Chapter 47. Patient Safety and Health Information 

Technology: Role of the Electronic Health Record 

Nancy Staggers, Charlene Weir, and Shobha Phansalkar 

Background 

An electronic health record (EHR) is a real-time, point-of-care, patient-centric information 

resource for clinicians1 that represents a major domain of health information technology (HIT). 

More recently, an EHR has been defined as “a longitudinal electronic record of patient health 

information, produced by encounters in one or more care settings.”2

 It includes patient 

information such as a problem list, orders, medications, vital signs, past medical history, notes, 

laboratory results, and radiology reports, among other things. The EHR generates a complete 

record of a clinical patient encounter or episode of care and underpins care-related activities such 

as decisionmaking, quality management, and clinical reporting. Some distinguish between the 

terms EHR and electronic medical record (EMR), with EMR focusing on ambulatory care 

systems. However, in practice, the terms are interchangeable. In this chapter, the term EHR 

relates to computerized patient health records stored within and among institutions. 

This chapter first presents a review of the literature about orders management—also called 

computerized provider (or physician or practitioner) order entry. The next section addresses 

barcoding, an area closely related to orders management. Third, the chapter synthesizes the 

literature about the impact of orders-related clinical decision-support systems on nursing practice. 

Ordering and associated functions in EHRs is a salient focus for several reasons. First, EHRs 

are a current centerpiece in contemporary health informatics. A nationwide emphasis exists to 

install these clinical systems over the next decade, largely because of a 2004 statement by 

President Bush that most Americans would have an EHR in 10 years.3

 Second, the benefits of 

EHRs are becoming more well known. For instance, a 2006 systematic review of the literature 

concluded that overall use of HIT increased adherence to guidelines for care, increased 

surveillance and monitoring of patients, yet had mixed effects for medication errors and time 

utilization.4

 Third, nurses in the United States are now or will be using EHRs in the near future. 

Understanding orders management through the EHR is imperative because the effect on nursing 

practice promises to be great. 

Orders management is an interdisciplinary activity crossing organizational boundaries; 

therefore, the literature review for this topic was broad, including all care settings and providers 

other than nurses. The ordering process inherently involves nurses, especially in acute care 

settings, as recipients of medical orders and initiators of nursing orders. However, this 

relationship may not be acknowledged in the design of empirical studies. 

The Institute of Medicine (IOM) recommended computerized orders and decision-support 

applications as main HIT mechanisms for increasing patient safety in the future.5

 Existing 

research about the nursing impact of orders, barcoding, and decision support within EHRs needs 

to be examined and then expanded in near-future research. This chapter reviews EHR ordering 

and the associated, more researched areas, and suggests EHR areas for future research. The 

authors chose to concentrate this section on information-intense versus technology-focused 

impacts. 

1 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Orders Management in EHRs 

Orders for patients are the connective tissue in any EHR. They are necessarily complex, 

integrating patient-specific interventions across departments. Orders are written by members of 

the health care team, primarily physicians and nurses. Orders management crosses customary 

boundaries, and it is just as likely to integrate computerized applications and functions as it is to 

disintegrate traditions. For example, information once the purview of one department becomes 

shared across many disciplines. Who owns data, such as a patient’s allergies or weight, becomes 

a topic of vigorous discussion. New work processes are crafted. Because of the complexity of 

orders management, computerized provider (physician) order entry (CPOE) has been a topic of 

research. 

The genesis for the recent increase in publications in this area was the IOM’s To Err Is 

Human: Building a Safer Health System report on errors in medicine. The IOM recommended 

information technology as a major mechanism to reduce errors.6 Likewise, the IOM’s Crossing 

the Quality Chasm: A New Health System for the 21st Century5 had a profound message for the 

information technology community and recommended, among other things, the installation of 

CPOE and decision support to improve patient safety. In its most recent publication, Preventing 

Medication Errors: Quality Chasm Series,

7

 the IOM recommends that clinicians make greater 

use of information technology for prescribing and dispensing medications. Thus, it is imperative 

to understand the practice impacts of CPOE and its related functions of barcoding and clinical 

decision support. 

Research Evidence—CPOE 

The CPOE studies were analyzed using a quality instrument specific to informatics called 

QUASII.8 This instrument assesses informatics study qualities across construct, internal, 

external, and statistical conclusion validity areas. The CPOE studies may be divided by QUASII 

scores into two tiers: Tier 1 (with QUASII scores at or above 61) and Tier 2 (with scores at or 

below 54). No scores between 54 and 61 were observed, and studies with QUASII scores below 

30 were excluded from consideration. Tier 1 includes studies that have more reported rigor in 

study design and controls with fewer possible threats to construct, internal, external, and 

statistical conclusion validity. Tier 2 includes studies with less reported rigor and increased 

possible threats to validity. Studies with less rigor are included here because they are often 

widely cited and have even dominated the literature. 

 The studies were sorted by dependent variables into medication errors, efficiency impacts 

(time and length of stay), and quality care. The studies are summarized in Tables 1 and 2. In 

Table 3 a sample of qualitative studies is listed to show the contrast in the types of variables 

examined in these studies compared to quantitative CPOE studies. 

Varying Definitions of CPOE 

The term CPOE is used imprecisely. Researchers have used the same term to mean orders 

with these differing capabilities: 

2 
 Patient Safety & Health Information Technology

 

• Electronic orders, including electronic transmission to appropriate ancillary departments9

• Electronic orders without an interface to one or more ancillary departments, requiring 

either order transcription to paper or order entry by others into systems with different 

functional capabilities10–12

• Orders including order sets13 

• Orders with no order sets14 

• Orders without capability for complex medications, such as intravenous (IV) orders, 

total parenteral nutrition (TPN), or complex functions such as oncology protocols 

• Orders with integrated alerts, reminders, and decision support to assure order 

completeness and accuracy, especially for medications15 

• Orders with no checks, alerts, reminders, or decision-support capabilities16, 17 

• Orders without a pharmacy interface or any decision-support capabilities18

• Orders with or without associated clinical documentation 

• Orders with full capabilities, complete decision support, documentation (especially an 

electronic medication administration record, or eMAR), and complete support for all 

orders, including complex protocols 

• No description of existing capabilities 

Researchers report conclusions as if the CPOE capabilities were equivalent, when these 

varying instantiations, in effect, amount to very different strengths of CPOE as an independent 

variable. In particular, the lack of appropriate departmental interfaces and integration in one 

study, such as a pharmacy interface in a study tracking medication error rates, is very much in 

contrast to a study examining medication errors using a system with an existing pharmacy 

interface. CPOE requiring order transcription of any kind—to a medication administration record 

(MAR) or foreign pharmacy system—necessarily increases errors, and these very transcription 

errors are typically included in the count of overall medication errors rates. 

The same notion can be applied to the presence or absence of computerized decision support 

in its most basic form. Basic decision support can allow checking for order completeness and 

accuracy. If medications errors are being examined, a CPOE study of an application with no 

basic order checking is not equivalent to studying one with any decision support integrated into 

CPOE. More advanced decision support for drug-drug or drug-allergy interactions, and checks 

for other interactions or dosing accuracy, add yet another level to CPOE applications. 

Researchers are led to conflicting conclusions if this variability of functions is not taken into 

consideration. At best, the broad scope of CPOE systems (and lack of specific descriptions in the 

studies of the features of systems) leads to confusion in the interpretation of results. These 

differing capabilities are noted when reported by researchers. 

CPOE Impacts and Variables Studied: Quantitative Studies 

Sites and CPOE applications. CPOE evaluations have been concentrated at large academic 

medical centers, particularly at Brigham & Women’s Hospital in Boston and the Ohio State 

University medical center in Columbus. The unique, “homegrown” systems at Brigham, 

Vanderbilt University in Nashville, and the Regenstrief Institute at the Indiana University School 

of Medicine (Indianapolis) populated earlier literature—although, more recently, vendor systems 

have been studied. Studies of vendor systems include the Siemens, Eclipsys, General Electric 

(GE), and Cerner CPOE applications, in descending order of frequency. 

3 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Medication errors and adverse drug events. The relationship of CPOE to medication 

errors, adverse drug events (ADEs), and subsets of those categories is reported in 12 studies (see 

Table 1). Of these, three systematic reviews addressed the effect of CPOE on medication errors 

and/or ADEs. The systematic reviews concluded that CPOE (and isolated clinical decisionsupport systems) can reduce medication errors.19–21 However, since these reviews were 

published, researchers have published conflicting conclusions about the topic.22, 23 

Studies on inpatient units in five different settings reported significant decreases in 

medication errors after CPOE implementation.11 More specifically, all medication errors and 

non-missed-dose errors decreased in two sites,10, 18 and potential and nonintercepted ADEs 

significantly decreased with the homegrown application at Brigham and Women’s Hospital.15 

Shulman and colleagues11 reported a lower proportion of medication errors with CPOE, and 

King and colleagues18 reported a 40 percent reduction in errors. Transcription errors were 

reduced or entirely eliminated.17, 20, 24 

In contrast, two researchers found no differences in rates for ADEs with CPOE.16, 18 In the 

outpatient arena, no differences were found in total errors, ADEs, or rules violations,16 although 

the system in this one setting lacked any order checking for completeness or accuracy and lacked 

basic decision support. The differences in ADE detection may be due to underpowered studies19

and also to the differences in functionality discussed earlier and the differing definitions for 

ADEs. Moreover, studies used varying scales to rate errors and ADEs ranging from selfdeveloped categorical scales of minor/major/serious to the American System of Health-Systems 

Pharmacists classification. 

Increases in medication error rates after implementation of CPOE have also been reported. 

In 2005, Spencer and colleagues12 reported an increase in one type of error (i.e., pharmacy 

processing) on two inpatient medicine units, while other medication errors were unchanged from 

pre-CPOE implementation. Unfortunately, this site had no system interface to pharmacy, and the 

researchers acknowledge that the increased error rate may have been related to the need to 

transcribe orders in the pharmacy. Without the interface or a method to ensure orders were 

complete and accurate, the only seeming advantage of CPOE at this site was the speed of 

communication to the pharmacy for transcription. Researchers in Portugal17 concluded that 

CPOE eliminated their transcription errors, but other errors continued—such as right class/wrong 

drug, and other errors likely solvable by the basic decision support they lacked (e.g., unclear 

orders, missing frequency, incorrect dosages, drug interactions, and duplicative therapies). 

Life-threatening errors and serious ADEs were higher in the early years at Brigham and 

Women’s, when no decision support was installed. For example, a screen for potassium orders 

allowed new, potentially very serious errors to occur.15 These potential errors were intercepted 

by either nursing or pharmacy before the drugs were administered. Likewise, CPOE at one 

institution in London created three major errors that could have resulted in harm or death of a 

patient had they not been intercepted.11 Again, this site had no decision support in place to 

prevent a reported error of 7 mg/kg of morphine being ordered instead of 7 mg, a potential 

overdose of 70 times the normal range. This site also saw an increase in minor, nonintercepted 

errors with CPOE, from 43 for handwritten orders to 93 with CPOE. That said, with all errors 

combined, the overall rate of errors was lower with CPOE. However, the details behind that 

overall rate show increases in potential major errors if decision-support capabilities are not 

available. This statement is in contrast to the conclusion by Chaudhry and colleagues,4

 perhaps 

4 
 Patient Safety & Health Information Technology

 

because his literature review concentrated on the years before these newer studies were 

published. 

In all studies, the researchers did not include external forces, contextual variables, or 

organizational forces that may have contributed to changes in medication errors. For example, 

several studies extended over multiple years, through changes in chief information officers, 

national changes about patient safety, and increased emphasis on medication errors. Especially 

with the more recent studies, changes in error rates could have been due to these factors as well 

as information technology implementations. 

The variability in conclusions may also be explained by the differences in available 

functions, particularly the lack of pharmacy interfaces and basic decision support. Therefore, the 

previous researchers’ conclusions about CPOE decreasing medication errors must be modified: 

CPOE can reduce medication errors if appropriate functions are available to prevent new errors. 

Transcription errors can be eliminated with electronic communication and interfaces together 

with structured order entry. CPOE can substantially reduce overall (and many serious) 

medication errors if (1) electronic communication and automatic order interfaces are in place, (2) 

basic order checks for completeness are present, and (3) decision support at its most basic level 

is available—checking for drug–drug and drug–allergy interactions and for dosing ranges. 

Clinical efficiency measures—time and length of stay. Eleven studies examined the 

effects of CPOE on efficiency measures of time and/or hospital length of stay (LOS) (see Table 

2). CPOE offers clear benefits in processing efficiency for orders management and availability of 

electronic laboratory and radiology results. CPOE reduced the time from order entry to results 

availability for laboratory and radiology orders in four 

sites.13, 25–27 Another clear benefit is that CPOE decreased the time from pharmacy ordering to 

medication administration time.13, 25, 26, 28 

Likely because of timely availability of results and faster order processing, patients’ hospital 

LOS was shorter.26, 29 One systematic review concluded that one of the benefits of CPOE is 

reduced LOS.20 Other clinical efficiencies are related to use of CPOE. For example, Ohio State 

University hospital saw a significant improvement in the number of patients whose abnormal 

potassium levels were normalized within 24 hours.24

On the other hand, order entry itself takes longer using CPOE than with paper. A systematic 

review of the impact of computers on time efficiency concluded that the use of central desktops 

for CPOE was not efficient, consuming 98.1 percent to 328.6 percent more time per working 

shift.30 CPOE took 2.2 minutes longer per patient, but after duplicative tasks were removed, the 

extra time per patient was shortened to an average of 0.43 min.31 At Brigham and Women’s, 

CPOE took 44–73 minutes longer per day, especially for entering one-time orders.32 However, 

this study was done before order sets were widely used. At Regenstrief, an early CPOE 

application took interns 33 minutes longer during a 10-hour period.29 Interns entered orders on 

microcomputers and then printed them, using them as traditional paper documents afterwards. 

