changed on 

successive days.

Rosenberg

1989224

What is the impact of 

window shape and reticle 

presence on 

performance?

Within-subjects comparison in a simulated space station

20 volunteers, screened for vision acuity

Noweir

1984225

What is the effect of noise 

exposure on industrial 

accidents?

Cross-sectional comparison of textile workers exposed to 

varying noise levels

n=2,458 male workers

Textile mills

Kwallek

1988226

What is the effect of a red 

vs. a blue office 

environment on clerical 

tasks?

Non-randomized comparison of typing skills between groups 

assigned to four different sequences of office colors

36 paid subjects, matched on age and typing ability

Simulated office spaces, 8 ft. wide, 11 ft., 9 in. long, 8 ft. 9 in. 

high, blue or red color

Enander

1987227

What is the effect of 

moderate cold exposure 

on psychomotor and 

cognitive tasks?

Time-series comparing cognitive and motor tasks in two 

temperatures (5.5 degrees celcius and 21 degrees celcius)

n=24; 12 males, 12 females; age 22-45

Climate chamber

124
Evidence Table 10. Physical environment (continued)

Author 

Year Outcomes Results

Design 

Suitability/

Study 

Execution 

Ainsworth

1993221

Performance: words typed, 

errors, ratio of errors per words 

typed

No significant differences for words 

typed, errors, and their ratio for the 

three colors.

Moderate

FAIR

Stone 

1993222

Performance: Managerial or 

computational task

Windows did not have an effect on 

performance.

Moderate

FAIR

Buchanan 

1991223

Dispensing errors were recorded 

by a pharmacist observer.

Error rates were significantly lower with 

the highest illumination level.

Moderate

GOOD

Rosenberg

1989224

Performance in visual alignment 

task

Significant difference between window 

shapes (less error with square 

window). Significant difference 

between reticle-present/absent (less 

error with reticle present condition).

Moderate

GOOD

Noweir

1984225

Accidents: Incidence; Frequency 

rate; Severity rate

No significant difference in incidence, 

frequency rates or severity rates.

Moderate

FAIR

Kwallek

1988226

Number of typing errors Subjects who were in blue room, and 

then switched to red room, made 

significantly more errors.

Least

FAIR

Enander

1987227

Performance tests: color word 

vigilance, simple reaction time, 

key tapping. Dexterity tests: 

screw manual, thumb tapping

The cold environment did not affect the 

performance tests but impaired the 

dexterity tests.

Least

FAIR

125
Evidence Table 11. Organizational factors

Study 

Year Study Question(s)

Design/

Population/Setting Consideration of Culture

Shortell

2000265

What is the impact of 

TQM and organizational 

culture on adverse 

outcomes among CABG 

patients?

Prospective cohort study 

of 16 randomly selected 

US hospitals w/CABG 

volume >200 procedures 

annually 

3,045 CABG patients 

864 administrators, 

nurses, and physicians

Supportive culture: provider 

perception of their involvement 

in decision-making and support 

to make necessary for 

improvement.

Aiken

199942

Is Magnet hospital status 

associated with lower 30-

day mortality among 

AIDS patients?

Cross-sectional study of 

1,205 AIDS patients 

admitted to 40 units in 20 

hospitals

820 nurses

Culture not considered explicitly

Magnet status and nurse control 

of practice used.

Helmreich 

1998262

What is the effect of 

national, professional, 

and organizational 

culture on errors and 

error management in 

aviation and operating 

rooms?

Descriptive:

Cross-sectional with 

some longitudinal data

Comparative:

15,000+ pilots from 22 

airlines in 36 countries

Culture as attitudes and values; 

considers national, 

organizational, and professional 

cultures and notes importance of 

subcultures.

Aiken

1994266

Is Magnet hospital status 

associated with lower inhospital mortality?

Cross-sectional study of 

39 Magnet hospitals and 

195 non-magnet US 

hospitals with more than 

100 Medicare discharges

Culture not a specified variable.

Magnet hospital status includes 

attributes associated with culture 

such as nurse autonomy, and 

collegial relationships.

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

126
Evidence Table 11. Organizational factors (continued)

Study 

Year Measures Main Findings

Design 

Suitability/ 

Study 

Execution

Shortell

2000265

TQM-baseline used as part of 

stratification for sample selection

TQM –Baldridge Scale

Culture Inventory- 20 item

Cost data

Patient Outcome: 

1) mortality

2) other adverse outcomes

3) functional health status 

 (Rand SF36)

4) patient satisfaction

Little association between 

TQM/culture and quality/outcome 

variables.

Greatest

GOOD

Aiken

199942

Magnet status designation

Clinical Environment Index (7 items)

30-day mortality rate

Magnet status, nurse control of 

practice and organization of care in 

dedicated AIDS unit associated 

with lower mortality and higher 

satisfaction.

Greatest

FAIR

Helmreich 

1998262

National culture: Hofstede tool and 

data

Organizational and Professional 

Culture: Operating Team Resource 

Management Survey and Flight 

Management Attitudes Survey

Strong professional cultures both 

impede and enhance safety.