Likewise, a more recent study at Massachusetts General Hospital33 demonstrated an increase in 

medical interns’ ordering time, among other time-related variables. Prior to CPOE, interns spent 

2.1 percent of their time ordering; after CPOE, they spent 9 percent of their time ordering. Two 

of these studies were published about early CPOE applications in the 1990s, and all four 

measured homegrown systems. None of the studies examined time for order sets or vendor-based 

solutions. Of note, CPOE may take providers somewhat longer to enter orders, but efficiencies 

are obtained later in the orders management cycle—in nursing, ancillary departments’ order 

processing, and in reduced time for results availability and administrative tasks.33 

5 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Quality care variables. Three studies examined variables not reported by others, namely 

the quality of documentation and one particular patient outcome (see Table 3). In one study, a 

significant increase occurred in the number of documented consents for do-not-resuscitate 

orders.34 In another study, researchers conducted a randomized controlled trial to examine the 

effect of medical students’ rotations in a CPOE site versus traditional sites on the quality of 

orders written on a fictitious patient50 They found that the quality scores for orders during an 

academic examination were significantly higher for students using CPOE.

A third study produced alarming results that conflict with other, more promising benefits of 

CPOE. A recent article reported an increased mortality coincident to a CPOE implementation at 

a pediatric hospital.14 The researchers reported a direct association between CPOE and increased 

mortality among pediatric patients admitted through interfacility transport. However, this facility 

experienced substantial workflow changes in conjunction with CPOE installation. For example, 

no preregistration was available, delaying order entry until full registration was completed after 

the patient physically arrived. This process change delayed therapies and diagnostic testing. 

Important human–computer interaction issues impacted treatment times. The new ordering 

system required substantially more order entry time during a critical period of patient care, and 

the wireless bandwidth capacity was often exceeded during peak periods. Crucial aspects of 

work organization changed with all medications being centralized, meaning that nurses were 

unable to access medications locally and the pharmacy could not process medication orders until 

they had been activated. Sadly, when the pharmacy accessed CPOE to process an order, other 

clinicians were locked out of the application. The researchers also reported a decrease in face-toface communications that provided relevant information for patient care management postCPOE. 

In this study, CPOE was likely a proxy variable for significant, but untoward, process 

changes in that particular institution. The lesson from this article is that work processes must be 

thoroughly examined before CPOE “goes live,” and projected, substantial treatment delays are 

an excellent reason to delay going live until work design is safe for patient care. For critically ill 

patients in emergency departments (EDs), intensive care units (ICUs), and pediatric units, new 

processes cannot delay treatment. Workflow and usability analyses can preclude the kinds of 

impacts seen in this article. 

CPOE and a Sampling of Qualitative Studies 

A sampling of qualitative studies shows a contrast in variables addressed by these 

researchers versus the researchers of quantitative studies (see Table 4). While not usual to 

include as evidence, these qualitative studies provide insights for future studies and as well as 

interesting aspects of CPOE. Koppel and colleagues23 interviewed 261 clinicians, including 

nurses, about CPOE and its perceived role in medication errors. Clinicians reported new errors 

with CPOE because of fragmented data and processes, lack of integration among systems, and 

human–computer interaction issues. For example, obtaining a summary view of all the 

medications a patient was receiving was difficult because providers had to scroll through 

multiple screens to view medications. In another example, the fit between computer and 

workflow processes was a problem because nurses typically charted medications at the end of a 

shift using global commands instead of charting at the time medications were actually given. 

And in another study of CPOE, Sittig and colleagues35 found that negative emotions about CPOE 

prevailed for both nurses and physicians. 

6 
 Patient Safety & Health Information Technology

 

 One study concluded that communication was broadly affected by CPOE, from 

interpersonal to intrainstitutional.36 Significant impacts on team and physician-nurse 

communication occurred with CPOE. Nurses felt that CPOE degraded communication among 

the dyad of doctor and nurse, and thought that it took more effort post-CPOE to get residents to 

come see patients needing attention. A multisite study of the Veterans Affairs’ (VA’s) early 

CPOE application showed that nurses thought the quality of care had improved with CPOE, but 

the control over their jobs and roles decreased.37 

 Quantitative studies examined more easily definable and, perhaps, more simplistic variables, 

such as measurable medication errors and ADEs, timed processes of order entry to results posted, 

and mortality. The qualitative studies, on the other hand, examined richer aspects of processes 

and interdependent variables, such as types of errors created by CPOE, interdependent 

communication patterns, and perceptions of role changes. With the complexity created by orders 

management, both methods are needed in the future research. 

Evidence-Based Practice Implications 

Silence about nursing impacts cuts across the CPOE quantitative studies. Only one 

quantitative study mentions nursing impacts with CPOE: changes associated with CPOE resulted 

in medications not being available on patient units in a timely manner, resulting in missed 

doses.10 Bates and colleagues acknowledged the increase in missed doses but deemed these 

minor, and the missed-dose errors were excluded from summary findings of the study. 

Impacts of CPOE on nursing were examined in the sample of qualitative studies. The sample 

of qualitative studies indicated that (1) nurses at three sites in one study had negative emotional 

perceptions of CPOE, (2) interpersonal communication between nurses and physicians was 

disrupted by CPOE, and (3) nurses perceived that the quality of care improved with CPOE. 

Even with few studies specifically addressing nursing issues, implications are evident. 

Nurses can expect improved speed for results availability with CPOE. The elapsed time from 

writing an order to available results is a clear and expected benefit. Although not surprising, this 

is a benefit to the care team and the patient. Obviously, legibility of orders and improved 

availability of information occurs as well, by virtue of orders being typed and available 

electronically. Nurses can expect more efficient treatment related to results availability and 

decreases in hospital LOS for patients post-CPOE. 

Whether medication errors and ADEs are impacted by CPOE depends upon available 

application functionality. Thus, all nurses will want to be aware of available functional and 

technical support and their implications. If, for example, the CPOE system has no pharmacy 

interface or integrated decision-support capabilities, aggressive monitoring systems will need to 

be in place to intercept medication errors ranging from transcription errors and interaction issues 

(drug-drug, drug-allergy interactions) to dosage issues (dose range, right class/wrong drug, 

frequency) and more serious errors. If no eMAR exists, then errors associated with transcription 

will be present because transcription to a paper medication administration record is required. 

Serious medication errors can increase with CPOE if no decision support is available. As 

functionality increases with computerized applications and electronic transmission, providerbased error-monitoring mechanisms can be tailored down in scope. Medication errors can 

decrease if interfaces and appropriate documentation using an eMAR are available, along with 

decision-support capabilities for order accuracy, dosing issues, and interaction checking. In fact, 

7 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

given the implications of CPOE without interfaces and decision support, nurses should actively 

support a CPOE installation only if adequate functionality will be installed. 

That said, no health information technology is a panacea. When lower-level errors are 

solved, such as results availability and transcription errors, a new level of issues will emerge, 

some beneficial and some prompting concern. CPOE creates professional interdependence and 

slices across departmental boundaries. Thus, changes in work design, roles, and communication 

will occur. Work processes need to be carefully analyzed for potential detrimental changes 

before going live, and either the work design or the EHR design must be tailored for patient 

safety and quality. Nurses can expect to feel the impact of more electronic and less face-to-face 

communication, especially from physicians. Knowing this, alternative communication channels 

and opportunities can be constructed. Roles will need to be renegotiated among medicine, 

pharmacy, and nursing for order activation, allergy entry, weight documentation, and other 

interdependent issues. 

Research Implications 

Study descriptions and designs. Identifying and specifying the capabilities of CPOE is 

imperative. Future studies should indicate the exact functions in the article and abstract as well, 

and in the title, if at all possible. Chaudhry and colleagues.4 noted this same issue with general 

HIT studies. Careful conclusions are necessary when CPOE does not provide basic functionality 

such as pharmacy, laboratory, or eMAR interfaces. CPOE capabilities are not equivalent, so they 

should be treated like the different independent variables they are. Adequate descriptions of these 

study characteristics are needed, at minimum. 

Broadening the definition of CPOE would allow researchers to conceptualize future studies 

differently. The term “order entry” misrepresents the concept by implying that only the entry 

portion of the whole process is important. Ordering is a process starting with entry, to 

communication, to processing by various recipients, and then to documenting actions against 

specific orders. By conceptualizing ordering in this way, future studies can be designed to 

measure impacts across the health team. 

Potential external influences need to be taken into account in study design as potential 

confounding variables. Studying CPOE in a natural environment is challenging research. 

However, rather than ignore these variables, as has been done in the past, future researchers 

should want to identify and control, or at least measure, these variables. This notion is stressed 

by Snyder and colleagues.38 External forces outside the institution should also be considered in 

study conclusions—for example, the influence of national trends for increasing patient safety 

with concomitant information technology installations. 

Future research themes. Three major themes for future research emerged: (1) nursing 

impacts from computerized orders management, (2) human-computer interaction issues, and (3) 

implementation science. The concept of CPOE needs to be expanded to encompass an orders 

management cycle. To date, the concept has been studied primarily as order entry in quantitative 

studies. The ordering process is a complex, interdependent, and interactive process composed of 

at least these multiple, intersecting elements: systems design, interpersonal and intersystems 

communication, implementation processes, and organizational structures. Thus, orders 

management needs to be examined in the future as the interdependent, interdisciplinary, and 

interactive process that it is. A few authors of qualitative studies have started that process. 

8 
 Patient Safety & Health Information Technology

 

9 

Because orders management is a complex process, identifying only simple outcomes 

variables does not do the phenomenon justice. Multiphased studies with multiple process and 

outcome variables are needed to begin to understand orders management and its impact. 

Nursing impacts of computerized orders management. From the nursing perspective, 

nearly any study of orders management with nursing impacts will be novel. Ideally, an 

interdisciplinary study of orders management should be crafted using both quantitative and 

qualitative methods. Crucial variables include the impact on workflow, cognitive processes for 

information synthesis across disparate systems, and patient safety issues with various vendors’ 

CPOE applications. A standardized method for medication error reporting is needed to facilitate 

reporting across institutions and vendor applications. In concert with recommendations from 

Kaushal, Shojania, and Bates,19 commercial products should be compared and key 

implementation factors identified. Mixed methods in future studies are very desirable since 

quantitative and qualitative methods would provide a powerful mechanism to uncover 

information about orders management as a complex process. 

Human–computer interaction issues. A second theme of future research relates to usability 

and human–computer interaction impacts of orders management and clinical decisionmaking. 

Human-computer interaction within EHRs is a critical area to explore in HIT. For at least a 

decade, health informatics experts have stated that user interface design and other related areas 

of human–computer interaction are understudied and, in fact, an area in desperate need of 

attention.39, 40 Yet, research in this area has moved at a glacial speed. In some recent literature, 

serious user interface issues have surfaced related to CPOE.14, 41 In particular, the rigid, linear, 

structured computing processes reflected in user interfaces did not adequately address clinicians’ 

work processes, which are nonlinear, interruptive, and flexible. These findings accentuate the 

need for research in user interface design. 

Research in human–computer interaction is beginning in health informatics, but more is 

immediately needed. Two researchers outlined detrimental effects of the usability aspects of 

order applications.14, 42 Patel40 studied issues surrounding physicians’ cognitive structures and 

stressed the importance of cognitive science to informatics. Ash and colleagues examined 

aspects of CPOE, such as unintended consequences of CPOE and other human-systems 

issues.41, 43, 44 Staggers45–48 studied effective screen designs as they related to the efficiency 

(response time) and effectiveness (accuracy) of various EHR designs. Future research needs to 

focus on systems usability to understand what designs facilitate safer orders management; what 

vendors offer safe, usable, and accurate orders management applications; how vendor 

applications compare in efficient and effective designs for interdisciplinary applications, such as 

orders management; what designs facilitate effective clinical decisionmaking; and what work 

design needs to be in place for successful implementation of CPOE. 

Implementation science. Clinical systems implementation in health settings should be a 

third focus of future research. Anecdotal guidelines exist for systems implementation, but little 

evidence is available to guide institutions across the nation as they implement EHRs. As of late 

2005, only about 20 percent of U.S. institutions had installed EHRs, HIMSS Analytics reported 

only 3 percent of institutions had CPOE by 2007 and none had a full EHR; therefore, research 

into the science of implementation can be of benefit in the future.49 At the very least, we should 

uncover factors crucial for implementation success in health settings, especially from the 

organization and system design perspective. Funding should be made available for 

implementation studies outside academic medical centers and urban areas.
Evidence Table 1. CPOE Effects on Medication Errors 

Source Setting CPOE Study Design Study Intervention Key Finding(s) Quality Score Bates 199815 Brigham & Women’s (medical and surgical units) Homegrown Pretest, post-test CPOE plus team effect on nonintercepted, potential, and all adverse drug events (ADEs). Nonintercepted serious ADEs decreased by 55%. Preventable ADEs declined 17% (not significant). Nonintercepted potential ADEs decreased 84%. CPOE plus team offer no additional benefit over CPOE only. No differences seen for all ADEs. 48 Bates 199910 Brigham & Women’s (3 medical units) Homegrown: Transcription to a paper medication administration record required Time series (1 pretest and 3 post- tests over 4 years) Medication errors for pre- and post- CPOE x 3. All errors decreased. Nonmissed dose errors decreased by 81%. Nonintercepted serious errors fell 86%. Nursing workflow impacted negatively and missed-dose errors increased. 43 Cordero 200425 Ohio State Univ. neonatal ICU

Inpatient 

Vendor (Siemens) 

Pretest, post-test with controls 

CPOE effect on accuracy of Gentamicin doses. 

Reduced medication errors for selected NICU drugs. 

77 

Gandhi 200516 Brigham & Women’s 4 

outpatient clinics 

Homegrown: CPOE without checks or decision support 

Cross-sectional Prescribing errors, 

potential ADEs, and rule violations. 

No differences in total errors, ADEs, or rules. 

74 

10 
 

11

Source Setting CPOE Study Design Study 

Intervention 

Key Finding(s) Quality 

Score 

Kaushal 200319

N/A Various forms Systematic review Effect of CPOE 

and clinical decision support on medication safety. 

CPOE and isolated clinical decisionsupport systems (CDSS) can reduce medication errors. Studies underpowered to detect differences in ADEs and have studied “homegrown” systems. 

N/A 

King 200318 Children’s 

Hospital, Toronto, Inpatient 

Vendor (Eclipsys). No interface with pharmacy. No decision support. 

Retrospective cohort study over 6 years 

Rate of medication errors on 2 CPOE medical units versus 1 medical and 2 surgical units over 3 years postimplementation. 

Medication errors were 40% lower on CPOE units, but no difference for ADEs. 

90 

Kuperman 2003 20

N/A Various forms Systematic review CPOE impact on 

LOS and medication errors. 

CPOE reduces medication errors (incorrect dosing, interactions), transcription errors. 

N/A 

Mirco 200517 Portugal, internal 

medicine units, institution not named 

Application not stated. Unit dose available, but no decision support or interaction checking. 

Pretest, post-test, no controls reported 

CPOE effect on medication errors. 

CPOE eliminates transcription and patient identification errors. Errors were right class/wrong drug and unclear orders. Smaller % of errors due to frequency, incorrect dose, drug interaction, duplicative therapy, length of therapy. 

37 

Papshev 200121

Electronic prescribing in ambulatory practice 

N/A Systematic review Electronic 

prescribing (including CPOE) effect on medication errors. 

Reduces medication errors. 

N/A 
12 

Source Setting CPOE Study Design Study 

Intervention 

Key Finding(s) Quality 

Score 

Potts 20049 Vanderbilt Homegrown 

“Wiz” order system 

Pre/post with controls 

CPOE effect on potential ADEs, medication prescribing errors (MPE), and rules violations (RV). 

Reduced ADEs, MPEs, and RVs. 

60 

Shulman 200511 Univ. College in London, ICUs Vendor (GE systems) Time series (1 pre and 4 post over 37 

weeks) 

CPOE without decision support and handwritten orders effect on medication errors and type of error. 

Lower proportion of errors with CPOE. Reduced major/moderate outcomes for nonintercepted and intercepted errors combined. Two errors with CPOE resulted in patient harm. Increase in minor intercepted errors with CPOE (43 versus 93). 

44 

Spencer 200512

U. North Carolina, Chapel Hill, 2 medicine units 

Vendor (Siemens). No pharmacy interface. 

Pretest, post-test with controls 

Effect of CPOE without decision support and handwritten orders on medication error rates. 

Increase in reported errors for pharmacy processing. Other errors unchanged. Errors include drug allergy, duplicate orders. 

50 
 

Evidence Table 2. Computerized Physician Order Entry (CPOE) Effects on Process Efficiency (Time and Length of Source Setting CPOE Study Design Study Intervention Key Finding(s) Quality 

Score 

Bates 199432 Brigham & Women’s 

(medicine and surgery house staff) 

Homegrown Pretest, post-test 

with no reported controls 

Pre/post CPOE medicine and surgery house staff time spent in order entry activities. 

CPOE takes more time (44–73 min/day), especially for one-time orders. 

50 

Cordero 200425

Ohio State Univ. neonatal intensive care unit (NICU), inpatient 

Vendor (Siemens) 

Pretest, post-test with controls 

CPOE effect on radiology result times for abdominal, chest films, medication turnaround times for caffeine loading doses, accuracy of Gentamicin doses. 

Reduced medication and radiology turnaround times, medication errors for selected NICU drugs. 

77 

Kuperman 200320

N/A Various Systematic review CPOE impact on 

length of stay (LOS). 

CPOE reduces LOS. N/A 

Lehman 200128

Rush Presbyterian 

Vendor (Siemens). No pharmacy interface. 

Pretest, post-test with no controls 

CPOE impact on pharmacy order turnaround time (order to medication delivery on unit). 

Over 60% faster with CPOE. 

74 

Mekhjian 200226

Ohio State Univ, transplant unit, medical intensive care unit, and surgical ICUs 

Vendor (Siemens). CPOE plus eMAR. 

Pretest, post-test with no controls 

CPOE impact on medication turnaround times, radiology procedures, lab results. 

Reductions in medication turnaround times, transcription errors. More timely results reporting for radiology and lab. Severity adjusted LOS decreased in 1 hospital but not another. 

65 

Ostbye 199727

Univ. Western Ontario 

Vendor (Siemens from Norway) 

Quasiexperimental parallel comparison, descriptive 

CPOE effects on results reporting on 2 similar surgical units. 

Average time to complete and transmit lab tests decreased from 7 to 1.5 minutes. Results availability decreased by about 3 hours. 

76 

13
14 

Source Setting CPOE Study Design Study Intervention Key Finding(s) Quality 

Score 

Overhage 200131 Regenstrief Institute Homegrown. Used in 11 

primary care clinics. 

Randomized controlled trial. Time-motion study. 

Time for CPOE compared to paper. Perceptions about order entry. 

CPOE is 2.2 min per patient longer, but when duplicative tasks are removed, only 0.43 min per patient longer. Perceptions that work is done faster, quality of care and documentation is improved. 

108 

Papshev 200121

N/A N/A Systematic review Electronic prescribing 

(including CPOE) effect on time 

Can eliminate the time gap between point of care and point of service. 

N/A 

Shu 200133 Massachusetts 

General Hospital 

Homegrown Pre/post with 

pager reminders for time recording 

CPOE impact on physician time. 

Interns spent 9% of their time ordering vs. 2.1% pre-CPOE. Counterbalanced by less time spent for nursing, pharmacy and quality and efficiency changes. 

54 

Tierney 199329 Regenstrief (Wishard 

Memorial Hospital) 6 inpatient internal medicine services 

Homegrown. Orders printed and not sent electronically to departments. 

Randomized controlled trial with time-motion study 

Effects of orders entered by interns into a computer workstation compared to paper. 

Mean LOS was 0.89 day shorter. CPOE interns spent 33 minutes longer per 10-hour period (5.5 min/pt/day). 

110 

Thompson 200413

St. Paul’s Hospital, Vancouver, 11-bed ICU 

Vendor (Eclipsys) 

Pretest, post-test Effect of CPOE on the 

timeliness of urgent lab and imaging test results. 

Improved test turnaround time for stat lab and radiology orders. 

54 
 

Evidence Table 3: Quality Care Variables Source Setting CPOE Study Design Study 

Intervention 

Key Findings Quality 

Score 

C

Han 200514 Univ. Pittsburgh, 

inpatient pediatrics 

Vendor (Cerner) Pretest, post-test 

with controls 

Effect of CPOE on pediatric mortality rates. 

Mortality rate increased from 2.8% to 6.57%. 

61 N

stpaUmcas

Salmasy & Marx 199734

Urban academic center 

Not stated Pretest, post-test 

with controls (over 4 years) 

CPOE effect on documented consents. 

Increased from 75% to 90%. 

74 S

pIed

Stair & Howell 199550 Georgetown Univ. medical 

students on emergency medicine rotations 

Not stated Randomized 

controlled trial. Students randomly assigned to 4 different locations. 

CPOE effect on quality of orders for an imaginary patient. 

Quality scores for medical students at CPOE sites better than manual. 

94 

15
16 

Evidence Table 4: Qualitative and Descriptive Studies Author(s) Site CPOE Study Design Study 

Intervention 

Key Finding(s) 

Ash 200441 Sites in the 

Netherlands, Australia, and 4 hospitals in the United States 

Various applications 

Qualitative description of unintended errors 

Unintended effects of patient care information systems. 

Unsuitable human–computer interrupted tasks, cognitive ovstructured or complete informfragmentation, overcompletenmisrepresentation of workflowand predictable, inflexibility, rimedication orders, work-aroucommunication & feedback, doverload, and a decrease in rerror catching. 

Dykstra 200236 Univ. of Virginia, El 

Camino Hospital, Puget Sound and American Lake VA Hospitals 

Various systems 

Qualitative CPOE’s role on 

communication patterns. 

Impacts on physician–nurse cwithout a physical presence, ainformation for the care-team increases, “black box” may m

Koppel 200542 Urban tertiary care 

teaching hospital 

Vendor (Eclipsys) 

Qualitative – interviews, focus groups, observations on 261 physicians, nurses, and pharmacy leaders 

CPOE’s role in medication errors. 

CPOE facilitated 22 medicatioto fragmentation of data, lack integration, and human-mach

Sittig 200535 U. Virginia, VA 

Hospital, El Camino hospitals with recent and long-standing CPOE installations 

Various systems 

Qualitative – interviews with 50 people (physicians, physician assistants, and nurse practitioners) 

Emotional responses to CPOE installations. 

Prevalent negative emotions. CPOE design (irrelevant alertfocusing making “the right thindo”). 

Weir 199537 VA Homegrown Descriptive survey Nurses’ 

perceptions of work, quality of care, and physician–nurse communication. 

Positive impact on the quality control. 
Patient Safety & Health Information Technology 

Barcode Medication Administration in EHRs 

Background 

To Err Is Human focused attention on the frequency of medical errors occurring in U.S. 

hospitals.6

 In response, the health care industry has been counting upon the strengths of 

technological innovations to improve patient safety and decrease medical errors. Before the IOM 

report, the Harvard Medical Practice Study revealed that medication errors most frequently 

occurred in hospitals.51 Medication errors can occur at any stage of the medication administration 

process—starting at the ordering of the drug by the physician, followed by dispensing of the drug 

by the pharmacist, and ultimately ending in the actual administration of the drug by the nurse to 

the patient. However, a 1995 study showed that 38 percent of potential and preventable ADEs 

occurred at the time of administration by nursing personnel.52 Further evaluation of these errors 

found that wrong dose, followed by wrong route and wrong drug, were the most common 

administration errors.53 

As discussed in the previous section of this chapter, the implementation of CPOE systems is 

targeted to eliminate errors occurring at the ordering phase. On the other hand, barcode 

medication administration (BCMA) systems work toward decreasing errors that arise further into 

the medication administration process. Integration of the two technologies, that is, BCMA 

systems with CPOE systems, can lead to significant improvements in patient safety and 

efficiency of medication administration. 

National organizations leading the patient safety efforts have recognized the improvements 

brought about by the implementation of barcode technology in hospitals. The IOM, National 

Patient Safety Foundation, and the American Society of Health-System Pharmacists have 

recommended the implementation of BCMA systems as a means for improving patient safety. In 

2004, the U.S. Food and Drug Administration (FDA) mandated the barcoding of all medications 

and blood components to decrease adverse events.54 This rule requires pharmaceutical companies 

to provide a National Drug Code (NDC) on most prescription medications and some over-thecounter medications. Additionally, in compliance with the Joint Commission’s (formerly the 

Joint Commission on Accreditation of Healthcare Organizations, or JCAHO) patient safety 

goals, all hospitals are required to implement barcoding technology for patient identification and 

for matching patients to their medications by 2007.55 

Thus, BCMA systems are strongly associated with efforts to bring about a culture of safety 

in health care. BCMA systems particularly impact the role of nursing in the administration of 

medications at the bedside. In this section, the empirical evidence surrounding the use of BCMA 

systems is evaluated, describing the integral role it has come to play in nursing care, and 

suggesting future directions for research. 

Use of BCMA for Medication Administration 

Barcoding technology has a variety of applications in health care. It has been used 

previously for a broad array of applications, such as transfusion and blood bag matching,56–59

tracking laboratory specimens,60 and inventory control,61–63 etc. However, the application of 

barcoding to medication administration is newer. 

Barcode technology can be used as a stand-alone application or linked to the CPOE or EHR 

system in the hospital. If the BCMA system is not integrated into the EHR, there would be 

17 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

limited capability to have real-time alerts to detect discrepancies of the medications administered 

against the orders entered by the physician or to maintain an accurate documentation of 

medications administered. 

In an integrated EHR environment, there is a seamless flow of information following every 

stage of the medication administration cycle, making it possible for BCMA systems to become 

part of the medication process workflow. Upon admission, every patient receives a barcoded 

wristband. These bands identify patients as they are steered through various tests and procedures 

at the hospital. After examining the patient, the provider enters medications electronically into 

the CPOE system. Following verification, the pharmacist packages unit doses of the ordered 

medications into barcoded containers and sends these to the nursing floor. The barcoding on the 

medication containers has information regarding type of medication, recommended dosage, and 

the frequency of administration. 

The unit doses sent by the pharmacy are stored in a medication cart, which also carries a 

wireless laptop computer and a hand-held scanner. In an integrated EHR environment, the 

barcode scanning is linked to the clinical databases via a wireless network. At the patient’s 

bedside, nurses scan their badges, or log into the BCMA system, scan the patient’s wristband, 

and scan the medication. The BCMA system validates whether the “five rights” of medication 

administration—right patient, right drug, right dose, right frequency, and right route64—match 

the order entered in the CPOE system. If there is a discrepancy, an alert is displayed on the 

computer screen. Once a medication is scanned it is automatically documented in the medication 

administration record (MAR) as having been administered to the patient. In most BCMA 

systems, the nurse has the capability to record missed medications or changes in the time that the 

medication was administered. Thus, the BCMA system not only offers real-time validation at the 

point of care, it can also reduce nurses’ workloads by creating an automatic and accurate log of 

the medications administered to the patient. 