Failures/errors occur at intersection 

of subcultures (e.g. surgeons and 

anesthesiologists).

Greatest

FAIR

Aiken

1994266

Magnet hospital status designation

ICD-9 casemix measure in-hospital 

mortality

Magnet hospitals have lower 

mortality rates than matched 

controls.

Greatest

FAIR

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

127
Evidence Table 11. Organizational factors (continued)

Study 

Year Study Question(s)

Design/

Population/Setting Consideration of Culture

Kaminski 

2001278

What are the 

relationships among 

organizational practices, 

injury rates, and 

productivity?

Cross-sectional

86 small manufacturing 

firms mostly located in 

Midwestern US

Teams

Maierhofer 

2000267

Are manager and 

employee values related 

to compliance with 

wearing gloves among 

hairdressers?

Survey of 218 matched 

manager/employee pairs 

from 842 salons in an 

Australian state

Culture not considered explicitly.

Values of preventive safety was 

independent variable.

Bond 

199943

What is the relationship 

of professional staffing 

levels and hospital 

occupancy and 

ownership with mortality 

rates?

Cross-sectional

3,763 general medical 

US hospitals

Occupancy

Ownership

Teaching affiliation

Schultz 

1999279

What is the relationship 

of hospital 

characteristics and 

mortality due to AMI 

(acute myocardial 

infarction)?

Cross-sectional

373 purposively-sampled 

acute care hospitals in 

California

Profit status

Total daily operating expenses

Teaching status

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

12
Evidence Table 11. Organizational factors (continued)

Study 

Year Measures Main Findings

Design 

Suitability/ 

Study 

Execution

Kaminski 

2001278

Lost-time injury rate: days away from 

work/100FTE

Team: use or non use of teams

Hours per week: average across 

employees

Inverse relationship between injury 

rates and hours worked.

Positive relationship between injury 

rates and teams.

Moderate

GOOD

Maierhofer 

2000267

Scale developed for study - 7 point 

Likert type

Positive relationship between 

manager's and employee's 

prevention values, no relationship 

to safe behavior.

Positive relationship between 

manager's and employee's time 

urgency and negative association 

with safe behavior.

Moderate

GOOD

Bond 

199943

Medicare mortality rates from HCFA 

and AHA

Hospital characteristics from AHA

Inverse association between 

ownership (Private, nonprofit and 

for-profit) and mortality rates.

No association between teaching 

status and mortality rates.

Inverse association between 

medical residency program size 

and mortality rates.

Moderate

FAIR

Schultz 

1999279

Profit status from AHA

Total daily operating expenses from 

Statewide Office of Health Planning 

and Development database

Teaching status from AMA

Relationship of teaching status to 

mortality rates was not signficant. 

Profit status and total daily 

operating expenses significantly 

and positively correlated with 

mortality.

Moderate

FAIR

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

129
Evidence Table 11. Organizational factors (continued)

Study 

Year Study Question(s)

Design/

Population/Setting Consideration of Culture

Stetzer 

1997260

Are employee 

perceptions of work 

climate related to 

workplace accidents?

Survey of 14,550 

employees from 25 work 

divisions in large utility 

company

Climate and culture are similar.

Shared cognitions of the work 

environment.

Multiple different climates 

related to specific elements such 

as safety or absence.

Edmondson 

1996269

How do group and 

organizational-level 

factors account for 

variance in drug error 

(ADE) rates across 

hospital units?

Cross-sectional study of 

8 randomly-selected units 

in two urban hospitals

Not explicitly identified as 

variable, used 'social climate' 

and 'unit climate' (blame vs 

learning) to describe findings 

across several survey items and 

interviews.

Harter 

2002277

What is the relationship 

between employee 

satisfaction-engagement 

at the business unit level 

and business outcomes, 

including accidents?

Meta Analysis

36 companies

7,939 business units

198,514 respondents

3 companies

121 business units had 

safety (accident) data

Employee satisfactionengagement

safety as outcome: lost 

workday/time incident or % of 

lost workdays.

HechanovaAlampay 

2001270

Does work team 

empowerment affect 

unsafe behaviors and 

accidents?

Survey of 531 employees 

of large chemical 

company in 24 

workgroups across 3 US 

states

Culture not considered explicitly.

Empowerment as independent 

variable.

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

130
Evidence Table 11. Organizational factors (continued)

Study 

Year Measures Main Findings

Design 

Suitability/ 

Study 

Execution

Stetzer 

1997260

Employee ratings of workers' and 

supervisors' commitment to quality

Overall climate and subclimates of 

quality and cooperation associated 

with lower accident rates.

Moderate

GOOD

Edmondso

n 

1996269

ADE identified by daily chart review 

and informal unit visits; confidential 

reporting system

Survey of unit social and 

organizational factors based upon 

measure used to study cockpit 

crews' performance 

Observation: semi-structured 

interviews of nursing and support 

staff

Quantitative:

Higher error rates with better unit 

performance.

Qualitative:

Higher error rates with higher 

openness ranking with one 

exception.