Research Evidence for BCMA 

The research evidence on the use of BCMA systems is limited. The majority of the studies 

reporting outcomes related to implementation of BCMA technology were conducted in Veterans 

Health Administration (VHA) facilities; the VHA is a pioneer in the implementation of barcode 

technology for medication administration. The BCMA system currently used at the VHA is a 

homegrown system that has undergone several modifications. BCMA technology was first 

prototyped at a Topeka, Kansas, facility in 1996. In 1999, the BCMA system was integrated with 

the Computerized Patient Record System (CPRS), which is the CPOE system used at the VHA.65

By 2000, the system and the associated hardware were implemented in 92 percent of the VHA 

inpatient wards. The following section discusses the evidence related to key variables associated 

with BCMA. 

Decrease in medication errors. The most commonly measured variable was the change in 

medication error rate. Four studies described the measurement of this variable: three showed a 

decrease, and one study recorded an increase in medication error rate post-BCMA 

implementation. Coyle and colleagues66 described the implementation of a BCMA system at one 

VHA facility—some of the refinements and upgrades that the system had undergone based on 

nursing recommendations. The changes were made to address specific workflow issues to 

increase acceptance of BCMA in routine practice. A survey administered to the nursing staff 

evaluated the acceptance of BCMA technology after 3 years of implementation. Results showed 

18 
Patient Safety & Health Information Technology 

that 97 percent of the nursing staff agreed that BCMA had decreased the risk of medication 

errors. Additionally, in the first year, the medication errors decreased by 23 percent, and, by the 

fifth year, by 66 percent. Consistent with these results, another VHA study by Johnson and 

colleagues65 compared the overall medication error rates in 1993 and 2001 and found an 86 

percent decrease over this period. Anderson and Wittwer67 conducted a similar study in a nongovernmental setting and found that the medication error rate decreased to less than 50 percent 

of its baseline value within 6 months of implementation of the BCMA system. 

Another study measuring medication error rates was conducted in the medical-surgical units 

of a Midwest government hospital.68 This study examined whether there was a difference in the 

medication error rate 1 year before and after implementation of BCMA. The error rates at the 

administering and dispensing stages were specifically examined. The study showed an 18 

percent increase in the medication error rate. However, this increase was explained by the ability 

of the BCMA system to record any discrepancies in the medication administration, such as late 

or missed doses, which were previously underreported or went undocumented. 

Discrepancies in documentation. The documentation functionality of BCMA systems 

enables the creation of an accurate and complete MAR, which can then become part of the 

patient’s EHR. However, large discrepancies exist in medication documentation, even in an 

electronic environment. Only one study compared the discrepancies in documentation that arise 

upon implementation of a BCMA system. An examination of the discrepancies between MAR 

and patient billing records for large-volume intravenous solutions identified three types of 

discrepancies.69 Failure to document administration to a patient occurred 38 percent of the time, 

the rate of failure to credit the patient for returned solution was 37 percent, and the rate of 

administration of solution to a patient other than for whom it was dispensed was 25 percent. This 

study also examined the potential for BCMA technology to decrease these discrepancies in 

documentation. A 19-percent improvement in the consistency of documentation was observed 

after introducing BCMA technology.

Impact on nursing workflow. A supplementary finding of the study by Barry and 

colleagues69 was that the lowest scanning rates for items were achieved by the nursing personnel, 

largely because BCMA has such a huge impact on the workflow of nurses. In this study, the 

scanning capability was available only at the nursing station, requiring nurses to return to the 

station each time they wanted to scan an item. Lack of consideration of nursing workflow 

processes can result in low rates of adoption of BCMA technology. Another study of the 

medication administration process from the perspective of nurses found ways to make the 

technology less disruptive to nurses’ workflow.70 The researchers described strategies to improve 

acceptance of the technology among nurses and hypothesized that a tangible measurement of this 

acceptance would be seen in the increase in scanning rates. Patient armband scans went up by 7 

percent, and medication label scanning showed a 15-percent increase over a 5-month period. The 

increase in scanning rates was small, but the study lacked a clear description of what the exact 

intervention was, and hence it is difficult to make conclusions about why it failed to have a larger 

impact on the scanning rates.

Using human factors theories to guide an ethnographic evaluation, before and after 

implementation of a BCMA system, the analysis and process-tracing protocols derived five 

negative, unintended effects of introducing this technology.71 The investigators found that 

BCMA technology can lead to the creation of work-arounds that might result in new paths to 

occurrence of ADEs. This study is the first of its kind to conduct an in-depth analysis of the 

19 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

design modifications, organizational policies, and elements of training that need to be in place 

for BCMA technology to fit seamlessly into the nurses’ workflow. 

Thus, the range of variables assessing the impact of BCMA technology on the workflow of 

nurses is broad and complex. An example of a simplistic variable is the measurement of the 

nurses’ acceptance of BCMA technology by number of medication and patient identification 

scans.70 Complex variables surrounding nurses’ workflow have been measured using conceptual 

frameworks derived from the human factors engineering domain, such as recognition-primed 

decisionmaking (RPD), human-automation interaction, workload, authority-responsibility 

double-binds, and mutual awareness among members of the clinical team.71 

Use of BCMA in the outpatient setting. Only one study described the use of BCMA in the 

outpatient pharmacy setting.72 This was a small feasibility study evaluating whether BCMA 

could be used for automatic verification of medications during dispensing. The study suggested 

that manual checks could be replaced by barcode technology to improve pharmacist productivity 

and increase cost savings; however, empirical evidence supporting these conclusions was absent. 

Beyond medication error rates. Unlike CPOE systems, there is a fair amount of uniformity 

when describing a BCMA system. Also, contrary to CPOE interventions—which were 

conducted largely at urban, academic institutions—BCMA studies are primarily conducted in 

VA settings. However, the slow penetration of this technology has resulted in very few 

evaluation studies. In general, there is a paucity of empirical evidence supporting the 

implementation of BCMA systems. The BCMA technology is advocated as an important 

safeguard for reducing ADEs, but sufficient evaluation of how this technology affects the 

dynamics of a complex hospital setting, in ways other than the reduction of medication error rate, 

is lacking. 

Evidence-Based Practice Implications 

The implementation and adoption of BCMA systems is slow, perhaps due to disruptions in 

nurses’ workflow. Ease of use is directly linked to technology adoption. Inpatient environments 

are extremely busy and require technology that can easily adapt to the needs of nurses, enabling 

them to adopt the new technology readily into routine care.73 Coyle and Heinen66 provide a good 

description of how nursing staff are involved in the design and modification of BCMA software 

at the VHA. These recommendations helped the information technology team design software to 

align with workflow needs. Also, there is an effort by vendors to constantly upgrade and update 

the equipment to make it more user-friendly. The VHA environment has reaped the benefits of 

involving its nursing staff in the development process and implementing staff 

recommendations.71, 74 Other hospitals need to consider this strategy to improve acceptance of 

BCMA technology by nurses. The VHA also has a national BCMA development team that is 

entrusted with the responsibility of continuously evaluating and revising the technology.71 

Impact on nursing workflow. One of the efforts recently suggested was the replacement of 

the laptop computer with hand-held devices. A field evaluation of usability of this technique with 

nurses revealed that, while this might be useful for the administration of pain medication and 

hanging IV fluids, it was not ideal for use with medications in general.66 Such determinations 

made from actual field studies serve two purposes. First, they enable nurses to be involved in the 

process of development and deployment, thus fostering ownership of the technology. Second, 

evaluation of the technology in a naturalistic setting can help us understand the far-reaching 

20 
Patient Safety & Health Information Technology 

impact that the introduction of a new technology can have on the workflow patterns—and 

prevent any new threats to safety that might be introduced by the technology itself. 

Changing nurses’ perceptions about technology. Adoption of BCMA technology calls for 

a behavioral change. According to the Technology Acceptance Model,75 such a change can be 

brought about by improving perceptions regarding the system, specifically perception of the ease 

of system use and its usability in routine practice. Tailoring interventions that are geared toward 

understanding nurses’ perceptions will promote adoption. System training should focus on 

educating nurses about how BCMA serves as a safety net and aids them in preventing errors. 

Also, organizational policies that support a transparent environment for the reporting of errors—

rather than a culture of blame—can improve nurses’ perception of BCMA technology. As the 

nursing shortage increases in hospitals across the nation, nurses need to view safety checks, such 

as BCMA technology, as aids rather than impediments in their practice. 

Enhancing interdisciplinary communication. A seamless integration between the CPOE 

and BCMA systems can enhance workflow and also build interdisciplinary communication. 

Information systems have the capability to serve as the common thread linking an 

interdisciplinary clinical team with the therapeutic decisions driving patient care. Expansion of 

BCMA into institutions will strengthen nurses’ relationship with other members on the clinical 

team and help other clinical staff to better appreciate nurses’ contribution to patient safety. 

Research Implications 

Nursing practice has undergone a dramatic transformation with the implementation of 

BCMA systems. BCMA increases the visibility of the nurse’s role in the medication 

administration process and contributes to the organization’s commitment to patient safety efforts. 

Strategies that can be employed to enhance future research efforts in this domain are discussed 

below. 

Medication error rate. A deeper examination is needed for the most commonly evaluated 

variable characterizing the medication error rate. No study reports an analysis regarding the type 

of ADEs, such as preventable and potential (often called near misses), that occurred while a 

BCMA system is in use. Such an evaluation would give us a deeper understanding of the ADEs 

that are being missed by the system and allow us to create modifications to better capture them.

Need for evaluation of economic outcomes. The policies of the FDA mandating barcoding 

of medications and the regulatory efforts of other patient safety organizations will, hopefully, 

encourage adoption of barcode technology. Research in this domain is limited and needs to be 

expanded to include examination of some core outcomes related to BCMA implementation. 

Quantitative estimations of return on investment following BCMA implementation and 

economic outcomes resulting from prevention of medication errors will expedite the adoption of 

this technology in more hospitals. 

Outcomes such as reduced length of stay, decreased number of nursing full-time equivalents 

needed to perform medication administration, and decreased litigation following administration 

of incorrect medications are important economic considerations that hospital administrators 

evaluate when deciding in which technology to invest their health care technology dollars. These 

outcomes need examination with respect to BCMA technology. 

Need for nurse involvement in BCMA implementation and design. As BCMA 

technology gets deployed in the medication administration process, it will have serious 

implications for nursing practice. Several issues surrounding the nursing workflow environment 

21 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

22 

need to be examined when implementing a BCMA system. Issues ranging from the usability of 

the hardware and software to pragmatic issues, such as the ease of use of the barcode reader and 

availability of the portable computer, will determine nurses’ acceptance of BCMA in their 

patient care routine. This offers nurse researchers unique opportunities to provide leadership in 

the development and design of BCMA systems. Active collaboration of nurses with information 

technology personnel—to provide input on the display of alerts for urgent orders, reports of 

missing medications, or on recording the missed medications during a shift—can be invaluable 

to the deployment of BCMA systems. 

Sociotechnical evaluation. Evaluation of BCMA technology from a sociotechnical 

perspective would help gain a deeper understanding of nurses’ use of BCMA systems. There is a 

paucity of literature measuring the sociotechnical issues of compliance with alerts, cognitive 

load, efficiency, productivity, and emotional aspects of using the technology. 

Documentation discrepancies. Besides serving as a safety check for nurses in the 

medication administration process, BCMA systems also play a key role in creating electronic 

MARs. Discrepancies in the documentation process have also been evaluated. Such 

discrepancies arise when medications that have been dispensed by the pharmacy fail to be 

administered by nurses. The returned medications are not credited back into the patient’s billing 

account. Thus, discrepancies arise between what is dispensed, what is administered, and 

ultimately what the patient is billed for. Even though documentation discrepancies have been 

examined, there is no evaluation of the economic impacts of these discrepancies. Such 

discrepancies could lead to undesirable fiscal outcomes for the hospital, which might affect 

adoption of these systems. A systems analysis of how these discrepancies arise and what 

organizational policies can be put in place to inhibit them needs to be conducted. 

The implementation of barcode technology can prevent the potential or near-miss errors that 

would not have been detected otherwise. Nurses must take an active and visible role in the 

development and deployment of BCMA technology. Participation is the key solution to 

implementing BCMA technology. 
 

Evidence Table 5. Studies Evaluating Barcode Medication Administration (BCMA) Source Setting BCMA Study Design Study 

Intervention 

Key Finding(s) Quality 

Score 

Anderson 200467 St. Mary’s Hospital 

Medical Center 

Vendor Pretest, post-test Decrease in 

medication error rate. 

The goal of 50% decrease in medication errors in the pilot unit was exceeded within 6 months of implementation. A 44% decrease in medication errors was reported for the entire hospital. 

46 

Barry 198969 2 nursing units 

and 2 controls in a private, not-for-profit hospital 

Homegrown Pretest, post-test 

with controls 

Potential for using barcode technology to reduce documentation errors of IV solution administration. 

Errors were traced to three primary sources: (1) failure to document administration of solution (38%), (2) failure to credit patient for IV solutions returned to the pharmacy (37%), and (3) administration of solution to the wrong patient (25%). 

48 

Coyle 2005 66 Various 

Veterans Affairs Medical Center (VAMC) hospitals 

Homegrown Time series Survey of nursing 

staff perceptions about BCMA decreasing risk for medication errors. Decrease in medication errors. 

After 3 years, 97% of the nursing staff agreed that BCMA could decrease the risk for medication errors, potential and actual. Medication errors decreased by 23% in the first year and by 66% after 5 years. 

44 

Englebright 200570

85 facilities of Hospital Corporation of America 

Unknown Pretest, post-test Frequency of 

scanning patient armbands and medication labels. 

Patient armband scanning increased by 7%, and medication label scanning increased by 13%. 

31 

23
24

Sourc

e Setting BCMA Study Design Study Intervention Key Finding(s) Quality Score 

Hokanson 198472 1 outpatient pharmacy service in an ambulatory clinic Homegrown Cross-sectional Feasibility study of using BCMA in the pharmacy setting. No dispensing errors were made. 39 Johnson 200265 VAMC Homegrown Time series Number of medication errors prevented. Prevented 549,000 medication errors while dispensing 8 million doses, in 6 years. 57 Low 200268 2 medical surgical units at a Midwest government hospital Vendor (Tremont BCMA) Pretest, post-test Medication error rate 12 months pre- and postimplementation of a BCMA system. Medication error rate increased by 18% after BCMA implementation due to enhanced reporting by BCMA system. 62 Patterson 200271 Acute care and nursing home wards of three VA hospitals Homegrown Cross-sectional, observational study before and after implementation To identify the negative, unintended side effects resulting from the 

implementation of BCMA systems that can create new paths to ADE occurrence. 

Five negative side effects after BCMA implementation were identified. 

98 
 Patient Safety & Health Information Technology 

Decision-Support Systems for Nursing 

Background 

Patient safety researchers view decision-support systems (DSS) as a solution to high rates of 

medical errors and inappropriate care. Many researchers view embedding DSS systems into 

well-developed, comprehensive CPOE systems as the only method to significantly impact 

clinical decisionmaking. Order entry with DSS harnesses the full potential of the computer to 

provide relevant information, guide decisions, and structure data entry.6, 76, 77 For this section of 

the chapter, the main focus is on decision-support interventions for nursing that are embodied 

within a CPOE system that is linked to a comprehensive electronic health record (EHR). DSS 

interventions were considered if they were implemented in the context of an existing CPOE 

system or could easily be integrated into a CPOE system. Decision-support systems are software 

designed to support or enhance clinical decisions. This is a broad definition and includes changes 

in information displays, alerts, reminders, or fully developed algorithmic computerized 

protocols. 

Research Evidence 

There were two published studies on nursing-specific DSS studies conducted in a CPOE 

environment.78, 79 There were many other published studies that reported early development work 

and validation results for DSS related directly to nursing. However, because so few were 

implemented, they were not eligible for inclusion here. Overall, there were 31 studies where the 

DSS intervention either was embedded in an EHR with CPOE or could reasonably be expected 

to have that capacity (see Table 6). This set of studies could be divided into two groups. The first 

group includes those studies targeting nursing decisionmaking directly (e.g., prevention of 

pressure ulcers, incontinence, triage). There were 13 studies in this group; however, only one 

was actually implemented in a CPOE system. The second group includes those studies largely 

targeting physicians, but the clinical focus could reasonably be associated with nursing. This 

judgment is, of course, subjective, as nursing is involved in almost all aspects of care. However, 

some activities have substantial nursing involvement. There were 18 studies in this second 

group, covering three broad areas: (1) acute care guidelines for selected topics, (2) critical care, 

and (3) preventive care. There were many other studies where the role of nursing might be 

significant, such as coagulation therapy or diabetes management. Because the nursing role was 

not explicated and would likely vary, these studies could not be included. 

Direct decision support for nursing. Out of the 31 studies identified as relevant, 13 

focused directly on nursing. Three studies concerned consultant systems for the prevention of 

pressure ulcers. All three were essentially qualitative or descriptive, presenting very little patient 

outcome data.80–82 Decision support for the management of urinary incontinence has been 

studied as well. Petrucci and colleagues83 found large increases in knowledge and decreases in 

episodes of urinary incontinence in a patient care unit where a consultation system was 

implemented, as compared to a unit in the same hospital where it had not been implemented. 

Three studies examined the performance of staff conducting telephone triage with the help of 

algorithmic decision-support systems. All found improved performance, although all three used 

the weakest design, a pretest, post-test evaluation.84–86 Two other studies directly addressed alerts 

and reminders to nursing staff for preventive care. Both of these showed strong results, including 

25 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

one study that compared standing orders with alerts directed at nursing staff to alerts directed 

toward physicians.79, 87 

In another study, the effects of different forms of physiologic data displays in a neonatal 

ICU were examined in a CPOE environment.78 In this study, all infants were randomly assigned 

during an 18-month period to one of four groups: (1) no display of trend data, (2) continuous 

display of trend data, (3) alternating 24-hour display of trend data starting in the first 24 hours, or 

(4) the same as the third group starting after the first 24 hours. The number of orders for colloid, 

blood gases, and ultrasound were measured, as were longer-range variables, such as total time on 

ventilation, total time on supplemental oxygen, length of stay, and death. No differences in 

patient outcomes were noted, although surveys found increased knowledge regarding neonatal 

physiology on the part of the staff. Because patients rather than clinicians were assigned 

randomly, it is highly likely that there was dispersion of the effects of the independent variable. 

Finally, several studies on a clinical decision-support system (CDSS) that guides nurses in 

identifying patient preferences consistently found improvement in the degree that nurses were 

able to act in accordance with patient preferences. These studies employed a high-quality design, 

and although the program was never embodied in an EHR, it is conceivable that one day it might 

implemented with resultant, improvement in the continuity of care.88–90

Indirect decision support for nursing. The remaining 18 studies were selected because 

they used a computerized intervention that either was instituted in a CPOE environment or used 

a well-established EMR. In addition, these studies focused in areas where nursing would likely 

be highly involved. One study79 contrasted standing orders (to the nursing staff) versus 

computerized reminders for preventive care for inpatients, finding nearly twice the improvement 

with standing orders. Three studies focused on the use of guidelines to prevent deep venous 

thrombi in post-surgical patients, involving both nursing and physician activities. In two of the 

studies, significant results were found for provider compliance using the guidelines, but no 

difference was found regarding patient outcomes.91, 92 In the third study,93 both compliance with 

guidelines and patient outcomes were improved. 

Four other studies were conducted in critical care settings, involving mostly complex 

guidelines (e.g., ventilator support). In two other studies, the effects of a computerized guideline 

for the treatment of adult respiratory distress syndrome were the focus of the investigation. Both 

were conducted by investigators from the LDS hospital in Salt Lake City, which had an 

extensive EHR at the time, but not full-scale provider order entry. East and colleagues94

examined the impact of the computerized guideline in a prospective multi-center randomized 

trial for 200 patients. No significant differences were found in survival or ICU length of stay 

between treatment groups. There was a significant reduction in morbidity as measured by a 

standard scoring system, as well as a lower incidence of over-distension lung injury. In a similar 

study, a pilot of the study published above at Memorial Hermann Shock Trauma ICU, McKinley 

and colleagues95 randomized 67 trauma patients to either being cared for by the protocol or not. 

No difference was found between patients in terms of survival, length of stay, or morbidity.

In another study conducted in the outpatient setting in the VA, with a complete CPOE 

system, computerized guidelines for mental health screening resulted in significantly higher 

compliance than paper guidelines. It was not clear in the description how nurses (not including 

the advance practice nurses) were involved in the implementation, but they might have been the 

individuals actually receiving the alerts.96 

Several studies using qualitative techniques found similar issues. Karfonta97 used grounded 

theory to examine the experiences of 23 nurses and 10 physicians using DSS systems in the ICU. 

26 
 Patient Safety & Health Information Technology 

Overall, all interviewees mentioned the role of DSS in assisting in forecasting the outcomes of 

decisions. Difficulties in learning the system, trusting the output, and understanding the 

technology were additional themes.97 Lyons and colleagues98 examined VA employees’ 

perceptions of guideline implementation and utilization in the VA’s CPOE system. Information 

technology issues were perceived as major barriers to effective guideline implementation. 

Patterson and colleagues99 conducted a human factors qualitative analysis of the VHA clinical 

reminder system and noted that increased workload, training, time, and role divisions were key 

barriers to success. 

The remaining studies all focused on preventive care reminders or hypertension followup, 

with only two focused mainly on nursing.87, 100 In most cases, but not all, the studies found 

improved compliance. Because the studies took place in the outpatient setting, the role of the 

nursing staff likely varied greatly, but is not elucidated. Further analysis would have to be done 

to determine if an increased role of nursing in providing followup and initiating immunization 

was a determinant contributor of success. 

Challenges with research evidence. Three main themes can be extracted from the results. 

First, for the most part, nursing activity is simply not addressed in these studies. Nurses make 

decisions every day about pain and wound management, whether a patient’s symptoms are 

severe enough to notify a physician. Nurses often have to decide if a patient’s symptom is drug 

related, or if a drug might interact with other drugs before they give them. In many cases, nurses 

have primary responsibility for patient education and family support. Few decision-support 

interventions have been developed for any of these high-level, decision-based actions. It is as if 

nursing decisionmaking is invisible and nurses are viewed as data collectors, rather than 

decisionmakers. In addition, one of the main roles that nurses fill in an inpatient setting is that of 

an intermediary between the patient and other providers. This communication role is a crucial 

function in ensuring quality of care. However, communication has been significantly neglected 

in EHR designs, as was noted earlier in this chapter and by many other authors.101, 102 Most of the 

work to create DSS has focused on structured documentation, order review, or systems designed 

to force or track nursing actions. For example, one study examined the effectiveness of putting a 

signal on a nurse and tracking where they were at all times to ensure efficiency.103 Another study 

examined the impact of opening locked medicine cabinets (in the room) only when medications 

were due, to ensure that nurses would give them on time.104

Second, the mechanics of providing DSS for nursing in a regular CPOE inpatient setting has 

not been well explicated. In many settings, the computers are located at the nursing stations, 

making them unavailable at the time of care. The model of having decision-support software 

located on computers situated at the nursing station fails to support a nurse who is constantly on 

the move. Development and exploratory work has been published, examining the use of portable 

laptops or hand-held computers, but no high-quality studies have reported on an actual 

implementation directed at nursing. 

Third, none of the studies have examined the mechanism of action for DSS interventions. 

This is true of the DSS literature in general. Because DSS interventions can range from alerting a 

clinician about something they already know (e.g., a reminder), to alerting the staff to where a 

patient is in a process (tracking), to providing new information that educates and informs, it is 

important to measure the intended psychological effect as well as the outcome.105, 106 Although 

most studies show significant increases in provider compliance, the effect is small, and the upper 

limits are in the low 40- or 50-percent levels. 

27 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Evidence-Based Practice Implications 

The above three themes provide a framework for discussing how to link this work to 

practice. Because the large body of nursing activity is simply not addressed in these studies, it is 

difficult to identify the important practice implications. Many clinical interventions are likely 

designed by nursing for quality improvement purposes using the EHR, and these are not being 

captured in the formal research literature. In the VHA, there are substantial local initiatives led 

by nursing to improve patient care using the functionalities provided by the CPOE. Few, if any, 

are published. If they are published, the focus is on “lessons learned” rather than to provide 

scientific evidence of efficacy. Nurses in practice can inform themselves of the functionalities of 

their EHR and volunteer to serve on hospital informatics committees that make strategic 

decisions. Adapting the system to nursing’s needs and adapting to the system is a process of 

whole-system transformation.107–109 

The second issue regarding the improvement of practice is how to improve the mechanics of 

providing DSS for nursing in the inpatient setting, given the fact that nurses are often on the 

move. Some of the more effective decision support can be arrangements of lists, printouts, and 

other easy-to-carry tools to simplify and organize data. Most EHRs have the capacity to be 

customized to individual clinical needs. Nurse managers are in a unique position to evaluate their 

system and participate in the development of low-resource-impact, decision-support tools. Nurse 

managers could also be involved in technology planning, to ensure that computers are available 

at the bedside. In addition, interventions, such as BCMA, structure nursing documentation. But 

because it is linked to an information system, imbedded DSS could easily be implemented to 

alert nurses about possible ADEs. 

Because DSS is likely to be implemented by administration, it is important that the nurse 

managers argue for evaluation of the system. Evaluation should focus on measuring the 

implementation itself, the work process changes, and the outcomes. The evaluation should be 

started at the same time as the implementation so that the information gleaned will not only 

minimize any negative impact on patient safety, but also will provide for maximum input by 

nursing staff during the change process. Ongoing evaluation is essentially a quality management 

activity and is a practical approach to clarifying the mechanisms of action—and to ensuring that 

the impact of DSS on nursing practice is formally addressed. 

Research Implications 

The three themes identified above also provide a framework for discussing future research 

implications. The work that needs to be addressed immediately is clarification of nursing roles in 

the implementation and success of DSS systems, especially those implemented in the context of 

CPOE systems. Most of these interventions are multidisciplinary and involve substantial process 

reengineering that goes largely unreported. Nurses are in a position to fully comprehend the 

depth of this reprocessing, and expanding our understanding in this area would be a contribution 

to the field as a whole. Implementation as a science is expanding, and nursing expertise is 

crucial. 

Secondly, much more work needs to be done to delineate and clarify the actual decisions 

made by nursing in order to develop effective decision-support systems. This goal can be 

accomplished in two ways. More qualitative work is needed to describe and analyze nursing 

decisionmaking using recent theoretical advances in the cognitive sciences. Activity theory,110

28 
 Patient Safety & Health Information Technology 

29 

goal theories,111 and adaptive rationality112 are areas that would be very useful as approaches to 

understanding nursing practice. In addition, although many studies have been published that 

explore decisionmaking and nursing expertise, some of which have been developed and 

validated, very few have been actually implemented. Nursing needs to examine the barriers that 

prevent the outcome of these research projects from reaching higher levels of adoption. 