Moderate

GOOD

Harter 

2002277

Gallup Workplace Audit (GWA) Employee satisfaction-engagement 

correlated with all outcomes 

including the safety outcome, 

inversely. 

Least

GOOD

HechanovaAlampay 

2001270

Empowerment assessment: 

21 item, 4 point Likert type scale 

completed by work team members 

trained in assessment

Unsafe behavior:

anonymous 18 item self-report 

survey

Empowerment was negatively 

correlated with unsafe behaviors 

and accidents.

Least

FAIR

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

131
Evidence Table 11. Organizational factors (continued)

Study 

Year Study Question(s)

Design/

Population/Setting Consideration of Culture

Wakefield

2001271

Are measures of nurses' 

perceptions of 

organizational culture 

and continuous quality 

improvement related to 

medication 

administration error 

reporting?

Survey of 297 nurses in 6 

Midwest hospitals 

Pattern of shared values, beliefs, 

and expectations.

Boreham 

2000268

What are the sources of 

risk (avoidable increases 

in probability of adverse 

patient outcome)?

Case series of 25 critical 

incidents in two 

emergency departments 

in Britain over 30 months

 Case series dependent.

Jones

2000272

What is impact of culture 

on work redesign 

initiatives?

Case series of 3 midsized community 

hospitals in West and 

Midwest

Respondents not 

identified

Explicitly measured.

Corporate culture as shared 

values and group behavior 

norms. 

Competing Values Framework: 

4 cultural orientations:

1) Clan

2) Developmental

3) Market

4) Hierarchy

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

132
Evidence Table 11. Organizational factors (continued)

Study 

Year Measures Main Findings

Design 

Suitability/ 

Study 

Execution

Wakefield

2001271

Culture Inventory: 20 item

QI Implementation Scale

MAE Reporting Survey

Significant correlation of extent of 

CQI and culture at individual and 

unit level.

Positive, non-significant correlation 

of CI, CQI, and reasons for not 

reporting error.

Hierarchical and rational cultures 

negatively associated with 

estimated errors.

Least

FAIR

Boreham 

2000268

Critical Incidents 

Observations

Some latent conditions were 

organizational factors: 

power and status differential 

coupled with horizontal distribution 

of tasks resulted in breadkdowns in 

communication across 

organizational and professional 

boundaries.

Least

GOOD

Jones

2000272

Culture Inventory

Nurse satisfaction*

Patient satisfaction*

Hospital w/balanced orientation 

most successful in implementing 

change & noted improvement in 

patient and nurse satisfaction 

scores.

2 other hospitals with increase in 

market and hierarchy had more 

difficulty and noted decrease in 

nurse satisfaction and latter also 

noted decrease in patient 

satisfaction.

Least

POOR

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

133
Evidence Table 11. Organizational factors (continued)

Study 

Year Study Question(s)

Design/

Population/Setting Consideration of Culture

Orbe

2000273

How do RNs 

communicate about and 

what factors govern 

reactions to 

organizational 

wrongdoing?

Case series of 202 

critical incidents from 372 

returned surveys sent to 

1,900 randomly selected 

RNs in Midwest w/active 

licenses

Themes emerged from analysis 

related to culture:

1) workplace dynamics

2) professional ideals

3) consequences of reporting

Edkins 

1997276

What are the human and 

organizational factors 

contributing to railway 

accidents?

Case series

Retrospective analysis of 

112 railway accidents 

and near accidents in 

Australia 

Railway Problem Factors (RPF)

managerial/organizational 

origins of accidents. 

Shortell

1995274

Do organizational culture 

and quality improvement 

process affect estimated 

patient outcomes?

Cross-sectional study of 

61 US hospitals primarily 

in the Midwest and West

Values, beliefs, and norms of an 

organization that shape its 

behavior.

Buller 

1988275

What is the effect of 

team building and goal 

setting on task 

performance 

(productivity and 

quality)?

Time series

20 mine stopes 

(excavation areas) over 

15 months

Teamwork and goal setting.

Goal setting alone.

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

134
Evidence Table 11. Organizational factors (continued)

Study 

Year Measures Main Findings

Design 

Suitability/ 

Study 

Execution

Orbe

2000273

Self reports of incidents of 

wrongdoing

5 themes including: 

1) workplace dynamics

2) tension between policy/reality 

w/workload

3) multiple informal ways of 

handling 

4) not report if seen as minor

5) report if patient care or nursing 

standards compromised.

Least

GOOD

Edkins 

1997276

Railway Safety Checklist

RPF identified through focus group 

of drivers and managers and refined 

to 13 by independent raters

15% of incidents attributable to 

organizational factors.

70% due to attentional factors.

Least

POOR

Shortell

1995274

Culture Inventory–20 item

QI Implementation–Baldridge Scale

Patient Impact Scale – improved 

patient outcomes, reduced errors 

and inappropriate treatment, 

increased patient satisfaction, 

improved continuity of care

Financial Impact Scale – reduced 

costs, increased profitability 

improved productivity/efficiency

Negative correlation between 

hospital bed size and culture 

(larger size associated with less 

group/developmental type culture). 