Finally, because the mechanism of action for DSS interventions is not examined, future 

advancement in the field of decision support is constrained. As described above, mechanisms are 

likely to be either psychological (e.g., directing attention, decreasing memory loads, or 

educational) and/or organizational (e.g., changing work processes and role behaviors). Designing 

studies that measure memory load, manipulate and test the role of attention, and directly assess 

learning effects as part of a DSS design would greatly advance the science. 
Evidence Table 6. Decision-Support Systems Within an Electronic Health Record Related to Nursing 

Source Setting Study Design Study Intervention Key Finding(s) Barnett 1983113 Outpatient Randomized controlled trial Reminders for followup for hypertensive patients. Followup was significantly impr

group receiving the reminders, followup attempted or achieved responsible physician and in threcording of blood pressure. 

Barton 1990114 Outpatient health maintenance organization Pretest, post-test Postcards compared to simple reminders compared to feedback and reminders. No changes in vaccination rate

computerized reminders were supplemented with feedback to providers. 

Cannon 200096 Outpatient mental health VA Randomized controlled trial with patients randomized within providers Computerized vs. paper reminders for screening and documentation of mood disorders using CaseWalker. The computerized screening rem

resulted in a higher screening rmood disorder (86.5 vs. 61 per0.008) and improved document

Clark 200580 Multilevel care in 

Canadian Health Region 

Qualitative, descriptive 

Computerized advisory management system to prevent pressure ulcers. 

Evaluation indicated an increasknowledge relating to pressure prevention, treatment strategierequired. Lack of visible senior leadership; time required to acqcomputer skills and to implemeguidelines; and difficulties with computer system were identifiebarriers. 

Coe 1977115 Outpatient Cluster case cohort Blood pressure management 

in outpatients using computerized reminders. 

No significant difference in patioutcomes. 

Cunningham 199878

Critical care Randomized control 

trial 

Continuous trend display vs. summative aggregated displays. 

None of the short-, medium-, opatient outcomes demonstrated significant benefit from the provcomputerized physiologic trend 

Dale 2003116 Emergency services Pretest, post-test Consultant support for nurse 

triage. 

More patients requiring an ambseen in the emergency departmintervention group as compared control (odds ratio = 2.62; 95% 3.85). 

Davidson 198487 Outpatient Pretest, post-test Specific nurse-targeted 

reminders. 

Significant increases in stool exfor occult blood (32% to 47%), examination (29% to 46%), and immunization (18% to 40%). 

30
 

31

Source Setting Study Design

Study Intervention Key Finding(s) 

Dexter 200479 Inpatient Randomized 

controlled trial 

Compared standing orders to computerized reminders in a CPOE environment. 

Patients with standing orders reinfluenza vaccine significantly m(42%) than those patients with (30%) (

P < 0.001). Patients wit

orders received a pneumococcsignificantly more often (51%) twith reminders (31%) (

P < 0.00

East 199994 Critical care Randomized 

controlled trial 

Computerized guidelines for management of ventilated patients. 

No significant difference in survlength of stay between the two 

groups (X2 = 0.49, 

P = 0.49) an

0.88, 

P = 0.37). There was a si

reduction in morbidity (F(1) = 4and severity of over-distension (F(1) = 45.2, 

P < 0.001). 

Hutchison 1989100 Outpatient clinic Pretest, post-test 

repeated measures 

Printed reminders attached to charts taken from EMR. 

Vaccination rate increased from 26.8% and no increase in influeimmunization in the comparison 

Karfonta 199997 Critical care Qualitative analysis 

of nurses and physicians 

DSS in general. DSS was seen to be important 

forecasting decisional outcomefour sub-areas: DSS learning, understanding DSS technologyDSS inferences, and trusting Ddata. UK. 

Kucher 200593 Inpatient in a CPOE 

environment 

Randomized controlled trial 

Alerts given to physicians. The rate of prophylaxis increas

in the control to 33% in the integroup. Those receiving prophyl41% less incidence of DVT thadid not. 
32

Source Setting Study Design

Study Intervention Key Finding(s) 

Lyons 200598 Inpatient VA Qualitative Perceptions of VA clinicians 

regarding the role of information technology in implementing guidelines. 

Eighteen themes clustered into domains. Workplace factors weoften discussed by administratodesign issues discussed most band personal concerns discussphysicians and nurses. Facilitaguideline maintenance and chaformats. Barriers included resoattitudes, time and workload, coglitches, computer complaints, retrieval, and order entry. Themdesignations included documenpatient records, decision suppoperformance evaluation, clinicaguidelines (CPG) implementatiliteracy, essential data, and comaccessibility. 

McKinley 200195 Critical care Randomized 

controlled trial 

Computerized guideline to manage ventilated patients. 

Outcome measures (i.e., survivlength of stay, morbidity, and bwere not significantly different bgroups. Fio2 > or = 0.6 and Pla35 cm H2O exposures were lesprotocol group. 

Mosen 200492 In patient post-op in 

highly developed EMR system, but not complete CPOE 

Pretest, post-test Guideline to prevent postsurgical DVT (deep vein 

thrombosis). 

The overall prophylaxis rate inc89.9% before implementation ocomputerized reminder system after implementation (

P < 0.000

combined 90-day rate of symptpulmonary embolism (PE), and attributable to PE remained the 1.0%; post-1.2%; odds ratio = 1= 0.67–2.20). 

Murray 2004116 Outpatient in a CPOE 

environment 

Randomized controlled trial 

Computerized suggestions given to (1) physicians, (2) pharmacists, (3) both, or (4) none. 

No significant differences found groups in terms of quality of lifehospitalizations, ER visits, costpressure (BP). 

Patterson 199891 Inpatient in a CPOE 

environment 

Pretest, post-test Computerized algorithm with 

protocols for prevention of DVTs. 

The preintervention rate of DVT over a 3-month period was 85.2921 eligible cases). For the 3 mfollowing the introduction of the computerized reminder, compliDVT prophylaxis increased to 9
 

33

Source Setting Study Design

Study Intervention Key Finding(s) 

Patterson 200499 Outpatient use of 

clinical reminders in the VA CPOE system 

Qualitative Human factors analyses 

were conducted on users across multiple settings and roles. 

Significant barriers and issues identified, including time, worklnonrelevance, ease of use, traicomplicated procedures for refu

Petrucci 199183 Nursing home in a nonCPOE environment Case control Disease management consultation for urinary 

incontinence. 

The number of wet occurrenceresiding on units where nurses UNIS decreased significantly; F 34.67. The knowledge of urinarincontinence also improved sigwhen nurses consulted UNIS; F 19.46. 

Rogers 1982117 Outpatient in a nonCPOE environment Randomized controlled trial Alerts to manage hypertension, obesity, and 

renal disease, using printouts only. 

Decreased BP, decreases in hoand length of stay. 

 Ruland 199988 Inpatient unit for the 

elderly 

Quasi-experimental nonrandom assignment; groups selected in tandem. 

Use of a systematic protocol for eliciting patient preferences given to nurses in experimental group. 

Patients whose nurse was givepersonal preferences reported congruent with their preference

Ruland 200289 Inpatient Randomized 

controlled trial 

Use of a hand-held computerized decision support. 

Nurses' use of CHOICE made more consistent with patient pr= 11.4; 

P < 0.001) and improve

preference achievement (F = 4

Ruland 200390 Outpatient Randomized 

controlled trial 

Use of a computerized system that collects patient preferences. 

Patient reports of topics addresthe consultations showed greatcongruence in the experimentacompared to control group. 

Schriger 1997118 Emergency services Time series Guidelines for treatment of 

occupational body fluid exposures. 

Mean % documentation of esseincreased from 57% to 98% in intervention phase, and aftercaincreased from 31% at baseline during the intervention phase, bdecreased to baseline when thsystem was removed. Compliaguidelines increased from 63% during the intervention phase. Pof charges increased from 44% during the intervention phase adecreased to 36% following the intervention. 
34

Sourc

e Setting Study Design

Study Intervention Key Finding(s) 

Schriger 2000119 Emergency services Time series Guidelines for care of febrile 

children. 

Percentage of 21 essential histphysical examination items incr80% during the baseline period the intervention phase (13% incCI = 10–15%). Mean percentagdocumentation of 10 items in thinstructions increased from 48% to 81% during the intervention increase; 95% CI = 28–38%). Ato baseline when the computer removed. 

Slovis 198585 Emergency services Pretest, post-test Triage DSS using flip charts; 

users were not clinicians. 

The DSS system shortened the 

response time from 14.2 minuteminutes for the most urgent cas0.05); resulted in a significant inthe use of advanced life suppothis group (

P < 0.02). 

Strachan 200186 Emergency services Pretest, post-test Triage Effective triage went from 20% 

Tang 1999120 Outpatient CPOE 

environment. 

2-yr prospective case control 

Reminders for immunization. Used physician volunteers for C

Compliance rates for the comppatient record system (CPR) usincreased 78% from baseline (whereas rates for the paper recuser group did not change sign0.18). 

Willson 199581 Inpatient Pretest, post-test Implementation of AHCPR 

guideline on pressure ulcers. 

Comparison of computerized pa previously implemented papeVery little data. 

Zielstorff 199782 Inpatient unit Case control Pressure ulcer DSS for 

nurses used by nurse volunteers; no data on usage. 

Dependent variables were knowdecisionmaking results from simNo patient data provided. 
Patient Safety & Health Information Technology 

Conclusion 

Across the sections in this chapter, several themes are apparent. First, nursing and nursing 

impacts are nearly absent in the current empirical studies of work on EHR orders and clinical 

decision support within ordering systems. Future research is needed to understand the impact of 

that technology on the role of nurses and workflow methods that are effective for nurses in a 

computerized orders environment. Nursing clearly participates in the orders process; yet, the 

assessment of that role is missing to date. More important, nurses and pharmacists serve in roles 

as protectors against errors in patient care. The counts of intercepted errors speak to this role in a 

simplistic way. More complex variables and expanded research is needed on this topic. With 

CDSS, nurses are studied as invisible partners in the care process rather than as decisionmakers 

themselves. Yet, nurses make thousands of care decisions a day. Borrowing methods from 

psychology, future researchers could expand the cognitive work in this area. 

BCMA is the exception to the absent nursing voice. In BCMA, nurses are integral to the 

success of the application. Medication error reduction with BCMA is apparent. Additionally, the 

VHA has effectively included nurses in the design and implementation of technology-assisted 

medication administration. However, technology assistance in medication administration 

represents a lower-level cognitive process than, say, decisionmaking about symptom assessment 

or an independent care intervention. Thus, future research on decision support for higher 

cognitive processes and the nurse as a full-fledged decisionmaker is warranted. 

There are several limitations to this work. A strong effort was made to have well-defined 

inclusion criteria to make the studies as homogeneous as possible and to allow valid 

comparisons. However, the inclusion criteria have limited this analysis to implemented solutions, 

narrowing the possible CDSS applications in particular. Likewise, studies were excluded from 

areas such as imaging and psychiatry; in the future these areas could be examined. Our results 

included some qualitative work, not usually considered as evidence, but included here to better 

describe the phenomena at hand. An analysis without qualitative studies would perhaps come to 

different conclusions. 

Studies in sociotechnical and human-computer interaction are needed in each of these areas. 

This would help us understand the complex processes inherent in technology design and 

adoption. Interdisciplinary examinations are needed in future research to understand 

interdependent roles. With technology becoming an omnipresent participant on today’s health 

care teams, traditional roles on a health care team have been altered. For example, computerized 

orders management changes roles, and role renegotiation must take place. New process and new 

issues emerge with complex technologies like CPOE; this interdependence needs to be 

systematically evaluated in the future. The research in HIT integrative functions is just 

beginning. Future opportunities are many for areas of great impact to nursing. 

Search Strategies 

CPOE Search Strategies 

A broad search of the literature from 1976 through the end of 2005 was undertaken as part 

of a larger study to locate articles dealing with the practice impacts of clinical computing 

applications. Searches were conducted in PubMed®, CINAHL, Cochrane, PsychInfo, DARE, 

INSPEC, CENTRAL, and HTA databases. The search strategy is located in appendix A. The 

35 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

search yielded 63,731 references with 1,023 abstracts rated as having empirical data. Abstracts 

were coded for relevancy and sorted into categories (e.g., clinical decision support, CPOE, EHR 

adoption). CPOE-coded articles were retrieved as a subset from the larger search results. Search 

terms were 

online order entry OR computer-based physician workstation OR practitioner 

order entry OR physician order entry OR electronic health record OR 

computerized physician documentation OR computer medical records OR 

medication order entry OR computer based order entry OR CPOE OR POE 

The CPOE search yielded 178 potentially eligible articles. 

CPOE articles were rated for eligibility with empirical studies of any design and systematic 

reviews being considered relevant. The relevant studies examined implemented solutions with a 

concentration on any practice implications of CPOE. Letters, opinions, and editorials were 

excluded, as were articles dealing with models or theoretical discussions about systems. Further, 

studies were excluded if they (a) provided only verbal summaries of CPOE impacts or 

satisfaction with CPOE; (b) focused solely on CPOE costs or ordering volumes; (c) primarily 

focused on imaging, dentistry, simulations, psychiatry, data mining, or genetics; or (d) focused 

solely on CPOE or EHR adoption methods. The authors separated studies with a major focus on 

guidelines and order-related decision support into a separate section of this chapter. This first 

section targets clinical impacts of paper-based ordering compared to CPOE. 