Significant association between 

group/developmental culture and 

QI implementation. 

Significant association between QI 

implementation and patient 

outcomes.

Least

FAIR

Buller 

1988275

Revenue/manshift

Tons/manshift

Grade of ore

Teamwork and goal setting had 

significant positive effect on quality.

No effect of either treatment 

condition on production.

Increase in revenue/manshift noted 

for both groups.

Least

POOR

*general findings noted but measures and results not reported

QI=quality improvement; MAE=medication administration errors; CQI=continuous quality improvement; CI=culture inventory;

TQM=total quality management;CABG=coronary artery bypass graft; ADE=adverse drug event; RN=registered nurse; 

AHA=American Hospital Association; AMA=American Medical Association

135
181

Appendix A: Research Team, Expert Panel, 

Technical Advisory Panel, and Peer Reviewers

Research Team:

David H. Hickam, MD, MPH, Principal Investigator

Professor, Department of Medicine and Division of Medical 

Informatics and Outcomes Research

Oregon Health & Science University 

Portland Veterans Affairs Medical Center

Portland, Oregon

Susan Severance, MPH

Project Coordinator

Division of Medical Informatics and 

Outcomes Research

Oregon Health & Science University

Portland, Oregon

Adrianne Feldstein, MD, MS

Clinical Investigator

Center for Health Research

Kaiser Permanente

Portland, Oregon

Paul Gorman, MD

Associate Professor, Division of Medical Informatics and

Outcomes Research

Oregon Health & Science University

Portland, Oregon

Leslie Ray, PhD, RN

Assistant Professor, Community Health Care

Systems Research

School of Nursing

Oregon Health & Science University

Portland, Oregon

Sherrie Schuldheis, PhD, RN

Director of Nursing Research

Portland VA Medical Center

Portland, Oregon
182

Kathryn Pyle Krages, AMLS, MA

Administrator, Oregon Evidence-based Practice Center

Division of Medical Informatics & Outcomes Research

Oregon Health & Science University

Portland, Oregon

Patty Davies, MS

Reference Librarian

Oregon Health & Science University

Portland, Oregon

Susan Wingenfeld

EPC Program Coordinator

Division of Medical Informatics & Outcomes Research

Oregon Health & Science University

Portland, Oregon

William R. Hersh, MD

Associate Director Oregon Evidence-based Practice Center

Professor and Head, Division of Medical Informatics & 

Outcomes Research

Oregon Health & Science University

Portland, Oregon 

Mark Helfand, MD, MPH

Director, Oregon Evidence-based Practice Center

Associate Professor, Medicine and Medical Informatics 

& Outcomes Research

Oregon Health & Science University

Portland, Oregon

Expert Panel:

Doug Bonacum, CSP, CIH

Vice President, Safety Management

Kaiser Permanente

Oakland, CA

Anthony Ciavarelli, EdD

Professor, School of Aviation Safety

Naval Postgraduate School

Salinas, CA
183

Martha S. Gerrity, MD, MPH, PhD, FACP

Associate Professor, Department of Medicine

Oregon Health & Science University

Portland Veterans Affairs Medical Center

Portland, OR

Michael A. Heumann, MPH

MA Manager

Environmental Occupational Epidemiology

Oregon Health Division

Portland, OR

Joseph Hurrell, PhD

Associate Director of Science

National Institute for Occupational Health and Safety

Centers for Disease Control and Prevention

Cincinnati, OH

Melvin Kohn, MD, MPH

State Epidemiologist and Assistant Administrator

Oregon Health Division

Portland, OR

Loukia Loukopoulos, PhD, Lt USNR

Research Scientist

Aerospace Human Factors Research Division

NASA Ames Research Center

Moffett Field, CA

P.J. Maddox, PhD, RN

Assistant Dean for Health Sciences and Director of the Office of Research

Center for Health Policy and Ethics

George Mason University

Fairfax, VA

Pamela H. Mitchell, PhD, CNRN, FAAN

Elizabeth S. Soule Professor and Associate Dean of Research

School of Nursing

University of Washington

Seattle, WA
184

Misha Pavel, PhD

Professor and Director of the Center for Information Technology

Oregon Graduate Institute of Science and Technology

Oregon Health & Science University

Beaverton, OR

Anne Sales, PhD, MSN

Research Scientist

HSR&D Center of Excellence

VA Puget Sound Health Care System

Tumwater, WA

Patricia F. Uris, PhD, RN

Program Administrator

Colorado State Board of Nursing

Denver, CO

Yan Xiao, PhD

Associate Professor of Anesthesiology and Information Systems

Director, Human Factors and Technology Research

University of Maryland

Baltimore, MD

Technical Advisory Panel:

Anthony Ciavarelli, EdD

Professor, School of Aviation Safety

Naval Postgraduate School

Salinas, CA

Joseph Hurrell, PhD

Associate Director of Science

National Institute for Occupational Health and Safety

Centers for Disease Control and Prevention

Cincinnati, OH

Eric Larson, MD

Associate Dean for Clinical Affairs and Professor of 

Medicine and Health Services

University of Washington

Medical Director

University of Washington Medical Center 

Seattle, WA
185

Robert Petzel, MD

Upper Midwest Health Care Network

Acting Director Network 23

Department of Veterans Affairs

Minneapolis, MN

Anne Sales, PhD, MSN

Research Scientist

HSR&D Center of Excellence

VA Puget Sound Health Care System

Tumwater, WA

Kaveh Shojania, MD

Department of Medicine

University of California, San Francisco

San Francisco, CA

Peer Reviewers:

Individuals

Doug Bonacum, CSP, CIH

Vice President, Safety Management

Kaiser Permanente

Oakland, CA

Pamela H. Mitchell, PhD, CNRN, FAAN

Elizabeth S. Soule Professor & Associate Dean of Research

School of Nursing

University of Washington

Seattle, WA

Deborah Nadzam, PhD

Senior Investigator

Quality Institute

The Cleveland Clinic Foundation

Cleveland, OH

Patricia F. Uris, PhD, RN

Program Administrator

Colorado State Board of Nursing

Denver, CO
186

Yan Xiao, PhD

Associate Professor of Anesthesiology and Information Systems

Director, Human Factors and Technology Research

University of Maryland

Baltimore, MD

Organizations

American Association of Colleges of Pharmacy

Elizabeth A. Flynn, PhD

Associate Research Professor

Center for Pharmacy Operations & Designs

Auburn University

Artesia, NM

American College of Clinical Pharmacy

Nancy Kimel, RPh

Patient Safety Specialist

Performance Improvement

Missouri Baptist Medical Center

St. Louis, MO 

American Medical Association

Heidi Bossley, MSN, MBA

Policy Analyst

Clinical Quality Improvement

Chicago, IL 

American Nurses Association

Patricia S. Yoder-Wise, EdD, RN, CNAA, FAAN

First Vice President, ANA

Professor, School of Nursing

Texas Tech University Health Sciences Center

Lubbock, TX

American Nurses Association

Rose Marie Martin, BSN, RN, OCN

Director, ANA Board of Directors

James Cancer Hospital

Ohio State University

Galloway, OH
187

Federal Agencies

Agency for Healthcare Research and Quality

David Atkins, MD, MPH

Chief Medical Officer

Center for Practice and Technology Assessment

Rockville, MD

Agency for Healthcare Research and Quality

Ronda G. Hughes, PhD, RN, MHS

Center for Primary Care Research

Rockville, MD

Institute of Medicine

Ann E. K. Page

Senior Program Officer

Board on HCS

Washington, DC

Occupational Safety and Health Administration

Sheila Brown Arbury, RN, MPH

Office of Occupational Health Nursing

Washington, DC
189

Appendix B: Search Strategies by Working 

Condition and for the Campbell Collaboration 

Database

Workforce Staffing

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp Medical Errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/ 

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp.

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 workload/ or workload.mp. or overwork.mp. 

13 exp professional competence/ 

14 work schedule tolerance/ or personnel turnover/ or teamwork.tw. 

15 burnout, professional/ 

16 "Personnel Staffing and Scheduling"/ or personnel staffing.mp. 

17 12 or 13 or 14 or 15 or 16 

18 11 and 17 

19 limit 18 to (human and english language) 

20 limit 19 to yr=1980-2002 
190

Database: CINAHL

--------------------------------------------------------------------------------

1 Medication Errors/ or medication errors.mp. 

2 Treatment Errors/ or medical errors.mp. 

3 Diagnostic Errors/ or diagnostic errors.mp. 

4 safety management.mp. or Safety/ 

5 Hospital Mortality/ or hospital mortality.mp. 

6 iatrogenic disease/ or iatrogenic.mp. 

7 Patient Safety/ or patient safety.mp. 

8 *Treatment Outcomes/ 

9 Quality of Health Care/ 

10 patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 WORKLOAD/ or WORKLOAD MEASUREMENT/

13 Personnel Turnover/ or personnel turnover.mp. 

14 TEAMWORK/ or teamwork.mp. 

15 BURNOUT, PROFESSIONAL/ 

16 "Personnel Staffing and Scheduling"/ or personnel staffing.mp. 

17 exp Professional Competence/og, pf, ev, st, td [Organizations, Psychosocial 

 Factors, Evaluation, Standards, Trends] 

18 12 or 13 or 14 or 15 or 16 or 17 

19 11 and 18 

Database: PsycINFO

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 error$.tw. 

8 1 or 2 or 3 or 4 or 5 or 6 or 7 

9 exp Work Scheduling/ or exp Work Rest Cycles/ or exp Workday Shifts/ or work 

 schedule.mp. 

10 teamwork.mp. 

11 exp *occupational stress/ 

12 exp work load/ or work load.tw. 

13 exp Professional Competence/ or professional competence.mp. 

14 9 or 10 or 11 or 12 or 13 

15 8 and 14 

16 limit 15 to human 
191

Workflow Design

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp Medical Errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/ 

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp. 

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 Efficiency, Organizational/ or organizational efficiency.mp. 

13 (workflow or work flow).tw. 

14 exp "Task Performance and Analysis"/ or task performance.mp. 

15 exp *Information Systems/og, sd, td, ma, ut 

16 (equipment safety and patient$).mp. 

17 *Equipment Design/ae, sn, td, mt

18 12 or 13 or 14 or 15 or 16 or 17 

19 11 and 18 

20 limit 19 to (human and english language) 

21 limit 20 to yr=1980-2002 
192

Database: CINAHL

--------------------------------------------------------------------------------

1 Medication Errors/ or medication errors.mp. 

2 Treatment Errors/ or medical errors.mp. 

3 Diagnostic Errors/ or diagnostic errors.mp. 

4 safety management.mp. or Safety/ 

5 Hospital Mortality/ or hospital mortality.mp. 

6 iatrogenic disease/ or iatrogenic.mp. 

7 Patient Safety/ or patient safety.mp. 

8 *Treatment Outcomes/ 

9 Quality of Health Care/ 

10 patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 Organizational Efficiency/ or organizational efficiency.mp. 

13 workflow.mp. 

14 exp "Task Performance and Analysis"/ or task performance.mp. 

15 exp *Information Systems/ma, og, st, td, ut [Manpower, Organizations, Standards, 

 Trends, Utilization] 

16 equipment safety/ and patient$.mp. 

17 *Equipment Design/ae, st, ev, td [Adverse Effects, Standards, Evaluation, Trends] 

18 12 or 13 or 14 or 15 or 16 or 17 

19 11 and 18 
193

Database: PsycINFO 

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 error$.tw. 

8 1 or 2 or 3 or 4 or 5 or 6 or 7 

9 exp Organizational Effectiveness/ or organizational efficiency.mp. 

10 (workflow or work flow).mp. 

11 equipment safety.mp. 

12 (equipment design$ or design$ of equipment).mp. 

13 (job performance or task performance).tw. 

14 9 or 10 or 11 or 12 or 13 

15 8 and 14 

16 limit 15 to (human and english language) 

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp Medical Errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp.

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 exp User-Computer Interface/ 

13 exp Expert Systems/ or expert systems.mp. 

14 (diagnosis or screening or appendicitis or abdominal pain).mp. 

15 13 and 14 

16 (distraction$ or interruption$ or multi task$ or multitask$).mp. 

17 closed loop control$.mp. 

18 limit 17 to review articles 

19 12 or 15 or 16 or 18 

20 11 and 19 

21 limit 20 to (human and english language) 

22 from 21 keep 1-1048
194

Database: CINAHL

--------------------------------------------------------------------------------

1 Medication Errors/ or medication errors.mp. 

2 Treatment Errors/ or medical errors.mp. 

3 Diagnostic Errors/ or diagnostic errors.mp. 

4 safety management.mp. or Safety/ 

5 Hospital Mortality/ or hospital mortality.mp. 

6 iatrogenic disease/ or iatrogenic.mp. 

7 Patient Safety/ or patient safety.mp. 

8 *Treatment Outcomes/ 

9 Quality of Health Care/ 

10 patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 exp Expert Systems/ or expert systems.mp. 

13 (diagnosis or screening or appendicitis or abdominal pain).tw. 

14 12 and 13 

15 closed loop control$.mp. 

16 exp User-Computer Interface/ or user computer interface.mp. 

17 (interruption$ or distraction$ or paging or paged).tw. 

18 14 or 15 or 16 or 17

19 11 and 18 

20 from 19 keep 1-103 

Database: PsycINFO

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 error$.tw. 

8 1 or 2 or 3 or 4 or 5 or 6 or 7 

9 exp Expert Systems/ or expert systems.mp. 

10 (diagnosis or screening or appendicitis or abdominal pain).mp. 

11 9 and 10 

12 closed loop control$.mp. 

13 (distraction$ or interruption$ or paged or paging).tw. 

14 exp Human Computer Interaction/ 

15 11 or 12 or 13 or 14 

16 8 and 15 

17 limit 16 to (human and english language) 

18 from 17 keep 1-234
195

Personal/Social

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp medical errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/ 

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp. 

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 Job Satisfaction/ or job satisfaction.mp. 

13 absenteeism/ 

14 Employee Grievances/ or employee grievance$.mp. 

15 Burnout, Professional/ or professional burnout.mp. 

16 professionalism.mp. 

17 professional culture.mp. 

18 MORALE/ or morale.mp. 

19 Professional Autonomy/ or professional autonomy.mp. 

20 professional power.mp. 

21 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 

22 11 and 21 

23 limit 22 to human 

24 limit 23 to english language 

25 23 not 24 

26 limit 25 to abstracts

27 24 or 26 

28 limit 27 to yr=1980-2002 
196

Database: CINAHL 

--------------------------------------------------------------------------------

1 (medical errors or medication errors or diagnostic errors).mp. 

2 quality of health care/ 

3 *safety/ or safety/st or safety management.mp. 