BCMA Search Strategy 

A review of studies published in peer-reviewed journals and meeting abstracts was 

undertaken. The search criteria used for PubMed® was as follows: 

barcode point-of-care technology OR bar code medication administration OR 

BCMA OR medication bar coding OR barcode medication administration OR 

barcode point-of-care technology OR eMAR OR electronic medication record 

The search was limited to studies with abstracts that were published in the English language 

and spanning the years 1976 to 2005. This search retrieved 205 abstracts, out of which 29 were 

relevant. A second search was conducted to look for studies that focused on the nursing domain 

by combining the above search strategy with “AND nursing.” The same limits were applied to 

this search as well. A total of 33 abstracts were retrieved, out of which 10 were considered 

relevant for this review. In all, 39 abstracts were considered relevant in our first evaluation. 

A second evaluation was conducted by retrieving and reading the full text articles. The 

inclusion criteria used to determine whether a study was relevant or not were the same as those 

used for CPOE, as described above. Eight studies provided evidence of actual implementation of 

a BCMA system and its evaluation; the remaining 31 articles were discarded. Further, quality 

assessment of the studies was conducted using the QUASII instrument. Table 5 presents a 

summary of the key findings and variables measured in the nine studies that were finally 

evaluated. 

Decision Support Search Strategy and Methods 

The broader search strategy used for this review is described at the beginning of the chapter. 

The results retrieved from this larger search were further analyzed for relevance to DSS 

interventions for nursing. To be included in the final round, a study had to be reporting an actual 

36 
Patient Safety & Health Information Technology 

evaluation or research study conducted with real patients cared for with the intervention in place. 

Although the focus was on those studies associated with CPOE and an EHR, few have been 

conducted (especially for nursing). Therefore, studies were included where the intervention 

could easily be implemented in the context of CPOE with an EHR. Studies whose main focus 

was on nursing interventions designed to improve documentation, care planning, or 

administration were not included. Simulations, early reports of development findings, or 

validation of DSS software were also not included, nor were studies that were simply descriptive 

or had a “lessons learned” perspective. However, studies that used methods close to a traditional 

qualitative methodology or formal human factors analysis were included when appropriate, for 

example, the focus was on a system implemented in real time. 

Inclusionary terms: 

Online order entry OR computer-based physician workstation OR practitioner order entry 

OR computer-based medical record OR electronic health record OR computerized physician 

documentation OR computer medical records OR decision support computer program OR 

health maintenance reminder OR CDSS OR computer-aided OR computerized decision 

making support OR clinical decision support system OR computerized feedback OR 

computer-assisted dosing OR computer feedback OR predictive instrument OR computeraided quality assurance OR computer alert OR clinician order entry OR provider order entry 

OR computerized reminder OR computer reminder OR computer-based monitoring system 

OR expert system OR computer-based medical decision support OR decision support system 

OR computer based order entry OR event reporting system OR electronic healthcare record 

OR electronic monitoring OR electronic health record OR electronic medical record OR 

electronic incident reporting OR electronic record OR electronic patient record OR 

electronic record keeping OR medical information system OR computer-predicted OR 

computer-based monitoring OR computer-based prompt system OR CPOE OR POE OR 

electronic journal OR medical reminders OR electronic reminders OR medical record alert. 

Inclusionary Mesh® terms (for PubMed® only):

"Decision Support Systems, Clinical"[MeSH] OR "Hospital Information Systems"[MeSH] 

OR "Medical Records Systems, Computerized"[MeSH] 

Exclusionary terms:

NOT (X-ray OR biochemistry OR DNA OR RNA OR genome OR tomography OR 

dentistry OR dental OR simulation OR molecular OR animal OR psychiatric OR 

biofeedback OR HIPAA OR in-home OR data mining OR algorithm) 

Exclusionary Mesh® terms (for PubMed® only): 

NOT ("Validation Studies"[Publication Type] OR "Editorial"[Publication Type] OR 

"Letter"[Publication Type] OR "News"[Publication Type] OR "Comment"[Publication 

Type] OR "legislation and jurisprudence"[Subheading] OR "Libraries, Medical"[MeSH]) 

Limits 

Articles considered were those published in English during the time period January 1, 1976 

to August 1, 2004. 

Acknowledgments 

This work was supported by a Veterans Administration Health Services Research and 

Development grant awarded to Charlene Weir, #02-097-2. 

37 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Author Affiliations 

Nancy Staggers, Ph.D., R.N., F.A.A.N., Associate Professor of Informatics, University of 

Utah College of Nursing, adjunct Associate Professor, School of Medicine. E-mail: 

Nancy.Staggers@hsc.utah.edu. 

Charlene Weir, Ph.D., R.N., Associate Director of Education and Evaluation, VA GRECC, 

Salt Lake City, UT. E-mail: Charlene.Weir@med.va.gov. 

Shobha Phansalkar, R.Ph., Ph.D., Informatician, Partners Healthcare System, Inc., Instructor, 

Harvard Medical School. E-mail: sphansalkar@partners.org. (This chapter was written while she 

was a doctoral candidate at the University of Utah Department of Biomedical Informatics.) 

References 

1. Healthcare Information and Management Systems 

Society. Electronic health record attributes and 

essential requirements. 2003. Available at: 

http://www.himss.org/content/files/ehrattributes0707

03.pdf. Accessed September 22, 2006. 

2. Healthcare Information and Management Systems 

Society. HIMSS EHRV: A definitional model and 

application process. 2006. Available at: 

http://www.himssehrva.org/docs/EHRVA_applicatio

n.pdf. Accessed September 22, 2006. 

3. Shortliffe EH. Strategic action in health information 

technology: Why the obvious has taken so long. 

Today the United States is poised to achieve what has 

been sought and anticipated for at least three decades. 

Health Aff (Millwood) 2005;24:1222-33. 

4. Chaudhry B, Wang J, Wu S, et al. Systematic review: 

Impact of Health Information Technology on quality, 

efficiency, and costs of medical care. Ann Intern Med 

2006;144;742-52. 

5. Institute of Medicine. Crossing the quality chasm: A 

new health system for the 21st century. Washington, 

DC: National Academy Press; 2001 

 6. Institute of Medicine. To err is human: Building a 

safer health system. Washington, DC: National 

Academy Press; 2000. 

7. Institute of Medicine. Preventing medication errors: 

Quality chasm series. Washington, DC: National 

Academies Press; 2006. 

8. Phansalkar S, Staggers N, Weir CR. Development of 

the QUASII (QUality Assessment of Studies in 

Informatics Implementations) instrument. VA 

HSR&D national meeting; 2006 Feb 15-17; 

Washington, DC; 2006. 

9. Potts AL, Barr FE, Gregory DF, Wright L, Patel NR. 

Computerized physician order entry and medication 

errors in a pediatric critical care unit. Pediatrics 

2004;1131 Pt 1:59-63. 

10. Bates DW, Teich JM, Lee J, et al. The impact of 

computerized physician order entry on medication 

error prevention. J Am Med Inform Assoc 

1999;6:313-21. 

11. Shulman R, Singer M, Goldstone J, et al. Medication 

errors: a prospective cohort study of hand-written and 

computerized physician order entry in the intensive 

care unit. Crit Care 2005;9(5):R516-21. 

12. Spencer DC, Leininger A, Daniels R, et al. Effect of 

a computerized prescriber-order-entry system on 

reported medication errors. Am J Health Syst Pharm 

2005;62:416-9. 

13. Thompson W, Dodek PM, Norena M, et al. 

Computerized physician order entry of diagnostic 

tests in an intensive care unit is associated with 

improved timeliness of service. Crit Care Med 

2004;32:1306-9. 

14. Han YY, Carcillo JA, Venkataraman ST, et al. 

Unexpected increased mortality after implementation 

of a commercially sold computerized physician order 

entry system. Pediatrics 2005;116:1506-12. 

15. Bates DW, Leape LL, Cullen DJ, et al. Effect of 

computerized physician order entry and a team 

intervention on prevention of serious medication 

errors. JAMA 1998;280:1311-6. 

16. Gandhi TK, Weingart SN, Seger AC, et al. 

Outpatient prescribing errors and the impact of 

computerized prescribing. J Gen Intern Med 

2005;20:837-41. 

38 
Patient Safety & Health Information Technology 

17. Mirco A, Campos L, Falcao F, et al. Medication 

errors in an internal medicine department. Evaluation 

of a computerized prescription system. Pharm World 

Sci 2005;27:351-2. 

18. King WJ, Paice N, Rangrej J, et al. The effect of 

computerized physician order entry on medication 

errors and adverse drug events in pediatric inpatients. 

Pediatrics 2003;112:506-9. 

19. Kaushal R, Shojania KG, Bates DW. Effects of 

computerized physician order entry and clinical 

decision support systems on medication safety: a 

systematic review. Arch Intern Med 2003;163:1409-

16. 

20. Kuperman GJ, Gibson RF. Computer physician order 

entry: benefits, costs, and issues. Ann Intern Med 

2003;139(1):31-9. 

21. Papshev D, Peterson AM. Electronic prescribing in 

ambulatory practice: promises, pitfalls, and potential 

solutions. Am J Manag Care 2001;7:725-36. 

22. Berger RG, Kichak JP. Computerized physician order 

entry: helpful or harmful? J Am Med Inform Assoc 

2004;11(2):100-3. 

23. Koppel R, Metlay JP, Cohen A, et al. Role of 

computerized physician order entry systems in 

facilitating medication errors JAMA 2005;293:1197-

203. 

24. Mekhjian H, Saltz J, Rogers, P, et al. Impact of 

CPOE order sets on lab orders. AMIA Annu Symp 

Proc 2003;931. 

25. Cordero L, Kuehn L, Kumar RR, et al. Impact of 

computerized physician order entry on clinical 

practice in a newborn intensive care unit. J Perinatol 

2004;24(2):88-93. 

26. Mekhjian HS, Kumar RR, Kuehn L, et al. Immediate 

benefits realized following implementation of 

physician order entry at an academic medical center. 

J Am Med Inform Assoc 2002;9:529-39. 

27. Ostbye T, Moen A, Erikssen G, et al. Introducing a 

module for laboratory test order entry and reporting 

of results at a hospital ward: an evaluation study 

using a multi-method approach. J Med Syst 

1997;21(2):107-17. 

28. Lehman ML, Brill JH, Skarulis PC,et al. Physician 

Order Entry impact on drug turn-around times. Proc 

AMIA Symp 2001;359-63. 

29. Tierney WM, Miller ME, Overhage JM, et al. 

Physician inpatient order writing on microcomputer 

workstations. Effects on resource utilization. JAMA 

1993;269:379-83. 

30. Poissant L, Pereira J, Tamblyn R, et al. The impact of 

electronic health records on time efficiency of 

physicians and nurses: A systematic review. J Am 

Med Inform Assoc 2005;12:505-16. 

31. Overhage JM, Perkins S, Tierney WM, et al. 

Controlled trial of direct physician order entry: 

Effects on physicians’ time utilization in ambulatory 

primary care internal medicine practices. J Am Med 

Inform Assoc 2001;8:361-71. 

32. Bates DW, Boyle DL, Teich, JM. Impact of 

computerized physician order entry on physician 

time. Proc Annu Symp Comput Appl Med Care 

1994;996. 

33. Shu K, Boyle D, Spurr C, et al. Comparison of time 

spent writing orders on paper with computerized 

physician order entry. Medinfo 2001;10:1207-11. 

34. Salmasy, D., Marx, E. A computerized system for 

entering orders to limit treatment: implementation 

and evaluation. J Clin Ethics 1997: 8: 258-63. 

35. Sittig DF, Krall M, Kaalaas-Sittig J, et al. Emotional 

aspects of computer-based provider order entry: A 

qualitative study. J Am Med Inform Assoc 

2005;12:561-7. 

36. Dykstra R. Computerized physician order entry and 

communication: Reciprocal impacts. Proc AMIA 

Symp 2002;230-4. 

37. Weir C, Lincoln M, Roscoe D, et al. Successful 

implementation of an integrated physician order entry 

application: A systems perspective. Proc Annu Symp 

Comput Appl Med Care 1995;790-4. 

38. Snyder R, Weston MJ, Fields W, et al. Computerized 

provider order entry system field research: The 

impact of contextual factors on study 

implementation. Int J Med Inform 2006;75(10-

11):730-40. 

39. Orthner H. Series preface. In Saba VK and 

McCormick KA, eds. Essentials of computers for 

nurses. New York: McGraw-Hill; 1996. 

40. Patel VK. Human computer interaction in health care. 

Handbook of medical informatics. 1997;473-93. 

41. Ash JS, Berg M, Coiera E. Some unintended 

consequences of information technology in health 

care: The nature of patient care information systemrelated errors. J Am Med Inform Assoc 

2004;11(2):104-12. 

39 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

42. Koppel R, Metlay JP, Cohen A, et al. Role of 

computerized physician order entry systems in 

facilitating medication errors. JAMA 2005;293:1197-

203. 

43. Ash J, Gorman P, Lavelle M, et al. Investigating 

physician order entry in the field: lessons learned in a 

multi-center study. Medinfo 2001;10:1107-11. 

44. Ash JS, Gorman PN, Lavelle M, et al. Multiple 

perspectives on physician order entry. Proc AMIA 

Symp 2000;27-31. 

45. Staggers N. Human-computer interaction. In 

Englebart S and Nelson R, eds. Information 

technology in health care: An interdisciplinary 

approach. New York: Harcourt Health Science 

Company; 2001. p. 321-45. 

46. Staggers N, Kobus D. Comparing response time, 

errors, and satisfaction between text-based and 

graphical user interfaces during nursing order tasks. J 

Am Med Inform Assoc 2000;7(2):164-76. 

47. Staggers N, Miller S. Using a web-based prototype 

and human-computer interaction concepts to develop 

a vision for a next generation patient care 

management system. Proc AMIA Symp 2001;652-6. 

48. Staggers N, Mills ME. Nurse-computer interaction: 

staff performance outcomes. Nurs Res 

1994;43(3):144-50. 

49. Hillestad R, Bigelow J, Bower A, et al. Can 

electronic medical record systems transform health 

care? Potential health benefits, savings, and costs. 

The adoption of interoperable EMR systems could 

produce efficiency and safety savings of $142-$371 

billion. Health Aff (Millwood) 2005;24:1103-17. 

50. Stair T, Howell J. Effect on medical education of 

computerized physician order entry. Acad Med 1995, 

6: 543. 

51. Leape LL, Brennan TA, Laird N, et al. The nature of 

adverse events in hospitalized patients. Results of the 

Harvard Medical Practice Study II. N Engl J Med 

1991;324:377-84. 

52. Leape LL, Bates DW, Cullen DJ, et al. Systems 

analysis of adverse drug events. ADE Prevention 

Study Group. JAMA 1995;274(1):35-43. 

53. Bates DW, Cullen DJ, Laird N, et al. Incidence of 

adverse drug events and potential adverse drug 

events. Implications for prevention. ADE Prevention 

Study Group. JAMA 1995;274(1):29-34. 

54. Bar code label requirement for human drug products 

and biological products. Final rule. Fed Regist 

2004;69:9119-71. 

55. Joint Commission on Accreditation of Healthcare 

Organizations. Potential 2005: National Patient 

Safety Goals Field Review. Available at: 

http://www.jcaho.org/accredited+organizations/05_n

psg_fr.html. Accessed April 21, 2004. 

56. Dzik WH, Corwin H, Goodnough LT, et al. Patient 

safety and blood transfusion: new solutions. Transfus 

Med Rev 2003;17(3);169-80. 

57. Ibbotson RN, Jackson RE. Data capture by the use of 

bar-coding in a blood transfusion centre. Med Lab 

Sci 1980;37:237-42. 

58. Murphy MF, Kay JD. Barcode identification for 

transfusion safety. Curr Opin Hematol 2004;11:334-

8. 

59. Turner CL, Casbard AC, Murphy MF. Barcode 

technology: Its role in increasing the safety of blood 

transfusion. Transfusion 2003;43:1200-9. 

60. Nichols JH, Bartholomew C, Brunton M, et al. 

Reducing medical errors through barcoding at the 

point of care. Clin Leadersh Manag Rev 

2004;18:328-34. 

61. Hughes TW. Automating the purchasing and 

inventory control functions. Am J Hosp Pharm 

1985;42:1101-7. 

62. Longe K. Bar coding in the OR: it's more than just 

inventory control. Mater Manag Health Care 

1995;4(4):40-2. 

63. Nold EG, Williams TC. Bar codes and their potential 

applications in hospital pharmacy. Am J Hosp Pharm 

1985;42:2722-32. 

64. Pape T. Applying airline safety practices to 

medication administration. MEDSURG Nursing 

2003;12(2):77-93. 

65. Johnson CL, Carlson RA, Tucker, CL, et al. Using 

BCMA software to improve patient safety in 

Veterans Administration Medical Centers. J Healthc 

Inf Manag 2002;16(1):46-51. 

66. Coyle GA, Heinen M. Evolution of BCMA within 

the Department of Veterans Affairs. Nurs Adm Q 

2005;29(1):32-8. 

67. Anderson S, Wittwer W. Using bar-code point-ofcare technology for patient safety. J Healthc Qual 

2004;26(6):5-11. 

40 
Patient Safety & Health Information Technology 

68. Low DK, Belcher JV. Reporting medication errors 

through computerized medication administration. 

Comput Inform Nurs 2002:20(5):178-83. 

69. Barry GA, Bass GE Jr, Eddlemon JK, et al. Bar-code 

technology for documenting administration of largevolume intravenous solutions. Am J Hosp Pharm 

1989:46:282-7. 

70. Englebright JD, Franklin M. Managing a new 

medication administration process. J Nurs Adm 

2005;35:410-3. 

71. Patterson ES, Cook RI, Render ML. Improving 

patient safety by identifying side effects from 

introducing bar coding in medication administration. 

J Am Med Inform Assoc 2002;9:540-53. 

72. Hokanson JA, Guernsey BG, Bryant SG, et al. The 

feasibility of barcode-based dispensing quality 

assurance programs. Drug Intell Clin Pharm 

1984;18(1):76-8. 

73. Ringold DJ, Santell JP, Schneider PJ, Arenberg S. 

ASHP national survey of pharmacy practice in acute 

care settings: Prescribing and transcribing—1998. J 

Health Syst Pharm 1999;56(2):142-57. 

74. Patterson ES, Rogers ML, Render ML. Fifteen best 

practice recommendations for bar-code medication 

administration in the Veterans Health Administration. 

Jt Comm J Qual Saf 2004;30:355-65. 

75. Venkatesh V, Davis FD. A theoretical extension of 

the technology acceptance model: four longitudinal 

field studies. Management Science 2000;46(2):186-

204. 

76. Bates DW, Kuperman G, Teich JM. Computerized 

physician order entry and quality of care. Qual 

Manag Health Care 1994;2(4):18-27. 

77. Bates DW, Kuperman GJ, Wang S, et al. Ten 

commandments for effective clinical decision 

support: making the practice of evidence-based 

medicine a reality. J Am Med Inform Assoc 

2003;10:523-30. 

78. Cunningham S, Deere S, Symon A, et al. A 

randomized, controlled trial of computerized 

physiologic trend monitoring in an intensive care 

unit. Crit Care Med 1998;26:2053-60. 

79. Dexter PR, Perkins SM, Maharry KS, et al. Inpatient 

computer-based standing orders vs physician 

reminders to increase influenza and pneumococcal 

vaccination rates: a randomized trial. JAMA 

2004;292:2366-71. 

80. Clarke HF, Bradley C, Whytock S, et al. Pressure 

ulcers: Implementation of evidence-based nursing 

practice. Journal of Advanced Nursing 2005;49:578-

90. 

81. Willson D, Ashton C, Wingate N, et al. 

Computerized support of pressure ulcer prevention 

and treatment protocols. Proc Annu Symp Comput 

Appl Med Care 1995;646-50. 

82. Zielstorff RD, Estey G, Vickery A, et al. Evaluation 

of a decision support system for pressure ulcer 

prevention and management: preliminary findings. 

Proc AMIA Annu Fall Symp 1997;248-52. 

83. Petrucci K, Petrucci P, Canfield K, et al. Evaluation 

of UNIS: Urological Nursing Information Systems. 

Proc Annu Symp Comput Appl Med Care 1991;43-7. 

84. Dale J, Williams S, Foster T, et al. Safety of 

telephone consultation for "non-serious" emergency 

ambulance service patients. Qual Saf Health Care 

2004;13:363-73. 

85. Slovis CM, Carruth TB, Seitz WJ, et al. A priority 

dispatch system for emergency medical services. Ann 

Emerg Med 1985;14:1055-60. 

86. Strachan H, Forster A, Stillie G. The impact of a 

decision support system on the role of the nurse in a 

primary care emergency centre. ITIN 2001;13(1):3-7. 

87. Davidson RA, Fletcher SW, Retchin S, et al. A nurseinitiated reminder system for the periodic health 

examination. Implementation and evaluation. Arch 

Intern Med 1984;144:2167-70. 

88. Ruland CM. Decision support for patient preferencebased care planning: effects on nursing care and 

patient outcomes. J Am Med Inform Assoc 

1999;6:304-12. 

89. Ruland CM. Handheld technology to improve patient 

care: evaluating a support system for preferencebased care planning at the bedside. J Am Med Inform 

Assoc 2002;9(2) 192-201. 

90. Ruland CM, White T, Stevens M, et al. Effects of a 

computerized system to support shared decision 

making in symptom management of cancer patients: 

preliminary results. J Am Med Inform Assoc 

2003;10:573-9. 

91. Patterson R. A computerized reminder for 

prophylaxis of deep vein thrombosis in surgical 

patients. Proc AMIA Symp 1998;573-6. 

41 
Patient Safety and Quality: An Evidence-Based Handbook for Nurses

92. Mosen D, Elliott CG, Egger M J, et al. The effect of a 

computerized reminder system on the prevention of 

postoperative venous thromboembolism. Chest 

2004;125:1635-41. 

93. Kucher N, Koo S, Quiroz R, et al. Electronic alerts to 

prevent venous thromboembolism among 

hospitalized patients. N Engl J Med 2005;352:969-

77. 

94. East TD, Heermann LK, Bradshaw RL, et al. 

Efficacy of computerized decision support for 

mechanical ventilation: results of a prospective multicenter randomized trial. Proc AMIA Symp 1999;251-

5. 

95. McKinley BA, Moore FA, Sailors RM, et al. 

Computerized decision support for mechanical 

ventilation of trauma induced ARDS: Results of a 

randomized clinical trial. J Trauma 2001;50:415-24; 

discussion 425. 

96. Cannon DS, Allen SN. A comparison of the effects of 

computer and manual reminders on compliance with 

a mental health clinical practice guideline. J Am Med 

Inform Assoc 2000;7(2):196-203. 

97. Karfonta TL. Decision support systems in the 

intensive care unit: Nurses’ and physicians’ 

experiences. [unpublished research]. Milwaukee, WI: 

University of Wisconsin; 1999. 

98. Lyons SS, Tripp-Reimer T, Sorofman BA, et al. VA 

QUERI informatics paper: Information technology 

for clinical guideline implementation: Perceptions of 

multidisciplinary stakeholders. J Am Med Inform 

Assoc 2005;12(1):64-71. 

99. Patterson ES, Nguyen AD, Halloran JP, et al. Human 

factors barriers to the effective use of ten HIV 

clinical reminders. J Am Med Inform Assoc 

2004;11(1):50-9. 

100. Hutchison BG. Effect of computer-generated 

nurse/physician reminders on influenza immunization 

among seniors. Fam Med 1989;21:433-7. 

101. Coiera E. Clinical communication: A new 

informatics paradigm. Proc AMIA Annu Fall Symp 

1996;17-21. 

102. Coiera E. When conversation is better than 

computation. J Am Med Inform Assoc 2000;7:277-

86. 

103. Robinson CB. Surveillance and nurses: the use and 

misuse of electronic monitoring. Research for 

Nursing Practice 1999;1(2):8. 

104. Barker KN, Pearson RE, Hepler CD, et al. Effect of 

an automated bedside dispensing machine on 

medication errors. Am J Hosp Pharm 1984;41:1352-

8. 

105. Berg M. Implementing information systems in health 

care organizations: Myths and challenges. Int J Med 

Inform 2001;64(2-3):143-56. 

106. Berg M, Goorman E. The contextual nature of 

medical information. Int J Med Inform 1999;56(1-

3):51-60. 

107. Ash J. Organizational factors that influence 

information technology diffusion in academic health 

sciences centers. J Am Med Inform Assoc 

1997;4(2):102-11. 

108. Berg M. Rationalizing medical work decision support 

techniques and medical practices. Cambridge, MA: 

MIT Press; 1997. 

109. Coiera E. Four rules for the reinvention of health 

care. BMJ 2004;328:1197-9. 

110. Nardi B. Context and consciousness: Activity theory 

and human-computer interaction. New York: MIT 

Press; 1996. 

111. Vallacher R, Wegner D. A theory of action 

identification. New York: Guilford; 1985. 

112. Gigerenzer G, Selton R. Bounded rationality: The 

adaptive toolbox. New York: MIT Press; 2002. 

113. Barnett GO, Winickoff RN, Morgan MM, et al. A 

computer-based monitoring system for follow-up of 

elevated blood pressure. Med Care 1983;21:400-9. 

114. Barton MB, Schoenbaum SC. Improving influenza 

vaccination performance in an HMO setting: The use 

of computer-generated reminders and peer 

comparison feedback. Am J Public Health 

1990;80:534-6. 

115. Coe FL, Norton E, Oparil S, et al. Treatment of 

hypertension by computer and physician—A 

prospective controlled study. J Chronic Dis 

1977;30(2) 81-92. 

116. Murray MD, Harris LE, Overhage JM, et al. Failure 

of computerized treatment suggestions to improve 

health outcomes of outpatients with uncomplicated 

hypertension: Results of a randomized controlled 

trial. Pharmacotherapy 2004;24:324-37. 

117. Rogers JL, Haring OM, Wortman PM, et al. Medical 

information systems: Assessing impact in the areas of 

hypertension, obesity and renal disease. Med Care 

1982;20(1) 63-74. 

42 
Patient Safety & Health Information Technology 

118. Schriger DL. Baraff LJ, Rogers WH, et al. 

Implementation of clinical guidelines using a 

computer charting system. Effect on the initial care of 

health care workers exposed to body fluids. JAMA 

1997;278:1585-90. 

119. Schriger DL, Baraff LJ, Buller K, et al. 

Implementation of clinical guidelines via a computer 

charting system: effect on the care of febrile children 

less than three years of age. J Am Med Inform Assoc 

2000;7(2):186-95. 

120. Tang PC, LaRosa MP, Newcomb C, et al. Measuring 

the effects of reminders for outpatient influenza 

immunizations at the point of clinical opportunity. J 

Am Med Inform Assoc, 1999;6(2):115-21. 

43