4 exp hospital mortality/ 

5 Iatrogenic Disease/ or iatrogenic disease.mp. 

6 (patient safety or safety of patient$).mp. 

7 *treatment outcome/ 

8 Patient$.ti. 

9 Job Satisfaction/ or job satisfaction.mp. 

10 ABSENTEEISM/ or absenteeism.mp. 

11 Employee Grievances/ or employee grievance$.mp. 

12 BURNOUT, PROFESSIONAL/ or burnout.mp. or Stress, Occupational/ 

13 PROFESSIONALISM/ or professionalism.mp. 

14 professional culture.mp.

15 MORALE/ or morale.mp. 

16 AUTONOMY/ or PROFESSIONAL AUTONOMY/ or autonomy.mp. 

17 Power/ or professional power.mp. 

18 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 

19 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 

20 18 and 19 

Database: PsycINFO

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 error$.tw. 

8 1 or 2 or 3 or 4 or 5 or 6 or 7 

9 exp Job Satisfaction/ or job satisfaction.mp. 

10 exp EMPLOYEE ABSENTEEISM/ or absenteeism.mp. 

11 employee grievance$.mp. 

12 exp Occupational Stress/ or burnout.mp. 

13 exp Professional Ethics/ or professionalism.mp. 

14 exp Professional Identity/ or professional culture.mp. 

15 exp MORALE/ or morale.mp. 

16 autonomy.mp. 

17 exp Power/ or professional power.mp.

18 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 

19 8 and 18 

20 limit 19 to human 
197

Physical Environment

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp Medical Errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/ 

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp. 

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 exp "Facility Design and Construction"/ or facility design.mp. 

13 exp Health Facility Environment/ 

14 (working environment$ or physical environment$).mp. 

15 exp Air Pollution, Indoor/ or indoor pollution.mp. 

16 indoor lighting.mp.

17 lighting/ 

18 acoustics.tw. or acoustics/ 

19 workplace.mp. 

20 NOISE/ 

21 working conditions.mp. 

22 exp "Interior Design and Furnishings"/ or interior design.mp. 

23 HUMIDITY/ or humidity.mp. 

24 indoor temperature$.mp. 

25 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 

26 11 and 25 

27 limit 26 to human 

28 limit 27 to english language 

29 27 not 28 

30 limit 29 to abstracts 

31 28 or 30 

32 limit 31 to yr=1980-2002 
198

Database: CINAHL 

--------------------------------------------------------------------------------

1 Medication Errors/ or medication errors.mp. 

2 Treatment Errors/ or medical errors.mp. 

3 Diagnostic Errors/ or diagnostic errors.mp. 

4 safety management.mp. or Safety/ 

5 Hospital Mortality/ or hospital mortality.mp. 

6 iatrogenic disease/ or iatrogenic.mp. 

7 Patient Safety/ or patient safety.mp. 

8 *Treatment Outcomes/ 

9 Quality of Health Care/ 

10 patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 exp "Facility Design and Construction"/ or facility design.mp. 

13 exp Health Facility Environment/ 

14 Work Environment/ or working environment.mp. 

15 physical environment.mp. or exp "Interior Design and Furnishings"/ or Noise/ or 

exp 

 "Hospital Design and Construction"/ 

16 exp Air Pollution, Indoor/ or indoor air pollution.mp. 

17 LIGHTING/ 

18 ACOUSTICS/ or acoustics.mp. 

19 workplace.mp. 

20 working conditions.mp.

21 HUMIDITY/ or humidity.mp. 

22 *temperature/ 

23 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 

24 11 and 23 
199

Database: PsycINFO

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 error$.tw. 

8 1 or 2 or 3 or 4 or 5 or 6 or 7 

9 exp Human Factors Engineering/ or facility design.mp. 

10 exp Facility Environment/ or facility environment.mp. 

11 exp Working Conditions/ or working environment.mp. 

12 indoor air pollution.mp. 

13 exp Illumination/ or indoor light$.mp. 

14 exp acoustics/ 

15 exp "noise levels (work areas)"/ 

16 exp Interior Design/ or interior design.mp. 

17 exp Facility Environment/ 

18 exp Temperature Effects/ 

19 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 

20 8 and 19

21 limit 20 to human 
200

Organizational Culture

Database: MEDLINE

--------------------------------------------------------------------------------

1 exp Medical Errors/ 

2 (medical errors or medication errors or diagnostic errors).mp. 

3 quality of health care/ 

4 *safety/ or safety/st or safety management.mp. 

5 exp hospital mortality/ 

6 Iatrogenic Disease/ or iatrogenic disease.mp. 

7 quality assurance health care/ 

8 (patient safety or safety of patient$).mp. 

9 *treatment outcome/ 

10 Patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 Organizational Culture/ or organizational culture.mp. 

13 organizational climate.mp. 

14 exp interprofessional relations/ and og.fs. 

15 leadership/ and og.fs. 

16 managerial style.mp. 

17 management style.mp. 

18 12 or 13 or 14 or 15 or 16 or 17 

19 11 and 18 

20 limit 19 to (human and english language and yr=1980-2002) 

Database: CINAHL

--------------------------------------------------------------------------------

1 Medication Errors/ or medication errors.mp. 

2 Treatment Errors/ or medical errors.mp. 

3 Diagnostic Errors/ or diagnostic errors.mp. 

4 safety management.mp. or Safety/ 

5 Hospital Mortality/ or hospital mortality.mp. 

6 iatrogenic disease/ or iatrogenic.mp. 

7 Patient Safety/ or patient safety.mp. 

8 *Treatment Outcomes/ 

9 Quality of Health Care/ 

10 patient$.ti. 

11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 

12 exp Skill Mix/ or skill mix.mp. 

13 exp Organizational Culture/ or organizational culture.mp. 

14 organizational climate.mp. 

15 *Interprofessional Relations/ 

16 Management Styles/ or management style.mp. 

17 12 or 13 or 14 or 15 or 16 

18 11 and 17 
201

Database: PsycINFO 

--------------------------------------------------------------------------------

1 exp Job Performance/ or human performance.mp. 

2 exp EMPLOYEE PRODUCTIVITY/ or exp Group Performance/ 

3 exp Person Environment Fit/ or person environment fit.mp. 

4 (medical errors or medication errors or diagnostic errors).mp. 

5 iatrogenic disease/ or iatrogenic.mp. 

6 exp Hospitalized Patients/ or patient safety.mp. 

7 1 or 2 or 3 or 4 or 5 or 6 

8 exp Organizational Climate/ or organizational culture.mp. 

9 organizational climate.mp. 

10 exp Teams/ or interprofessional relations.mp. 

11 exp LEADERSHIP QUALITIES/ or exp LEADERSHIP STYLE/ 

12 exp Conflict Resolution/ or exp Leadership Style/ 

13 8 or 9 or 10 or 11 or 12 

14 7 and 13 

15 limit 14 to human 
202

Campbell Collaboration Database

Social, Psychological, Educational, and Criminological Trials 

Register (C2-SPECTR)

Database: Campbell Collaboration

--------------------------------------------------------------------------------

1 patient and safety 

2 safety

3 medical and error

4 error

5 error and safety

6 working and conditions

7 quality and health and care

8 medication and errors

9 diagnostic and errors

10 hospital and mortality

11 iatrogenic and disease

12 quality and assurance

13 treatment and outcome

14 job and performance

15 employee and productivity

16 person and environment and fit

17 human and performance

18 productivity

Note: We searched in the keywords and non-indexed fields.
Appendix C: Search Results

203

Workforce

Staffing

Workflow Design Personal/Social Physical 

Environment

Organizational

Factors

Level 1 

Titles/abstracts 

captured in 

database 

searches

Titles/abstracts 

selected from 

databases

Additions from 

other sources

216 62 207 91

378 335 209 116

115

345

Level 2 

Full text 

articles 

reviewed

Effect of Healthcare Working Conditions on Patient Safety

Search and Selection of Citations

MEDLINE (HealthSTAR) PsycINFO

* Campbell Collaboration database

CINAHL

EBSCO

379

CC*

332

Level 3 

Full text 

articles 

included

33 119 7 2 25

56 16 11 13 19

6,517

+

5,758 3835 3543 2815

+ + + +

14 0

+

69 14

0

0
204

Appendix D: Data Abstraction Guidelines

Source

Author(s):

Editor(s): 

Year:

Title:

Journal:

Volume (Issue) or Edition:

Publisher (Place):

Page(s):

Exclusion Code at the Paper Review Level (if applicable): 

Study Design

Purpose of Study: 

Industrial/Healthcare Setting:

Comparison Groups:

Working Conditions Measured:

Length of Followup:

Interventions:

Co-Interventions:

Blinding:
205

Sample/Population

Population: 

Inclusion Criteria:

Sample Characteristics:

Groups/n:

Baseline Differences in Groups:

Non-respondents:

Exclusions:

Intervention

Description:

Procedure:

Duration of Intervention:

Measures/Analysis

Baseline Measures:

Outcome Measures:

Confounders:

Analysis Methods:

Outcome

Relationship of outcome to patient safety: 

 

Strength of association between working condition and outcome: 
206

Overall findings (Check one):

_____ Working condition affects patient safety.

_____ Working condition does not affect patient safety.

Comments:

Quality

Study Design (Check as appropriate):

Randomized Controlled Trial

Cohort Study

Case Control Study

Cross-sectional Study

Case Series

Survey/Self-report

Pre-test/Post-test

Other

Comments:

Internal Validity: 

Comparability of Groups:

Differential Loss to Follow Up:

Measurement/Instrumentation Issues: 

Maturation/Pre-testing Effects: 

Clear Description of Interventions: 

Other Issues: 

External Validity (Check as appropriate):

Sensitized or Pre-tested Population

Specialized/Atypical Population

Selection Biases (non-random subject selection)

Reactive Effects of Experimental Settings

Multiple Interventions

Comments: