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> **AHRQ Quality Indicators**
>
> **Guide to Patient Safety Indicators**
>
> Department of Health and Human Services
>
> Agency for Healthcare Research and Quality
>
> [http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/)
>
> March 2003\
> Version 3.0 (February 20, 2006)

# Preface {#preface .HeadOther}

In health care as in other arenas, that which cannot be measured is
difficult to improve. Providers, consumers, policy makers, and others
seeking to improve the quality of health care need accessible, reliable
indicators of quality that they can use to flag potential problems or
successes; follow trends over time; and identify disparities across
regions, communities, and providers. As noted in a 2001 Institute of
Medicine study, *Envisioning the National Health Care Quality Report*,
it is important that such measures cover not just acute care but
multiple dimensions of care: staying healthy, getting better, living
with illness or disability, and coping with the end of life.

The Agency for Healthcare Research and Quality (AHRQ) Quality Indicators
(QIs) are one Agency response to this need for multidimensional,
accessible quality indicators. They include a family of measures that
providers, policy makers, and researchers can use with inpatient data to
identify apparent variations in the quality of inpatient or outpatient
care. AHRQ's Evidence-Based Practice Center (EPC) at the University of
California San Francisco (UCSF) and Stanford University adapted,
expanded, and refined these indicators based on the original Healthcare
Cost and Utilization Project (HCUP) Quality Indicators developed in the
early 1990s.

The new AHRQ QIs are organized into three modules: **Prevention Quality
Indicators**, **Inpatient Quality Indicators**, and **Patient Safety
Indicators**. AHRQ has published the three modules as a series. The
first module -- Prevention Quality Indicators -- was released in 2001
and the second module -- Inpatient Quality Indicators -- was released in
2002. Both are available at AHRQ's Quality Indicators Web site at
[http://www.qualityindicators.ahrq.gov](http://www.qualityindicators.ahrq.gov/).

This third module focuses on potentially preventable complications and
iatrogenic events for patients treated in hospitals. The Patient Safety
Indicators (PSIs) are measures that screen for adverse events that
patients experience as a result of exposure to the health care system;
these events are likely amenable to prevention by changes at the system
or provider level. The PSIs were initially released in March 2003. The
PSIs now include 20 Provider-level and 7 Area-level Indicators.

Full technical information on the first two modules can be found in
*Refinement of the HCUP Quality Indicators*, prepared by the
UCSF-Stanford EPC. It can be accessed at AHRQ's Quality Indicators Web
site
([[http://www.qualityindicators.ahrq.gov/downloads.htm]{.underline}](http://www.qualityindicators.ahrq.gov/downloads.htm)).
The technical report for the third module, entitled *Measures of Patient
Safety Based on Hospital Administrative Data―The Patient Safety
Indicators*, is also available on AHRQ's Quality Indicators Web site.

Improving patient safety is a critical part of efforts to provide high
quality health care in the United States. This guide is intended to
facilitate such efforts. As always, we would appreciate hearing from
those who use our measures and tools so that we can identify how they
are used, how they can be refined, and how we can measure and improve
the quality of the tools themselves. You may contact us by sending an
e-mail to <support@qualityindicators.ahrq.gov>.

Irene Fraser, Ph.D., Director

Center for Organization and Delivery Studies

# Acknowledgments {#acknowledgments .HeadOther}

  ------------------------ -------------- -------- ------------------------
  Support efforts,                                 
  including refinement and                         
  enhancement of the AHRQ                          
  Quality Indicators and                           
  related products, are                            
  provided by the Support                          
  for Quality                                      
  Indicators-II contract                           
  team.                                            

                                                   

  The following                                    
  individuals from                                 
  **Battelle Memorial                              
  Institute,** **Stanford                          
  University,** and                                
  **University of                                  
  California (UC)**                                
  constitute the Support                           
  for Quality                                      
  Indicators-II core team:                         

                                                   

  Sheryl M. Davies, M.A.   Mark Gritz,             Kathryn M. McDonald,
                           Ph.D.                   M.M.

  Bruce Ellis, M.S.        Greg Hubert,            Patrick Romano, M.D.,
                           B.S.                    M.P.H

  Jeffrey Geppert, J.D.    Elaine Keller,          Jeff Schoenborn, B.S.
                           M.Ed.                   
  ------------------------ -------------- -------- ------------------------

  -------------------------------------- --------------------------------
  The **Agency for Healthcare Research   
  and Quality** Support for Quality      
  Indicators team includes:              

  Marybeth Farquhar, Project Officer     Mary B. Haines, Contract Officer

  Mamatha Pancholi, Project Officer      
  -------------------------------------- --------------------------------

This product is based on the work of many individuals who contributed to
its development and testing.

The following staff from the **Evidence-based Practice Center (EPC) at
UCSF-Stanford** performed the evidence review, completed the empirical
evaluation, and created the programming code and technical documentation
for the AHRQ Quality Indicators:

  ----------------------------------- -----------------------------------
  ***Core Project Team***             

  Kathryn M. McDonald, M.M.           Sheryl M. Davies, M.A. (Stanford)
  (Stanford),                         

  *principal investigator*            Bradford W. Duncan, M.D. (Stanford)

                                      Kaveh G. Shojania, M.D. (UCSF)

  *Investigators*                     

  Patrick S. Romano, M.D., M.P.H.     Angela Hansen, B.A. (Stanford),
  (UC-Davis)                          *EPC*

  Jeffrey Geppert, J.D. (Stanford)    *Research Assistant*

                                      
  ----------------------------------- -----------------------------------

The following staff from **Social & Scientific Systems**, **Inc.**,
developed this software product, documentation, and guide:

  ---------------------------------- ------------------------------------
  ***Programmers***                  ***Technical Writer***

  Leif Karell                        Patricia Burgess

  Kathy McMillan                     

  Fred Rohde                         ***Graphics Designer***

                                     Laura Spofford
  ---------------------------------- ------------------------------------

Contributors from the **Agency for Healthcare Research and Quality**:

  ---------------------------------- ------------------------------------
  Anne Elixhauser, Ph.D.             Marlene Miller, M.D., M.Sc.

  Denise Remus, Ph.D., R.N.          Margaret Coopey, R.N., M.G.A, M.P.S.

  H. Joanna Jiang, Ph.D.             
  ---------------------------------- ------------------------------------

We wish to also acknowledge the following individuals and organizations
for their aid in this report: Doug Staiger, Dept. of Economics,
Dartmouth College; Ros McNally, National Primary Care Research and
Development Centre, University of Manchester; Rita Scichilone and the
American Health Information Management Association; the various
professional organizations that provided nominations for our clinical
review panels; the clinical panelists; the peer reviewers of the
evidence report; and the beta-testers of the software products, all of
whose input was invaluable.

**Table of Contents**

Preface [i](#preface)

Acknowledgments [ii](#acknowledgments)

1.0 Introduction to the AHRQ Patient Safety Indicators
[1](#introduction-to-the-ahrq-patient-safety-indicators)

1.1 What Are the Patient Safety Indicators?
[2](#what-are-the-patient-safety-indicators)

1.2 How Can the PSIs Be Used to Assess Patient Safety?
[4](#how-can-the-psis-be-used-to-assess-patient-safety)

1.3 What Does this Guide Contain? [5](#what-does-this-guide-contain)

1.4 Support for Potential and Current Users of the AHRQ QIs
[5](#support-for-potential-and-current-users-of-the-ahrq-qis)

2.0 Origins and Background of the Quality Indicators
[6](#origins-and-background-of-the-quality-indicators)

2.1 Development of the AHRQ Quality Indicators
[6](#development-of-the-ahrq-quality-indicators)

2.2 AHRQ Quality Indicator Modules [6](#ahrq-quality-indicator-modules)

3.0 Methods of Identifying, Selecting, and Evaluating the Quality
Indicators
[8](#methods-of-identifying-selecting-and-evaluating-the-quality-indicators)

3.1 Step 1: Define the Concepts and the Evaluation Framework
[8](#step-1-define-the-concepts-and-the-evaluation-framework)

3.2 Step 2: Search the Literature to Identify Potential PSIs
[10](#step-2-search-the-literature-to-identify-potential-psis)

3.3 Step 3: Develop a Candidate List of PSIs
[11](#step-3-develop-a-candidate-list-of-psis)

3.4 Step 4: Review the PSIs [13](#step-4-review-the-psis)

3.5 Step 5: Evaluate the PSIs Using Empirical Analysis
[14](#step-5-evaluate-the-psis-using-empirical-analysis)

4.0 Summary Evidence on the Patient Safety Indicators
[16](#summary-evidence-on-the-patient-safety-indicators)

4.1 Limitations in Using the PSIs [22](#limitations-in-using-the-psis)

4.2 Further Research on PSIs [22](#further-research-on-psis)

4.3 Use of External Cause-of-Injury Codes
[23](#use-of-external-cause-of-injury-codes)

5.0 Detailed Evidence for Patient Safety Indicators
[25](#detailed-evidence-for-patient-safety-indicators)

5.1 Complications of Anesthesia (PSI 1)
[26](#complications-of-anesthesia-psi-1)

5.2 Death in Low-Mortality DRGs (PSI 2)
[28](#death-in-low-mortality-drgs-psi-2)

5.3 Decubitus Ulcer (PSI 3) [30](#decubitus-ulcer-psi-3)

5.4 Failure to Rescue (PSI 4) [32](#failure-to-rescue-psi-4)

5.5 Foreign Body Left During Procedure, Provider Level (PSI 5)
[34](#foreign-body-left-during-procedure-provider-level-psi-5)

5.6 Foreign Body Left During Procedure, Area Level (PSI 21)
[34](#foreign-body-left-during-procedure-area-level-psi-21)

5.7 Iatrogenic Pneumothorax, Provider Level (PSI 6)
[36](#iatrogenic-pneumothorax-provider-level-psi-6)

5.8 Iatrogenic Pneumothorax, Area Level (PSI 22)
[36](#iatrogenic-pneumothorax-area-level-psi-22)

5.9 Selected Infections Due to Medical Care, Provider Level (PSI 7)
[38](#selected-infections-due-to-medical-care-provider-level-psi-7)

5.10 Selected Infections Due to Medical Care, Area Level (PSI 23)
[38](#selected-infections-due-to-medical-care-area-level-psi-23)

5.11 Postoperative Hip Fracture (PSI 8)
[40](#postoperative-hip-fracture-psi-8)

5.12 Postoperative Hemorrhage or Hematoma (PSI 9)
[42](#postoperative-hemorrhage-or-hematoma-psi-9)

5.13 Postoperative Hemorrhage or Hematoma (PSI 27)
[42](#postoperative-hemorrhage-or-hematoma-psi-27)

5.14 Postoperative Physiologic and Metabolic Derangement (PSI 10)
[45](#postoperative-physiologic-and-metabolic-derangement-psi-10)

5.15 Postoperative Respiratory Failure (PSI 11)
[47](#postoperative-respiratory-failure-psi-11)

5.16 Postoperative Pulmonary Embolism or Deep Vein Thrombosis (PSI 12)
[49](#postoperative-pulmonary-embolism-or-deep-vein-thrombosis-psi-12)

5.17 Postoperative Sepsis (PSI 13) [51](#postoperative-sepsis-psi-13)

5.18 Postoperative Wound Dehiscence, Provider Level (PSI 14)
[53](#postoperative-wound-dehiscence-provider-level-psi-14)

5.19 Postoperative Wound Dehiscence, Area Level (PSI 24)
[53](#postoperative-wound-dehiscence-area-level-psi-24)

5.20 Accidental Puncture or Laceration, Provider Level (PSI 15)
[55](#accidental-puncture-or-laceration-provider-level-psi-15)

5.21 Accidental Puncture or Laceration, Area Level (PSI 25)
[55](#accidental-puncture-or-laceration-area-level-psi-25)

5.22 Transfusion Reaction, Provider Level (PSI 16)
[57](#transfusion-reaction-provider-level-psi-16)

5.23 Transfusion Reaction, Area Level (PSI 26)
[57](#transfusion-reaction-area-level-psi-26)

5.24 Birth Trauma―Injury to Neonate (PSI 17)
[59](#birth-traumainjury-to-neonate-psi-17)

5.25 Obstetric Trauma―Vaginal Delivery with Instrument (PSI 18)
[61](#obstetric-traumavaginal-delivery-with-instrument-psi-18)

5.26 Obstetric Trauma―Vaginal Delivery without Instrument (PSI 19)
[63](#obstetric-traumavaginal-delivery-without-instrument-psi-19)

5.27 Obstetric Trauma―Cesarean Delivery (PSI 20)
[65](#obstetric-traumacesarean-delivery-psi-20)

6.0 Using Different Types of QI Rates
[67](#using-different-types-of-qi-rates)

7.0 References [68](#references)

Appendix A: Links [A-1](#appendix-a-links)

**List of Tables**

[Table 1: AHRQ Provider-Level Patient Safety Indicators
18](#__RefHeading___Toc128139598)

[Table 2. AHRQ Area Level Patient Safety Indicators
21](#__RefHeading___Toc128139599)

[Table 3: Indicators and Use of External Cause-of-Injury Codes
24](#__RefHeading___Toc128139600)

# Introduction to the AHRQ Patient Safety Indicators

Hospitals in the United States provide the setting for some of life's
most pivotal events---the birth of a child, major surgery, treatment for
otherwise fatal illnesses. These hospitals house the most sophisticated
medical technology in the world and provide state-of-the-art diagnostic
and therapeutic services. But access to these services comes with
certain costs. About 30% of personal health care expenditures in the
United States go towards hospital care,[^1] and the rate of growth in
spending for hospital services has only recently leveled out after
several years of increases following a half a decade of declining
growth.[^2] Simultaneously, concerns about the quality of health care
services have reached a crescendo with the Institute of Medicine's
series of reports describing the problem of medical errors[^3] and the
need for a complete restructuring of the health care system to improve
the quality of care.[^4] Policymakers, employers, and consumers have
made the quality of care in U.S. hospitals a top priority and have
voiced the need to assess, monitor, track, and improve the quality of
inpatient care.

Hospital administrative data offer a window into the medical care
delivered in our nation's hospitals. These data, which are collected as
a routine step in the delivery of hospital services, provide information
on diagnoses, procedures, age, gender, admission source, and discharge
status. From these data elements, it is possible to construct a picture
of the quality of medical care. Although quality assessments based on
administrative data cannot be definitive, they can be used to flag
potential quality problems and success stories, which can then be
further investigated and studied. Hospital associations, individual
hospitals, purchasers, regulators, and policymakers at the local, State,
and Federal levels can use readily available hospital administrative
data to begin the assessment of quality of care. I In 2003, AHRQ first
published the *National Healthcare Quality Report*[^5] (NHQR) and
*National Healthcare Disparities Report*[^6] (NHDR) which provide a
comprehensive picture of the level and variation of quality within four
components of health care quality---effectiveness, safety, timeliness,
and patient centeredness. These reports incorporated many Prevention
Quality Indicators, Inpatient Quality Indicators, and Patient Safety
Indicators. Selected mortality and utilization indicators from the IQI
module will be included in the next NHQR and NHDR reports.[^7]

The AHRQ Quality Indicators are now being used for applications beyond
quality improvement. Some organizations have used the AHRQ Quality
Indicators to produce web based, comparative reports on hospital
quality, such as the Texas Department of State Health Services[^8] and
the Niagara Coalition[^9]. These organizations also supplied users with
guidance on indicator interpretation. Other organizations have
incorporated selected AHRQ QIs into pay for performance demonstration
projects or similar programs, such as the Centers for Medicare and
Medicaid Services (CMS)[^10] and Anthem Blue Cross Blue Shield of
Virginia where hospitals would be financially rewarded for performance.
Guidance on these alternative uses of the AHRQ QIs is summarized in an
AHRQ *Summary Statement on Comparative Reporting*[^11] and accompanying
publication titled G*uidance for Using the AHRQ Quality Indicators for
Hospital-Level Public Reporting or Payment* [^12].

The Agency for Healthcare Research and Quality (AHRQ) Patient Safety
Indicators (PSIs) are a tool that takes advantage of hospital
administrative data. The PSIs represent the current state-of-the-art in
measuring the safety of hospital care through analysis of inpatient
discharge data.

This update of the AHRQ Patient Safety Indicators (PSIs) (Version 3.0)
incorporates updates to the ICD-9-CM and DRG codes for FY2006. In
addition, the Census and empirical data used in the risk-adjustment have
been updated to the most recent data available.

New micropolitan statistical areas and updated metropolitan statistical
areas were established by the federal Office of Management and Budget
(OMB) circular 03-04 (last revised December 4, 2005). To reflect these
changes, all PSI documentation now refers to Metro Area instead of MSA.
The SAS and SPSS software allows users to specify stratification by
county level with U.S. Census FIPS or modified FIPS, or by Metro Area
with OMB 1999 or OMB 2003 definition. The AHRQ QI Windows Application
allows users to generate reports stratified by all four of these, as
well as by State.

See the section \"What Does this Guide Contain?\" for more information.

## What Are the Patient Safety Indicators?

The PSIs are a set of measures that can be used with hospital inpatient
discharge data to provide a perspective on patient safety. Specifically,
PSIs screen for problems that patients experience as a result of
exposure to the healthcare system and that are likely amenable to
prevention by changes at the system or provider level. These are
referred to as complications or adverse events. PSIs are defined on two
levels: the provider level and the area level.

-   *Provider-level Indicators* provide a measure of the potentially
    preventable complication for patients who received their initial
    care and the complication of care within the same hospitalization.
    Provider-level Indicators include only those cases where a secondary
    diagnosis code flags a potentially preventable complication.

-   *Area-level Indicators* capture all cases of the potentially
    preventable complication that occur in a given area (e.g.,
    metropolitan service area or county) either during hospitalization
    or resulting in subsequent hospitalization. Area-level Indicators
    are specified to include principal diagnosis, as well as secondary
    diagnoses, for the complications of care. This specification adds
    cases where a patient's risk of the complication occurred in a
    separate hospitalization.

Three PSIs, 27 through 29, that measured 3^rd^-degree obstetric trauma
have been removed. A new area-level PSI, Postoperative Hemorrhage or
Hematoma, has been added as PSI #27.

The PSIs include the following Provider-level Indicators:

  ---------------------------------------------------------- ------------
  **Patient Safety Indicators - Provider**                   **PSI
                                                             Number**

  Complications of Anesthesia                                1

  Death in Low-Mortality DRGs                                2

  Decubitus Ulcer                                            3

  Failure to Rescue                                          4

  Foreign Body Left During Procedure                         5

  Iatrogenic Pneumothorax                                    6

  Selected Infections Due to Medical Care                    7

  Postoperative Hip Fracture                                 8

  Postoperative Hemorrhage or Hematoma                       9

  Postoperative Physiologic and Metabolic Derangements       10

  Postoperative Respiratory Failure                          11

  Postoperative Pulmonary Embolism or Deep Vein Thrombosis   12

  Postoperative Sepsis                                       13

  Postoperative Wound Dehiscence                             14

  Accidental Puncture or Laceration                          15

  Transfusion Reaction                                       16

  Birth Trauma -- Injury to Neonate                          17

  Obstetric Trauma -- Vaginal with Instrument                18

  Obstetric Trauma -- Vaginal without Instrument             19

  Obstetric Trauma -- Cesarean Delivery                      20
  ---------------------------------------------------------- ------------

In addition, the following PSIs were modified into Area-level Indicators
to assess the total incidence of the adverse event within geographic
areas:

  ---------------------------------------------------------- ------------
  **Patient Safety Indicators - Area**                       **PSI
                                                             Number**

  Foreign Body Left During Procedure                         21

  Iatrogenic Pneumothorax                                    22

  Selected Infections Due to Medical Care                    23

  Postoperative Wound Dehiscence                             24

  Accidental Puncture or Laceration                          25

  Transfusion Reaction                                       26

  Postoperative Hemorrhage or Hematoma                       27
  ---------------------------------------------------------- ------------

## How Can the PSIs Be Used to Assess Patient Safety?

Widespread consensus exists that health care organizations can reduce
patient injuries by improving the environment for safety―from
implementing technical changes, such as electronic medical record
systems, to improving staff awareness of patient safety risks. Clinical
process interventions also have strong evidence for reducing the risk of
adverse events related to a patient's exposure to hospital care.^2^
PSIs, which are based on computerized hospital discharge abstracts from
the AHRQ's Healthcare Cost and Utilization Project (HCUP), can be used
to better prioritize and evaluate local and national initiatives.
Analyses of these and similar inexpensive, readily available
administrative data sets may provide a screen for potential medical
errors and a method for monitoring trends over time. The following
scenario illustrates one potential application of the PSIs.

+-----------------------------------------------------------------------+
| Evaluating and Improving Quality of Care                              |
|                                                                       |
| A hospital association recognizes its member hospitals' need for      |
| information that can help them evaluate the quality of care they      |
| provide. There is significant interest in assessing, monitoring, and  |
| improving the safety of inpatient care. After learning about the AHRQ |
| PSIs, the association decides to apply the indicators to the          |
| discharge abstract data submitted by individual hospitals. For each   |
| hospital, the association develops a report with graphic presentation |
| of the risk-adjusted data to show how the hospital performs on each   |
| indicator compared to its peer group, the State as a whole, and other |
| comparable States. National and regional averages from the AHRQ       |
| Healthcare Cost and Utilization Project (HCUP) database are also      |
| provided as additional external benchmarks. Three years of trend data |
| are included to allow the hospital to examine any changing patterns   |
| in its performance.                                                   |
|                                                                       |
| One member hospital, upon receiving the report, convenes an internal  |
| work group comprised of clinicians and quality improvement            |
| professionals to review the information and identify potential areas  |
| for improvement. The hospital leadership is committed to performance  |
| excellence and providing a culture supportive of systems evaluation   |
| and redesign. To begin their evaluation, they apply the AHRQ software |
| to their internal administrative data to distinguish those patients   |
| who experienced the complication or adverse event from those who did  |
| not. This step establishes the focus for chart review.                |
|                                                                       |
| After the initial analysis of the administrative and clinical data,   |
| the work group meets with clinical departments involved in care of    |
| these patients. They begin an in-depth analysis of the system and     |
| processes of care. Through application of process improvement         |
| concepts, they begin to identify opportunities for improvement. After |
| selection of their priority area (for example, reduction of           |
| postoperative complications), they begin work, including:             |
|                                                                       |
| -   Review and synthesize the evidence base and best practices from   |
|     scientific literature.                                            |
|                                                                       |
| -   Work with the multiple disciplines and departments involved in    |
|     care of surgical patients to redesign care based on best          |
|     practices with an emphasis on coordination and collaboration.     |
|                                                                       |
| -   Evaluate information technology solutions.                        |
|                                                                       |
| -   Implement performance measurements for improvement and            |
|     accountability.                                                   |
|                                                                       |
| -   Incorporate monitoring of performance measurements in the         |
|     departmental and senior leadership meetings and include in the    |
|     Board quality improvement reports.                                |
+-----------------------------------------------------------------------+

## What Does this Guide Contain?

This guide provides information that hospitals, State data
organizations, hospital associations, and others can use to decide how
to use the PSIs. First, it describes the origin of the entire family of
AHRQ Quality Indicators. Second, it provides an overview of the methods
used to identify, select, and evaluate the AHRQ PSIs. Third, the guide
summarizes the PSIs specifically, describes strengths and limitations of
the indicators, documents the evidence that links the PSIs to the
quality of health care services, and then provides in-depth descriptions
of each PSI.

The list of detailed definitions that was contained in Appendix A in
previous versions has been removed from this Guide and is now available
as a separate document, *Patient Safety Indicators Technical
Specifications*. That document also incorporates the list of operating
room procedure codes that was previously a separate document. [Appendix
A](#appendix-a-links) now contains links to documents and tools that may
be of interest to PSI users.

The \"Detailed Methods\" information that was previously in Appendix B
has been removed. A new section, \"Using Different Types of QI Rates,\"
has been added.

The list of major operating room ICD-9-CM procedure codes now contained
in the *Patient Safety Indicators Technical Specifications* document is
based on the AHRQ Procedure Classes that assign all ICD-9-CM procedure
codes to one of four categories:

-   Minor Diagnostic - Non-operating room procedures that are diagnostic
    (e.g., 87.03 CT scan of head)

-   Minor Therapeutic - Non-operating room procedures that are
    therapeutic (e.g., 02.41 Irrigate ventricular shunt)

-   Major Diagnostic - All procedures considered valid operating room
    procedures by the Diagnosis Related Group (DRG) grouper and that are
    performed for diagnostic reasons (e.g., 01.14 Open brain biopsy)

-   Major Therapeutic - All procedures considered valid operating room
    procedures by the Diagnosis Related Group (DRG) grouper and that are
    performed for therapeutic reasons (e.g., 39.24 Aorta-renal bypass).

For the AHRQ PSIs, major operating room procedures are ICD-9-CM
procedure codes in categories #3 (major diagnostic) and #4 (major
therapeutic).

## Support for Potential and Current Users of the AHRQ QIs

Technical assistance is available, through an electronic user support
system monitored by the QI support team, to support users in their
application of the PSI software. The same e-mail address may be used to
communicate to AHRQ any suggestions for PSI enhancements, general
questions, and any QI related comments you may have. AHRQ welcomes your
feedback. The Internet address for user support and feedback is:
<support@qualityindicators.ahrq.gov>. AHRQ also offers a listserv to
keep you informed on the Quality Indicators (QIs). The listserv is used
to announce any QI changes or updates, new tools and resources, and to
distribute other QI related information. This is a free service. Sign-up
information is available at the QI website at
<http://www.qualityindicators.ahrq.gov/signup.htm>.

# Origins and Background of the Quality Indicators

In the early 1990s, in response to requests for assistance from
State-level data organizations and hospital associations with inpatient
data collection systems, AHRQ developed a set of quality measures that
required only the type of information found in routine hospital
administrative data---diagnoses and major procedures, along with
information on patient's age, gender, source of admission, and discharge
status. These States were part of the Healthcare Cost and Utilization
Project, an ongoing Federal-State-private sector collaboration to build
uniform databases from administrative hospital-based data.

AHRQ developed these measures, called the HCUP Quality Indicators, to
take advantage of a readily available data source---administrative data
based on hospital claims---and quality measures that had been reported
elsewhere.[^13] The 33 HCUP QIs included measures for avoidable adverse
outcomes, such as in-hospital mortality and complications of procedures;
use of specific inpatient procedures thought to be overused, underused,
or misused; and ambulatory care sensitive conditions.

Although administrative data cannot provide definitive measures of
health care quality, they can be used to provide *indicators* of health
care quality that can serve as the starting point for further
investigation. The HCUP QIs have been used to assess potential
quality-of-care problems and to delineate approaches for dealing with
those problems. Hospitals with high rates of poor outcomes on the HCUP
QIs have reviewed medical records to verify the presence of those
outcomes and to investigate potential quality-of-care problems.[^14] For
example, one hospital that detected high utilization rates for certain
procedures refined patient selection criteria for these procedures to
improve appropriate utilization.

## Development of the AHRQ Quality Indicators 

Since the original development of the HCUP QIs, the knowledge base on
quality indicators has increased significantly. Risk-adjustment methods
have become more readily available, new measures have been developed,
and analytic capacity at the State level has expanded considerably.
Based on input from current users and advances to the scientific base
for specific indicators, AHRQ funded a project to refine and further
develop the original QIs. The project was conducted by the UCSF-Stanford
EPC.

The major constraint placed on the UCSF-Stanford EPC was that the
measures could require only the type of information found in hospital
discharge abstract data. Further, the data elements required by the
measures had to be available from most inpatient administrative data
systems. Some State data systems contain innovative data elements, often
based on additional information from the medical record. Despite the
value of these record-based data elements, the intent of this project
was to create measures that were based on a *common denominator
discharge data set*, without the need for additional data collection.
This was critical for two reasons. First, this constraint would result
in a tool that could be used with any inpatient administrative data,
thus making it useful to most data systems. Second, this would enable
national and regional benchmark rates to be provided using HCUP data,
since these benchmark rates would need to be calculated using the
universe of data available from the States.

## AHRQ Quality Indicator Modules 

The work of the UCSF-Stanford EPC resulted in the *AHRQ Quality
Indicators*, which are available as separate modules:

-   **Prevention Quality Indicators**. These indicators consist of
    "ambulatory care sensitive conditions," hospital admissions that
    evidence suggests could have been avoided through high-quality
    outpatient care or that reflect conditions that could be less
    severe, if treated early and appropriately.

-   **Inpatient Quality Indicators**. These indicators reflect quality
    of care inside hospitals and include inpatient mortality;
    utilization of procedures for which there are questions of overuse,
    underuse, or misuse; and volume of procedures for which there is
    evidence that a higher volume of procedures is associated with lower
    mortality.

-   **Patient Safety Indicators**. These indicators focus on potentially
    preventable instances of complications and other iatrogenic events
    resulting from exposure to the health care system.

-   **Pediatric Quality Indicators.** This module, available in January,
    2006, contains indicators that apply to the special characteristics
    of the pediatric population.

The core of the Pediatric Quality Indicators (PedQIs) is formed by
indicators drawn from the original three modules. Some of these
indicators were already geared to the pediatric population (for example,
IQI 4 -- Pediatric Heart Surgery Volume). These indicators are being
removed from the original modules.

Others were adapted from indicators that apply to both adult and
pediatric populations. These indicators remain in the original module,
but will apply only to adult populations.

# Methods of Identifying, Selecting, and Evaluating the Quality Indicators

Since the literature surrounding PSIs is sparse, the project team used a
variety of additional techniques to identify, select, and evaluate each
indicator, including clinician panels, expert coders, and empirical
analyses.

## Step 1: Define the Concepts and the Evaluation Framework

In approaching the task of evaluating patient safety indicators based on
administrative data, the project team developed a conceptual framework
and standardized definitions of commonly used terms.

### Standardized Definitions

In the literature, the distinctions between medical error, adverse
events, complications of care, and other terms pertinent to patient
safety are not well established and are often used interchangeably. In
this report, the terms medical error, adverse events or complications,
and similar concepts are defined as follows:

> **Case finding indicators.** Indicators for which the primary purpose
> is to identify specific cases in which a medical error *may* have
> occurred, for further investigation.
>
> **Complication or adverse event.** "An injury caused by medical
> management rather than by the underlying disease or condition of the
> patient."[^15] In general, adverse events prolong the hospitalization,
> produce a disability at the time of discharge, or both. Used in this
> report, complication does not refer to the sequelae of diseases, such
> as neuropathy as a "complication" of diabetes. Throughout the report,
> "sequelae" is used to refer to these conditions.
>
> **Medical error.** "The failure of a planned action to be completed as
> intended (i.e., error of execution) or the use of a wrong plan to
> achieve an aim (i.e., error of planning)." The definition includes
> errors committed by any individual, or set of individuals, working in
> a health care organization.[^16]
>
> **Patient safety.** "Freedom from accidental injury," or "avoiding
> injuries or harm to patients from care that is intended to help them."
> Ensuring patient safety "involves the establishment of operational
> systems and processes that minimize the likelihood of errors and
> maximizes the likelihood of intercepting them when they occur."[^17]
>
> **Patient safety indicators.** Specific quality indicators which also
> reflect the quality of care inside hospitals, but focus on aspects of
> patient safety. Specifically, PSIs screen for problems that patients
> experience as a result of exposure to the healthcare system, and that
> are likely amenable to prevention by changes at the system or provider
> level.
>
> **Preventable adverse event.** An adverse event attributable to error
> is a "preventable adverse event."[^18] A condition for which
> reasonable steps may reduce (but not necessarily eliminate) the risk
> of that complication occurring.
>
> **Quality.** "Quality of care is the degree to which health services
> for individuals and populations increase the likelihood of desired
> health outcomes and are consistent with current professional
> knowledge." In this definition, "the term *health services* refers to
> a wide array of services that affect health...(and) applies to many
> types of health care practitioners (physicians, nurses, and various
> other health professionals) and to all settings of care..."[^19]
>
> **Quality indicators.** Screening tools for the purpose of identifying
> potential areas of concern regarding the quality of clinical care. For
> the purpose of this report, we focus on indicators that reflect the
> quality of care inside hospitals. Quality indicators may assess any of
> the four system components of health care quality, including patient
> safety (see below), effectiveness (i.e., "providing services based on
> scientific knowledge to all who could benefit, and refraining from
> providing services to those not likely to benefit), patient
> centeredness, and timeliness (i.e., "minimizing unnecessary
> delays\").[^20]
>
> **Rate based indicators.** Indicators for which the primary purpose is
> to identify the rate of a complication rather than to identify
> specific cases.

While the definitions above are intended to distinguish events that are
less preventable from those that are more preventable, the difference is
best described as a spectrum. To conceptualize this spectrum, the
project team developed the following three categories of conditions:

1.  **Conditions that could be either a comorbidity or a complication.**
    Conditions considered comorbidities (for example, congestive heart
    failure) are present on admission and are not caused by medical
    management; rather, they are due to the patient's underlying
    disease. It is extremely difficult to distinguish complications from
    comorbidities for these conditions using administrative data. As a
    result, these conditions were not considered in this report.

2.  **Conditions that are likely to reflect medical error.** These
    conditions (for example, foreign body accidentally left during a
    procedure) are likely to have been caused by medical error. Most of
    these conditions appear infrequently in administrative data, and
    thus rates of events lack the precision to allow for comparisons
    between providers. However, these conditions may be the subject of
    case-finding indicators.

3.  **Conditions that conceivably, but not definitively reflect medical
    error.** These conditions (for example, postoperative DVT or PE)
    represent a spectrum of preventability between the previous two
    categories―from those that are mostly unpreventable to those that
    are mostly preventable. Because of the uncertainty regarding the
    preventability of these conditions and the likely heterogeneity of
    cases with the condition, indicators using these conditions are less
    useful as case-finding indicators. However, examining the rate of
    these conditions may highlight potential areas of concern.

### Evaluation Framework

To evaluate the soundness of each indicator, the project team applied
the same framework as was applied in the technical report[^21] for the
Prevention Quality Indicators (PQIs) and Inpatient Quality Indicators
(IQIs), available at
<http://www.qualityindicators.ahrq.gov/downloads.htm>. This included six
areas of evidence:

-   **Face validity.** Does the indicator capture an aspect of quality
    that is widely regarded as important and subject to provider or
    public health system control? Consensual validity expands face
    validity beyond one person to the opinion of a panel of experts.

-   **Precision.** Is there a substantial amount of provider- or
    community-level variation that is not attributable to random
    variation?

-   **Minimum bias.** Is there either little effect on the indicator of
    variations in patient disease severity and comorbidities, or is it
    possible to apply risk adjustment and statistical methods to remove
    most or all bias?

-   **Construct validity.** Does the indicator perform well in
    identifying true (or actual) quality of care problems?

-   **Fosters real quality improvement.** Is the indicator insulated
    from perverse incentives for providers to improve their reported
    performance by avoiding difficult or complex cases, or by other
    responses that do not improve quality of care?

-   **Applicati**o**n.** Has the measure been used effectively in
    practice? Does it have potential for working well with other
    indicators?

Face validity (consensual validity) was evaluated using a structured
panel review, minimum bias was explored empirically and briefly during
the panel review, and construct validity was evaluated using the limited
literature available. A full discussion of this framework is available
in the Stanford Technical report[^22] available at
<http://www.qualityindicators.ahrq.gov/downloads.htm>.

The relative importance of each of these evaluation areas may differ by
individual PSIs. Precision and minimum bias may be less important for
indicators that are primarily designed to screen only for medical error,
since these events are relatively rare. In general, these indicators are
better used as case-finding indicators. For these indicators,
comparisons between rates are less relevant. However, for rate-based
indicators, concerns of precision and minimum bias remain if indicators
are used in any comparison of rates (comparison to national averages,
peer group, etc.).

## Step 2: Search the Literature to Identify Potential PSIs

The literature searches performed in connection with assessing potential
AHRQ QIs[^23] identified many references relevant to potential PSIs. In
addition, the project team performed electronic searches for articles
published before February 2002 followed by hand searching the
bibliographies of identified references. Members of the project team
were queried to supplement this list, based on their personal knowledge
of recent work in the field. Because Iezzoni et al.'s Complications
Screening Program (CSP)[^24] included numerous candidate indicators, the
team also performed an author search using her name. Forthcoming
articles and Federal reports in press, but not published, were also
included when identified through personal contacts.

The project team identified 326 articles from the Medline search.
Articles were screened using both the titles and abstracts. To qualify
for abstraction, an article must have described, evaluated, or validated
a potential indicator of medical errors, patient safety, or potentially
preventable complications based on International Classification for
Diseases - Ninth Revision - Clinical Modifications (ICD-9-CM) coded
administrative (hospital discharge or claims) data. Some indicators were
also considered if they appeared to be readily translated into ICD-9-CM,
even if the original authors did not use ICD-9-CM codes.

This search was adapted slightly and repeated using the OVID interface
with EMBASE[^25], limited to articles published from January 1990
through the end of first quarter 2002. The EMBASE search identified 463
references, and these articles were screened in the same manner. After
elimination of articles that had already been identified using
Medline[^26] and the other approaches described above, only nine
additional articles met the criteria for abstraction.

## Step 3: Develop a Candidate List of PSIs

The project team developed a candidate list of PSIs by first reviewing
the literature, then selecting a subset of indicators to undergo face
validity testing by clinician panels.

### Candidate List of PSIs

The literature search located relatively few patient safety indicators
that could be defined using unlinked administrative data. The majority
of these indicators were from the Complications Screening Program
(CSP),[^27] which was developed to identify potentially preventable
complications of adult medical and surgical hospital care using commonly
available administrative data. The algorithm uses discharge abstract
data―specifically ICD-9-CM diagnosis and procedure codes, patient age,
sex, diagnosis-related group (DRG), and date of procedure―to identify 28
complications that raise concern about the quality of care based on the
rate of such occurrences at individual hospitals. Each of the
complications is applied to some or all of the following specified "risk
pools" separately: major surgery, minor surgery, invasive cardiac
procedure, endoscopy, medical patients, and all patients. In addition,
specified inclusion and exclusion criteria are applied to each
complication to ensure that the complication developed in-hospital, as
opposed to being present on admission, and that the complication was
potentially preventable.

Four later studies were designed to test criterion and construct
validity by validating the data used to construct CSP screens,
validating the screens as a flag for actual quality problems, and
validating the replicability of hospital-level results using different
data sources.[^28] [^29] [^30] [^31] These studies raised concerns about
the validity of the CSP, because flagged cases for most indicators were
no more likely than unflagged controls to have suffered explicit process
failures.

The project team also reviewed all ICD-9-CM codes implemented in or
before 1999 that were identified by AHRQ as possibly describing medical
errors or reflecting the consequences of such errors.[^32] (This initial
set of indicators is referred to as the Miller et al. indicators.) The
project team added relevant codes from the 2000 and 2001 revisions of
ICD-9-CM and selected codes from the CSP, such as those not clearly
reflective of medical error, but representing a potentially preventable
complication. This process was guided principally by conceptual
considerations. For example, codes for postoperative AMI (an evaluated
indicator that was not included in the final indicator set) were
included in the evaluation set since recent evidence suggests that AMI
is a potentially preventable complication.[^33] A few codes were also
deleted from the initial list based on a review of ICD-9-CM coding
guidelines, described in *Coding Clinics for ICD-9-CM* and the *American
Hospital Association's ICD-9-CM Coding Handbook*. For example, the code
2593 for hypoglycemic coma specifically excludes patients with diabetes
mellitus, the population for which this complication is most
preventable. This process of updating the Miller et al. PSIs resulted in
a list of over 200 ICD-9-CM codes (valid in 2001) potentially related to
medical error.

Codes identified in the CSP and updated from the Miller et. al. PSIs
were then grouped into indicators. Where feasible, codes were compiled
as they were in the CSP, or in some cases the Miller et al. PSIs,
depending on which grouping yielded more clinically homogeneous groups.
In most cases the resulting indicators were not identical to the CSP
indicators, although they were closely related, as some of the specific
codes included in the original CSP had been eliminated after the team's
review of coding guidelines. The remaining codes were then incorporated
into the most appropriate CSP-based indicator, or were grouped into
clinically meaningful concepts to define novel indicators. Exclusion
criteria were added based on CSP methods and clinical judgment. As a
result, over 40 patient safety indicators were defined that, while
building on prior work, reflected significantly changed measures to
focus more narrowly on the most preventable complications.

Indicators were defined with both a numerator (complication of interest)
and a denominator (population at risk). Different patient subpopulations
have inherently different risks for developing a complication, with some
patients having almost no risk. Thus, the denominator for each indicator
represents the specific population at risk. The intention was to
restrict the complication (and consequently the rate) to a more
homogeneous population who are actually at risk for that complication.
In general, the population at risk corresponded to one risk pool (e.g.,
major surgery) from the CSP, if applicable, or was defined more
narrowly.

### Subset Selection

After the project team developed a list of potential indicators, they
selected a subset of indicators to undergo face validity testing by
clinician panels, as described in Step 4. Two sources of information
guided the selection process.

First, validation data from previous studies were reviewed and
thresholds were set for retaining CSP-based indicators. Four studies
were identified that evaluated the CSP indicators. Three of these
studies, examined the predictive value of each indicator in identifying
a complication that occurred in-hospital, regardless of whether this
complication was due to medical error or was preventable. [^34] [^35]
[^36] In a fourth study, nurses identified specific process failures
that may have contributed to complications. In order to be retained as a
potential PSI, at least one of the first three studies needed to
demonstrate a positive predictive value of at least 75%, meaning that 3
out of 4 patients identified by the measure did indeed have the
complication of interest.[^37] In addition, the positive predictive
value of a \"process failure\" identified in the fourth study needed to
reach or exceed 46%, which was the average rate for surgical cases that
were not flagged by any of the CSP indicators. As a result, only
CSP-derived indicators that were at least somewhat predictive of
objectively defined process failures or medical errors were retained.

Second, specific changes to previous definitions or constructs of
indicators fell into the following general categories:

-   Changes to the denominator definitions (inclusion or exclusion
    criteria), intended to reduce bias due to the inclusion of atypical
    patients or to improve generalizability to a broader set of patients
    at risk.

-   Elimination of selected ICD-9-CM codes from numerator definitions,
    intended to focus attention on more clinically significant
    complications or complications more likely to result from medical
    errors.

-   Addition of selected ICD-9-CM codes to numerator definitions,
    intended to capture related complications that could result from the
    same or similar medical errors.

-   Division of a single indicator into two or more related indicators,
    intended to create more clinically meaningful and conceptually
    coherent indicators.

-   Stratification or adjustment by relevant patient characteristics,
    intended to reflect fundamental clinical differences among
    procedures (e.g., vaginal delivery with or without instrumentation)
    and the complications that result from them, or fundamental
    differences in patient risk (e.g., decubitus ulcer in lower-risk
    versus high-risk patients).

A total of 34 indicators, intended to be applied to all age groups, were
retained for face validity testing by clinician panels. Because the
primary intent in developing these indicators was to detect potentially
preventable complications related to health care exposure, the final
definitions for this set of indicators represented mostly new measures
that built upon previous work.

### Coding Review

Experts in ICD-9-CM codes reviewed each code for accuracy of capturing
the complication and population at risk. In some cases, additional codes
or other refinements to the indicators were suggested based on current
coding guidelines.

## Step 4: Review the PSIs

The project team conducted a structured review of each indicator to
evaluate the face validity (from a clinical perspective) of the
indicators. The methodology for the structured review was adapted from
the RAND/UCLA Appropriateness Method[^38] and consisted of an initial
independent assessment of each indicator by clinician panelists using an
initial questionnaire, a conference call among all panelists, followed
by a final independent assessment by clinician panelists using the same
questionnaire. The review sought to establish *consensual validity,*
which "extends face validity from one expert to a panel of experts who
examine and rate the appropriateness of each item...."[^39] The panel
process served to refine definitions of some indicators, add new
measures, and dismiss indicators with major concerns from further
consideration.

Eight panels were formed: two panels examined complications of medical
care indicators, three panels examined surgical complications
indicators, one panel assessed indicators related to procedural
complications, and two panels examined obstetric complications
indicators.

Fifteen professional clinical organizations nominated a total of 162
clinicians to be panelists. To be eligible to participate, nominees were
required to spend at least 30% of their work time on patient care,
including hospitalized patients. Nominees were asked to provide
information regarding their practice characteristics, including
specialty, subspecialty, and setting. Fifty-seven panelists were
selected to ensure that each panel had diverse membership in terms of
practice characteristics and setting.

### Initial Assessment of the Indicators

Panelists were presented with four or five indicators, including the
standardized text used to describe each ICD-9-CM code, the specific
numeric code, exclusion and inclusion criteria, the clinical rationale
for the indicator, and the specification criteria. For each indicator,
panelists completed a 10-item questionnaire that evaluated the ability
of the indicator to screen out conditions present on admission, the
potential preventability of the complication, and the ability of the
indicator to identify medical error. In addition, the questionnaire
asked panelists to consider potential bias, reporting or charting
problems, potential for gaming the indicator, and adverse effects of
implementing the indicator. Finally, the questionnaire provided an
opportunity for panelists to suggest changes to the indicator.

### Conference Call Participation

After the panelists submitted the initial evaluation questionnaires,
they participated in a 90-minute conference call for their panel to
discuss the indicators. In general, agenda items for the conference call
focused on points of disagreement among panelists. However, panelists
were explicitly told that consensus was not the goal of discussion. In
some cases, panelists agreed on proposed changes to the indicator
definitions, and such consensus was noted and the definition was
modified accordingly before the final round of rating.

Panelists were prompted throughout the process to consider the
appropriate population at risk for each indicator (specifically
inclusion and exclusion criteria) in addition to the complication of
interest. However, if panelists wished to discuss other aspects of the
indicator, this discussion was allowed within the time allotted for that
indicator (approximately 15 minutes). If time remained at the end of a
call, topics that were not fully addressed previously were revisited.

### Final Evaluation and Tabulation of Results

Following each conference call, the project team made changes to each
indicator suggested by panelists for changes that reached near consensus
of the panelists. The indicators were then redistributed to panelists
with the questionnaires used in the initial evaluation. The reason for
all each indicator definition change was included, and panelists were
asked to re-rate the indicator based on their current opinion. They were
asked to keep in mind the discussion during the conference call.

Results from the final evaluation questionnaire were used to calculate
median scores from the 9-point scale for each question and to categorize
the degree of agreement among panelists. Median scores determined the
level of acceptability of the indicator, and dispersion of ratings
across the panel for each applicable question determined the agreement
status. Therefore the median and agreement status were independent
measurements for each question. Six criteria were used to identify the
panel opinions (i.e., median, agreement status category) on the
following aspects of the indicator:

-   Overall usefulness of the indicator.

-   Likelihood that the indicator measures a complication and not a
    comorbidity (specifically, present on admission).

-   Preventability of the complication.

-   Extent to which the complication is due to medical error.

-   Likelihood that the complication is charted given that it occurs.

-   Extent that the indicator is subject to bias (systematic
    differences, such as case mix that could affect the indicator, in a
    way not related to quality of care).

The project team used the ratings of the overall appropriateness of each
indicator to assess its overall usefulness as a screen for potential
patient safety problems. Indicators were triaged into three sets:
Accepted Indicators (described in this guide), Experimental Indicators,
and Rejected Indicators.

## Step 5: Evaluate the PSIs Using Empirical Analysis

The project team conducted empirical analyses to explore the frequency
and variation of the indicators, the potential bias, based on limited
risk adjustment, and the relationship between indicators. The data
sources used in the empirical analyses were the 1997 Florida State
Inpatient Database (SID) for initial testing and development and the
1997 HCUP State Inpatient Database for 19 States (referred to in this
guide as the HCUP SID) for the final empirical analyses.

The rates presented in the Detailed Evidence Section of this guide, as
well as the means and parameter reference files used by the PSI
software, reflect analyses of the 2003 SID from 38 states.[^40]

All potential indicators were examined empirically by developing and
conducting statistical tests for precision, bias, and relatedness of
indicators. Three different estimates of hospital performance were
calculated for each indicator:

1.  The raw indicator rate was calculated using the number of adverse
    events in the numerator divided by the number of discharges in the
    population at risk by hospital.

2.  The raw indicator was adjusted to account for differences among
    hospitals in age, gender, modified DRG, and comorbidities.

-   Adjacent DRG categories that were separated by the presence or
    absence of comorbidities or complications were collapsed to avoid
    adjusting for the complication being measured. Most of the
    super-Major Diagnostic Category (MDC) DRG categories were excluded
    for the same reason.

-   APR-DRG risk adjustment was not implemented because removing
    applicable complications from each indicator was beyond the scope of
    this project.

-   The ICD-9-CM codes used to define comorbidity categories were
    modified to exclude conditions likely to represent potentially
    preventable complications in certain settings.

-   "Acute on chronic" comorbidities were captured so that some patients
    with especially severe comorbidities would not be mislabeled as not
    having conditions of interest.

-   Comorbidities in obstetric patients were added.

3.  Multivariate signal extraction methods were applied to adjust for
    reliability by estimating the amount of "noise" (i.e., variation due
    to random error) relative to the amount of "signal" (i.e.,
    systematic variation in hospital performance or reliability) for
    each indicator.

Similar reliability adjustment has been used in the literature for
similar purposes.[^41] [^42] The project team constructed a set of
statistical tests to examine precision, bias, and relatedness of
indicators for all accepted Provider-level Indicators, and precision and
bias for all accepted Area-level Indicators. It should be noted that
rates based on fewer than 30 cases in the numerator or the denominator
are not reported. This exclusion rule serves two purposes:

-   It eliminates unstable estimates based on too few cases.

-   It helps protect the identities of hospitals and patients.

# Summary Evidence on the Patient Safety Indicators

This project took a four-pronged approach to the identification,
development, and evaluation of PSIs that included use of literature,
clinician panels, expert coders, and empirical analyses. The literature
review and the findings from the clinical panels combined with data
analysis provide evidence to suggest that a number of discharge-based
PSIs may be useful screens for organizations, purchasers, and
policymakers to identify safety problems at the provider level, as well
as to document systematic area-level differences in patient safety
problems.

Most adverse events identified by the PSIs have a variety of causes in
addition to potential medical error leading to the adverse event,
including underlying patient health and factors that do not vary
systematically. Clinician panelists rated only two of the accepted
indicators as very likely to reflect medical error: (1) transfusion
reaction and (2) foreign body left in during a procedure. These
indicators proved to be very rare, with less than 1 per 10,000 cases at
risk.

Table 1 summarizes the results of the literature review, clinician
panels, and empirical analyses on the provider-level PSIs. Table 2
provides the same information for the area-level PSIs. The tables list
each indicator, provide its definition, identify any concerns about its
validity based on the clinician panels, and summarize the strength of
evidence in the literature for each indicator.

The following notes about some of the terms in the table are intended to
help the reader understand the context in which they are used.

**Validity Concerns.** The following concerns, raised during our panel
review, are listed if they affect the validity of the particular
indicator:

> **Rare** ―This indicator is relatively rare and may not have adequate
> statistical power for some providers.
>
> **Condition definition varies**―This indicator includes conditions for
> which diagnosis may be subjective, depending on the threshold of the
> physician, and patients with the same clinical state may not have the
> same diagnosis.
>
> **Underreporting or screening**―Conditions included in this indicator
> may not be systematically reported (leading to an artificially low
> rate) or may be routinely screened for (leading to a higher rate in
> facilities that screen).
>
> **Adverse consequences**―Use of this indicator may have undesirable
> effects, such as increasing inappropriate antibiotic use.
>
> **Stratification suggested**―This indicator includes some high risk
> patient groups and stratification is recommended when examining rates,
>
> **Unclear preventability**―As compared to other PSIs, the conditions
> included in this indicator may be less preventable by the health
> system.
>
> **Heterogeneous severity**―This indicator includes codes that
> encompass several levels of severity of a condition that cannot be
> ascertained by the codes.
>
> **Case mix bias**―This indicator was felt to be particularly subject
> to systematic bias, and DRG and comorbidity risk adjustment may not
> adequately address the concern.
>
> **Denominator unspecific**―The denominator for this indicator is less
> than ideal, because the true population at risk could not be
> identified using ICD-9-CM codes. Some patients are likely included who
> are not truly at risk, or some patients who are at risk are not
> included.

**Empirical Performance.** The performance of each indicator is measured
for the following:

> **Rate**―The rate measures the number of adverse events per 1,000
> population at risk. Rates represent the average rate of the indicator
> for a nationwide sample of hospitals.
>
> **Deviation**―Standard deviation is an estimate of systematic
> variation. For the PSIs, standard deviation is reported between
> providers.
>
> **Bias**―Bias represents the degree to which the results may be
> influenced by outside factors. Bias ratings are based on a series of
> tests of bias using DRG and comorbidity risk adjustment. Those
> indicators flagged with **X+** demonstrated substantial bias and
> should be risk adjusted. Those indicators flagged with **X** also
> demonstrated some bias. Those without a flag did not demonstrate
> substantial bias in empirical tests, but may nonetheless be
> substantially biased in a manner not detectable by the bias tests.
> Those marked with **N/A** did not undergo empirical testing of bias
> due to lack of systematic variation.

**Strength of Evidence.** The following key findings represent a review
of the limited literature assessing the validity of the indicators:

> **Coding**―Sensitivity is the proportion of patients who suffered an
> adverse event, based on detailed chart review or prospective data
> collection, for whom that event was coded on a discharge abstract or
> Medicare claim. Predictive value is the proportion of patients with a
> coded adverse event who were confirmed as having suffered that event,
> based on detailed chart review or prospective data collection.
>
> **Construct, explicit process**―Adherence to specific, evidence-based
> or expert-endorsed processes of care, such as appropriate use of
> diagnostic modalities and effective therapies. The construct is that
> hospitals that provide better processes of care should experience
> fewer adverse events.
>
> **Construct, implicit process**―Adherence to the "standard of care"
> for similar patients, based on global assessment of quality by
> physician chart reviewers. The construct is that hospitals that
> provide better overall care should experience fewer adverse events.
>
> **Construct, staffing**―The construct is that hospitals that offer
> more nursing hours per patient day, better nursing skill mix, better
> physician skill mix, or more experienced physicians should have fewer
> adverse events.

The following distinctions were used to summarize the strength of the
published evidence for each indicator:

> \- Published evidence suggests that the indicator lacks validity in
> this domain (i.e., less than 50% sensitivity or predictive value;
> explicit or implicit process failure rates no more frequent than among
> control patients).
>
> **0** No published evidence regarding this domain of validity.
>
> **±** Published evidence suggests that the indicator may be valid in
> this domain, but different studies offer conflicting results (although
> study quality may account for these conflicts).
>
> **+** Published evidence suggests that the indicator is valid, or is
> likely to be valid, in this domain (i.e., one favorable study).
>
> **++** There is strong evidence supporting the validity of this
> indicator in this domain (i.e., multiple studies with consistent
> results, or studies showing both high sensitivity and high predictive
> value).
>
> When content validity is exceptionally high, as for transfusion
> reaction or iatrogenic pneumothorax, construct validity becomes less
> important.

A complete description of each PSI is included later in the guide under
"Detailed Evidence for Patient Safety Indicators" and in the document
*Patient Safety Indicators Technical Specifications.* See [Appendix
A](#appendix-a-links).

[]{#__RefHeading___Toc128139598 .anchor}Table 1: AHRQ Provider-Level
Patient Safety Indicators

+----------+---------------+-------------+----------------+------------+
| **PSI    | *             | **Validity  | **Empirical    | **Strength |
| Name**   | *Definition** | Concerns**  | Pe             | of         |
|          |               |             | rformance^b^** | Evidence** |
+==========+===============+=============+================+============+
| Compl    | Cases of      | Condition   | Provider Rate  | 0 Coding\  |
| ications | anesthetic    | definition  | = 0.758\       | 0 Explicit |
| of       | overdose,     | varies      | Provider SD =  | Process\   |
| Ane      | reaction, or  |             | 2.119          | 0 Implicit |
| sthesia\ | endotrachial  | Und         |                | Process\   |
| (PSI 1)  | tube          | erreporting | Pop. Rate =    | 0 Staffing |
|          | misplacement  | or          | 0.814\         |            |
|          | per 1,000     | screening   | Bias = Not     |            |
|          | surgery       |             | detected ^c^   |            |
|          | discharges.   | Denominator |                |            |
|          | Excludes      | unspecific  |                |            |
|          | codes for     |             |                |            |
|          | drug use and  |             |                |            |
|          | s             |             |                |            |
|          | elf-inflicted |             |                |            |
|          | injury.       |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Death in | In-hospital   | He          | Provider Rate  | \+ Coding\ |
| Low      | deaths per    | terogeneous | = 2.599\       | 0 Explicit |
| M        | 1,000         | severity    | Provider SD =  | Process\   |
| ortality | patients in   |             | 31.803         | + Implicit |
| DRGs     | DRGs with     |             |                | Process\   |
| (PSI 2)  | less than     |             | Pop. Rate =    | 0 Staffing |
|          | 0.5%          |             | 0.620\         |            |
|          | mortality.^a^ |             | Bias = X+      |            |
|          | Excludes      |             |                |            |
|          | trauma,       |             |                |            |
|          | immuno        |             |                |            |
|          | -compromised, |             |                |            |
|          | and cancer    |             |                |            |
|          | patients.     |             |                |            |
+----------+---------------+-------------+----------------+------------+
| D        | Cases of      | Und         | Provider Rate  | -- Coding\ |
| ecubitus | decubitus     | erreporting | = 25.521\      | 0 Explicit |
| Ulcer    | ulcer per     | or          | Provider SD =  | Process\   |
| (PSI 3)  | 1,000         | screening   | 46.108         | 0 Implicit |
|          | discharges    |             |                | Process\   |
|          | with a length | He          | Pop. Rate =    | ± Staffing |
|          | of stay of 5  | terogeneous | 22.661\        |            |
|          | or more days. | severity    | Bias = X+      |            |
|          | Excludes      |             |                |            |
|          | patients with | Case mix    |                |            |
|          | paralysis or  | bias        |                |            |
|          | in MDC 9, MDC |             |                |            |
|          | 14, and       |             |                |            |
|          | patients      |             |                |            |
|          | admitted from |             |                |            |
|          | a long-term   |             |                |            |
|          | care          |             |                |            |
|          | facility.     |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Failure  | Deaths per    | Adverse     | Provider Rate  | \+ Coding\ |
| to       | 1,000         | c           | = 105.390\     | 0 Explicit |
| Rescue\  | patients      | onsequences | Provider SD =  | Process\   |
| (PSI 4)  | having        |             | 88.150         | 0 Implicit |
|          | developed     | Str         |                | Process\   |
|          | specified     | atification | Pop. Rate =    | ++         |
|          | complications | suggested   | 127.687\       | Staffing   |
|          | of care       |             | Bias = X+      |            |
|          | during        | Unclear     |                |            |
|          | hos           | pre         |                |            |
|          | pitalization. | ventability |                |            |
|          | Excludes      |             |                |            |
|          | patients age  | He          |                |            |
|          | 75 and older, | terogeneous |                |            |
|          | neonates in   | severity    |                |            |
|          | MDC 15,       |             |                |            |
|          | patients      |             |                |            |
|          | admitted from |             |                |            |
|          | long-term     |             |                |            |
|          | care facility |             |                |            |
|          | and patients  |             |                |            |
|          | transferred   |             |                |            |
|          | to or from    |             |                |            |
|          | other acute   |             |                |            |
|          | care          |             |                |            |
|          | facility.     |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Foreign  | Discharges    | Rare        | Provider Rate  | 0 Coding\  |
| Body     | with foreign  |             | = 0.071\       | 0 Explicit |
| Left     | body          | Str         | Provider SD =  | Process\   |
| During   | accidentally  | atification | 0.340          | 0 Implicit |
| Pr       | left in       | suggested   |                | Process\   |
| ocedure\ | during        |             | Pop. Rate =    | 0 Staffing |
| (PSI 5)  | procedure per | Denominator | 0.084\         |            |
|          | 1,000         | unspecific  | Bias = N/A     |            |
|          | discharges    |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Ia       | Cases of      | Denominator | Provider Rate  | 0 Coding\  |
| trogenic | iatrogenic    | unspecific  | = 0.408\       | 0 Explicit |
| Pneu     | pneumothorax  |             | Provider SD =  | Process\   |
| mothorax | per 1,000     |             | 0.951          | 0 Implicit |
| (PSI 6)  | discharges.   |             |                | Process\   |
|          | Excludes      |             | Pop. Rate =    | 0 Staffing |
|          | trauma,       |             | 0.562\         |            |
|          | thoracic      |             | Bias = X       |            |
|          | surgery, lung |             |                |            |
|          | or pleural    |             |                |            |
|          | biopsy, or    |             |                |            |
|          | cardiac       |             |                |            |
|          | surgery       |             |                |            |
|          | patients, and |             |                |            |
|          | MDC 14.       |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Selected | Cases of      | Und         | Provider Rate  | 0 Coding\  |
| In       | secondary     | erreporting | = 2.468\       | 0 Explicit |
| fections | ICD-9-CM      | or          | Provider SD =  | Process\   |
| Due to   | codes 9993 or | screening   | 8.575          | 0 Implicit |
| Medical  | 00662 per     |             |                | Process\   |
| Care     | 1,000         | Adverse     | Pop. Rate =    | 0 Staffing |
| (PSI 7)  | discharges.   | c           | 2.137\         |            |
|          | Excludes      | onsequences | Bias = X       |            |
|          | patients with |             |                |            |
|          | immu          |             |                |            |
|          | nocompromised |             |                |            |
|          | state or      |             |                |            |
|          | cancer.       |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Case mix    | Provider Rate  | \+ Coding\ |
| perative | in-hospital   | bias        | = 0.627\       | + Explicit |
| Hip      | hip fracture  |             | Provider SD =  | Process\   |
| F        | per 1,000     | Denominator | 11.261         | + Implicit |
| racture\ | surgical      | unspecific  |                | Process\   |
| (PSI 8)  | discharges.   |             | Pop. Rate =    | 0 Staffing |
|          | Excludes      |             | 0.276\         |            |
|          | patients in   |             | Bias = X       |            |
|          | MDC 8, with   |             |                |            |
|          | conditions    |             |                |            |
|          | suggesting    |             |                |            |
|          | fracture      |             |                |            |
|          | present on    |             |                |            |
|          | admission and |             |                |            |
|          | MDC 14.       |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Str         | Provider Rate  | ± Coding\  |
| perative | hematoma or   | atification | = 1.732\       | ± Explicit |
| He       | hemorrhage    | suggested   | Provider SD =  | Process\   |
| morrhage | requiring a   |             | 3.333          | + Implicit |
| or       | procedure per | Case mix    |                | Process\   |
| H        | 1,000         | bias        | Pop. Rate =    | 0 Staffing |
| ematoma\ | surgical      |             | 2.121\         |            |
| (PSI 9)  | discharges.   | Denominator | Bias = Not     |            |
|          | Excludes MDC  | unspecific  | detected       |            |
|          | 14.           |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Condition   | Provider Rate  | -- Coding\ |
| perative | specified     | definition  | = 1.278\       | 0 Explicit |
| Phy      | physiological | varies      | Provider SD =  | Process\   |
| siologic | or metabolic  |             | 9.969          | 0 Implicit |
| and      | derangement   |             |                | Process\   |
| M        | per 1,000     |             | Pop. Rate =    | --         |
| etabolic | elective      |             | 1.043\         | Staffing   |
| Dera     | surgical      |             | Bias = X       |            |
| ngement\ | discharges.   |             |                |            |
| (PSI 10) | Excludes      |             |                |            |
|          | patients with |             |                |            |
|          | principal     |             |                |            |
|          | diagnosis of  |             |                |            |
|          | diabetes and  |             |                |            |
|          | with          |             |                |            |
|          | diagnoses     |             |                |            |
|          | suggesting    |             |                |            |
|          | increased     |             |                |            |
|          | s             |             |                |            |
|          | usceptibility |             |                |            |
|          | to            |             |                |            |
|          | derangement.  |             |                |            |
|          | Excludes      |             |                |            |
|          | obstetric     |             |                |            |
|          | admissions.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Unclear     | Provider Rate  | \+ Coding\ |
| perative | acute         | pre         | = 10.181\      | ± Explicit |
| Res      | respiratory   | ventability | Provider SD =  | Process\   |
| piratory | failure per   |             | 34.938         | + Implicit |
| Failure\ | 1,000         | Case mix    |                | Process\   |
| (PSI 11) | elective      | bias        | Pop. Rate =    | ± Staffing |
|          | surgical      |             | 9.285\         |            |
|          | discharges.   |             | Bias = X+      |            |
|          | Excludes MDC  |             |                |            |
|          | 4 and 5 and   |             |                |            |
|          | obstetric     |             |                |            |
|          | admissions.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of deep | Und         | Provider Rate  | \+ Coding\ |
| perative | vein          | erreporting | = 10.944\      | + Explicit |
| PE or    | thrombosis or | or          | Provider SD =  | Process\   |
| DVT\     | pulmonary     | screening   | 39.208         | + Implicit |
| (PSI 12) | embolism per  |             |                | Process\   |
|          | 1,000         | Str         | Pop. Rate =    | ± Staffing |
|          | surgical      | atification | 9.830\         |            |
|          | discharges.   | suggested   | Bias = X+      |            |
|          | Excludes      |             |                |            |
|          | obstetric     |             |                |            |
|          | patients.     |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Condition   | Provider Rate  | ± Coding\  |
| perative | sepsis per    | definition  | = 17.023\      | 0 Explicit |
| Sepsis\  | 1,000         | varies      | Provider SD =  | Process\   |
| (PSI 13) | elective      |             | 58.224         | 0 Implicit |
|          | surgery       | Adverse     |                | Process\   |
|          | patients,     | c           | Pop. Rate =    | --         |
|          | with length   | onsequences | 10.872\        | Staffing   |
|          | of stay more  |             | Bias = X+      |            |
|          | than 3 days.  |             |                |            |
|          | Excludes      |             |                |            |
|          | principal     |             |                |            |
|          | diagnosis of  |             |                |            |
|          | infection, or |             |                |            |
|          | any diagnosis |             |                |            |
|          | of            |             |                |            |
|          | immu          |             |                |            |
|          | nocompromised |             |                |            |
|          | state or      |             |                |            |
|          | cancer, and   |             |                |            |
|          | obstetric     |             |                |            |
|          | admissions.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Posto    | Cases of      | Case mix    | Provider Rate  | 0 Coding\  |
| perative | reclosure of  | bias        | = 2.118\       | 0 Explicit |
| Wound    | postoperative |             | Provider SD =  | Process\   |
| Deh      | disruption of |             | 9.628          | 0 Implicit |
| iscence\ | abdominal     |             |                | Process\   |
| (PSI 14) | wall per      |             | Pop. Rate =    | 0 Staffing |
|          | 1,000 cases   |             | 1.998\         |            |
|          | of            |             | Bias = X       |            |
|          | a             |             |                |            |
|          | bdominopelvic |             |                |            |
|          | surgery.      |             |                |            |
|          | Excludes      |             |                |            |
|          | obstetric     |             |                |            |
|          | admissions.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Ac       | Cases of      | Und         | Provider Rate  | ± Coding\  |
| cidental | technical     | erreporting | = 2.356\       | 0 Explicit |
| Puncture | difficulty    | or          | Provider SD =  | Process\   |
| or       | (e.g.,        | screening   | 3.285          | 0 Implicit |
| Lac      | accidental    |             |                | Process\   |
| eration\ | cut or        | Unclear     | Pop. Rate =    | 0 Staffing |
| (PSI 15) | laceration    | pre         | 3.549\         |            |
|          | during        | ventability | Bias = X+      |            |
|          | procedure)    |             |                |            |
|          | per 1,000     |             |                |            |
|          | discharges.   |             |                |            |
|          | Excludes      |             |                |            |
|          | obstetric     |             |                |            |
|          | admissions.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| Tra      | Cases of      | Rare        | Provider Rate  | 0 Coding\  |
| nsfusion | transfusion   |             | = 0.007\       | 0 Explicit |
| R        | reaction per  | Str         | Provider SD =  | Process\   |
| eaction\ | 1,000         | atification | 0.112          | 0 Implicit |
| (PSI 16) | discharges.   | suggested   |                | Process\   |
|          |               |             | Pop. Rate =    | 0 Staffing |
|          |               |             | 0.005\         |            |
|          |               |             | Bias = N/A     |            |
+----------+---------------+-------------+----------------+------------+
| Birth    | Cases of      | Condition   | Provider Rate  | -- Coding\ |
| Trauma―  | birth trauma, | definition  | = 5.425\       | 0 Explicit |
| Injury   | injury to     | varies      | Provider SD =  | Process\   |
| to       | neonate, per  |             | 17.182         | 0 Implicit |
| Neonate\ | 1,000         | Unclear     |                | Process\   |
| (PSI 17) | liveborn      | pre         | Pop. Rate =    | 0 Staffing |
|          | births.       | ventability | 5.531\         |            |
|          | Excludes some |             | Bias = N/A     |            |
|          | preterm       | He          |                |            |
|          | infants and   | terogeneous |                |            |
|          | infants with  | severity    |                |            |
|          | osteogenic    |             |                |            |
|          | imperfecta.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| O        | Cases of      | Unclear     | Provider Rate  | \+ Coding\ |
| bstetric | obstetric     | pre         | = 191.203\     | 0 Explicit |
| Trauma―  | trauma (3^rd^ | ventability | Provider SD =  | Process\   |
| Vaginal  | or 4^th^      |             | 140.435        | 0 Implicit |
| Delivery | degree        | Case mix    |                | Process\   |
| with     | lacerations)  | bias        | Pop. Rate =    | 0 Staffing |
| Ins      | per 1,000     |             | 191.006\       |            |
| trument\ | instru        |             | Bias = N/A     |            |
| (PSI 18) | ment-assisted |             |                |            |
|          | vaginal       |             |                |            |
|          | deliveries.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| O        | Cases of      | Unclear     | Provider Rate  | \+ Coding\ |
| bstetric | obstetric     | pre         | = 44.292\      | 0 Explicit |
| Trauma―  | trauma (3^rd^ | ventability | Provider SD =  | Process\   |
| Vaginal  | or 4^th^      |             | 37.115         | 0 Implicit |
| Delivery | degree        | Case mix    |                | Process\   |
| without  | lacerations)  | bias        | Pop. Rate =    | 0 Staffing |
| Ins      | per 1,000     |             | 46.340\        |            |
| trument\ | vaginal       |             | Bias = N/A     |            |
| (PSI 19) | deliveries    |             |                |            |
|          | without       |             |                |            |
|          | instrument    |             |                |            |
|          | assistance.   |             |                |            |
+----------+---------------+-------------+----------------+------------+
| O        | Cases of      | Unclear     | Provider Rate  | \+ Coding\ |
| bstetric | obstetric     | pre         | = 4.460\       | 0 Explicit |
| Trauma―  | trauma (3^rd^ | ventability | Provider SD =  | Process\   |
| Cesarean | or 4^th^      |             | 20.871         | 0 Implicit |
| D        | degree        | Case mix    |                | Process\   |
| elivery\ | lacerations)  | bias        | Pop. Rate =    | 0 Staffing |
| (PSI 20) | per 1,000     |             | 4.315\         |            |
|          | Cesarean      |             | Bias = N/A     |            |
|          | deliveries.   |             |                |            |
+----------+---------------+-------------+----------------+------------+

^a^ DRGs that are divided into "with complications and comorbidities"
and "without complications and comorbidities" are only included if both
divisions have mortality rates below 0.5%.

[^b^]{.mark} Notes under **Empirical Performance**:

**Provider Rates -** Observed (unadjusted) and unweighted rates for
providers (hospitals) and their standard deviations (SD) were calculated
using the HCUP Year 2003 SID from 38 states. Provider rates are per
1,000.

**Population Rates -** The population rates are weighted provider rates
(weighted by the number of discharges for each indicator).

[]{#__RefHeading___Toc128139599 .anchor}Table 2. AHRQ Area Level Patient
Safety Indicators

+-----------+---------------+--------------+-------------+------------+
| **PSI     | *             | **Validity   | **Empirical | **Strength |
| Name**    | *Definition** | Concerns**   | Perfo       | of         |
|           |               |              | rmance^a^** | Evidence** |
+===========+===============+==============+=============+============+
| Foreign   | Discharges    |              | Area Rate = |            |
| Body Left | with foreign  |              | 1.572\      |            |
| During    | body          |              | Area SD =   |            |
| P         | accidentally  |              | 5.204       |            |
| rocedure\ | left in       |              |             |            |
| (PSI 21)  | during        |              | Pop. Rate = |            |
|           | procedure per |              | 1.452       |            |
|           | 100,000       |              |             |            |
|           | population    |              |             |            |
+-----------+---------------+--------------+-------------+------------+
| I         | Cases of      |              | Area Rate = |            |
| atrogenic | iatrogenic    |              | 8.688\      |            |
| Pneu      | pneumothorax  |              | Area SD =   |            |
| mothorax\ | per 100,000   |              | 10.436      |            |
| (PSI 22)  | population.   |              |             |            |
|           | Excludes      |              | Pop. Rate = |            |
|           | thoracic      |              | 7.921       |            |
|           | surgery, lung |              |             |            |
|           | or pleural    |              |             |            |
|           | biopsy, or    |              |             |            |
|           | cardiac       |              |             |            |
|           | surgery       |              |             |            |
|           | patients, and |              |             |            |
|           | MDC 14.       |              |             |            |
+-----------+---------------+--------------+-------------+------------+
| Selected  | Cases of      |              | Area Rate = |            |
| I         | secondary     |              | 26.534\     |            |
| nfections | ICD-9-CM      |              | Area SD =   |            |
| Due to    | codes 999.3   |              | 26.287      |            |
| Medical   | or 996.62 per |              |             |            |
| Care\     | 100,000       |              | Pop. Rate = |            |
| (PSI 23)  | population.   |              | 29.463      |            |
|           | Excludes      |              |             |            |
|           | patients with |              |             |            |
|           | immu          |              |             |            |
|           | nocompromised |              |             |            |
|           | state or      |              |             |            |
|           | cancer.       |              |             |            |
+-----------+---------------+--------------+-------------+------------+
| Post      | Cases of      |              | Area Rate = |            |
| operative | reclosure of  |              | 3.192\      |            |
| Wound     | postoperative |              | Area SD =   |            |
| De        | disruption of |              | 6.199       |            |
| hiscence\ | abdominal     |              |             |            |
| (PSI 24)  | wall per      |              | Pop. Rate = |            |
|           | 100,000       |              | 2.688       |            |
|           | population.   |              |             |            |
|           | Excludes      |              |             |            |
|           | obstetric     |              |             |            |
|           | admissions.   |              |             |            |
+-----------+---------------+--------------+-------------+------------+
| A         | Cases of      |              | Area Rate = |            |
| ccidental | technical     |              | 52.994\     |            |
| Puncture  | difficulty    |              | Area SD =   |            |
| or        | (e.g.,        |              | 29.962      |            |
| La        | accidental    |              |             |            |
| ceration\ | cut or        |              | Pop. Rate = |            |
| (PSI 25)  | laceration    |              | 46.0729     |            |
|           | during        |              |             |            |
|           | procedure)    |              |             |            |
|           | per 100,000   |              |             |            |
|           | population.   |              |             |            |
|           | Excludes      |              |             |            |
|           | obstetric     |              |             |            |
|           | admissions.   |              |             |            |
+-----------+---------------+--------------+-------------+------------+
| Tr        | Cases of      |              | Area Rate = |            |
| ansfusion | transfusion   |              | 0.111\      |            |
| Reaction\ | reaction per  |              | Area SD =   |            |
| (PSI 26)  | 100,000       |              | 1.287       |            |
|           | population.   |              |             |            |
|           |               |              | Pop. Rate = |            |
|           |               |              | 0.076       |            |
+-----------+---------------+--------------+-------------+------------+
| Post      | Cases of      |              | Area Rate   |            |
| operative | hematoma or   |              | =13.654\    |            |
| H         | hemorrhage    |              | Area SD =   |            |
| emorrhage | requiring a   |              | 13.786\     |            |
| or        | procedure per |              | Pop. Rate = |            |
| Hematoma\ | 100,000       |              | 13.008      |            |
| (PSI 27)  | population.   |              |             |            |
+-----------+---------------+--------------+-------------+------------+

[^a^]{.mark} Notes under **Empirical Performance**:

**Area Rates -** Observed (unadjusted) and unweighted rates for areas
(counties) and their standard deviations (SD) were based on 2,570
geographic areas (counties) in the HCUP Year 2003 SID from 38 states.
Area rates are per 100,000.

**Population Rates -** The population rates are weighted area rates
(weighted by the area populations).

## Limitations in Using the PSIs 

Many important concerns cannot currently be monitored well using
administrative data, such as adverse drug events, and using these data
tends to favor specific types of indicators. For example, the PSIs
evaluated in this report contain a large proportion of surgical
indicators, rather than medical or psychiatric, because medical
complications are often difficult to distinguish from comorbidities that
are present on admission. In addition, medical populations tend to be
more heterogeneous than surgical, especially elective surgical
populations, making it difficult to account for case-mix. Panelists
often expressed that indicators were more applicable to patient safety
when limited to elective surgical admissions. However, the careful use
of administrative data holds promise for screening to target further
data collection and analysis. The ability to assess all patients at risk
for a particular patient safety problem, along with the relative low
cost, are particular strengths of these data sets.

Two broad areas of concern also hold true for these data sets.

1.  Questions about the clinical accuracy of discharge-based diagnosis
    coding lead to concerns about the interpretation of reported
    diagnoses that may represent safety problems. Specifically:

-   Administrative data are unlikely to capture all cases of a
    complication, regardless of the preventability, without false
    positives and false negatives (sensitivity and specificity).

-   When the codes are accurate in defining an event, the clinical
    vagueness inherent in the description of the code itself (e.g.,
    "hypotension"), may lead to a highly heterogeneous pool of clinical
    states represented by that code.

-   Incomplete reporting is an issue in the accuracy of any data source
    used for identifying patient safety problems, as medical providers
    might fear adverse consequences as a result of "full disclosure" in
    potentially public records such as discharge abstracts.

2.  The information about the ability of these data to distinguish
    adverse events in which no error occurred from true medical errors
    is limited. A number of factors―such as the heterogeneity of
    clinical conditions included in some codes, lack of information
    about event timing available in these data sets, and limited
    clinical detail for risk adjustment―contribute to the difficulty in
    identifying complications that represent medical error or may be at
    least in some part preventable.

These factors may exist for other sources of patient safety data as
well. For example, they have been raised in the context of the Joint
Commission on Accreditation of Healthcare Organizations (JCAHO)
implementation of a "sentinel event" program geared at identifying
serious adverse events that may be related to underlying safety
problems.

## Further Research on PSIs

The initial validation evaluations reviewed and performed for the PSIs
leave substantial room for further research with detailed chart data and
other data sources. Future validation work should focus on the
following:

-   The sensitivity and specificity of these indicators in detecting the
    occurrence of a complication.

-   The extent to which failures in processes of care at the system or
    individual level are detected using these indicators.

-   The relationship of these indicators with other measures of quality,
    such as mortality.

-   Further explorations of bias and risk adjustment.

Enhancements to administrative data are worth exploring in the context
of further validation studies that use data from other sources. For
example, as with other quality indicators, the addition of timing
variables may prove particularly useful in identifying whether a
complication was present on admission, or whether it occurred during the
hospitalization. While some of the complications that are present on
admission may indeed reflect adverse events of care in a previous
hospitalization or outpatient care, many may reflect comorbidities
instead of complications. A second example area―linking hospital data
over time and with outpatient data and other hospitalizations―would
allow inclusion of complications that occur after discharge and likely
would increase the sensitivity of the PSIs.

## Use of External Cause-of-Injury Codes

Several of the PSIs are based on capturing external cause-of-injury
(e-code) data. These codes are used to classify environmental events,
circumstances, and conditions as the cause of injury, poisoning, or
other adverse events. External cause-of-injury codes are critical to
evaluate population-based, cause-specific data on nonfatal injuries at
the state and local levels. However, not all states collect this
information in their hospital discharge data programs nor do all state
uniform billing committees require use of e-codes. Users of the PSIs
should be knowledgeable of the e-code requirements and practices of
hospitals represented in the input data file.

Table 3 provides a summary of the PSIs that are dependent on e-codes for
their definition (required), the PSIs that use e-codes within their
definition, and the PSIs that do not use any e-codes in their
definition. If use of e-codes is not mandated or coding may be highly
variable across hospitals, the PSIs that are dependent upon e-codes
should not be used and the PSIs that include e-codes in their definition
should be used with caution.

[]{#__RefHeading___Toc128139600 .anchor}Table 3: Indicators and Use of
External Cause-of-Injury Codes

  ------------- ------------------------- ------------------------------------
  **Indicator   **Indicator Name**        **Use of External Cause-of-Injury
  Number (used                            Codes**
  in                                      
  software)**                             

  15 & 25       Accidental Puncture or    Required. Used in both the numerator
                Laceration                and denominator definitions.

  17            Birth Trauma              Not used.

  1             Complications of          Required. Used in the numerator
                Anesthesia                definition.

  2             Death in Low Mortality    Not used.
                DRGs                      

  3             Decubitus Ulcer           Not used.

  4             Failure to Rescue         Not used.

  5 & 21        Foreign Body Left During  Required. Used in the numerator
                Procedure                 definition although the other ICD-9
                                          CM codes may capture the same
                                          information.

  6 & 22        Iatrogenic Pneumothorax   Not used.

  20            Obstetric Trauma --       Not used.
                Cesarean Section          

  18            Obstetric Trauma --       Not used.
                Vaginal with Instrument   

  19            Obstetric Trauma --       Not used.
                Vaginal without           
                Instrument                

  9             Postoperative Hemorrhage  Not used.
                or Hematoma               

  8             Postoperative Hip         Used as exclusion criteria in
                Fracture                  denominator population.

  10            Postoperative Physiologic Not used.
                and Metabolic             
                Derangements              

  12            Postoperative Pulmonary   Not used.
                Embolism or Deep Vein     
                Thrombosis                

  11            Postoperative Respiratory Not used.
                Failure                   

  13            Postoperative Sepsis      Not used.

  14 & 24       Postoperative Wound       Not used.
                Dehiscence                

  7 & 23        Selected Infections Due   Not used.
                to Medical Care           

  16 & 26       Transfusion Reaction      Required. Used in the numerator
                                          definition although the other ICD-9
                                          CM codes may capture the same
                                          information.

  27            Postoperative Hemorrhage  Not used.
                or Hematoma               
  ------------- ------------------------- ------------------------------------

# Detailed Evidence for Patient Safety Indicators

This section provides an abbreviated presentation of the details of the
literature review and the empirical evaluation for each PSI, including:

-   The definition of the indicator

-   The outcome of interest (or numerator)

-   The population at risk (or denominator)

-   The type of indicator

-   The measures of empirical performance. Rates are population rates as
    reported in Table 1 (PSI -- Provider) and Table 2 (PSI -- Area).
    Provider rates are per 1,000 qualifying discharges and Area rates
    are per 100,000 population.

The two-page descriptions for each indicator also include a more
detailed discussion of the panel review, the literature review, the
source of the indicator, and the results of the empirical analysis,
including information related to adjustments to increase the robustness
of the rates:

-   Reliability. Statistics on the signal standard deviation, signal
    share, and signal ratio were used to examine the effect of the
    reliability adjustment. Multivariate methods were applied to most of
    the indicators, and overall the reliability adjustment reduced the
    provider-level variation dramatically. In general, indicators with
    higher rates tend to perform better on tests of reliability; as a
    result, obstetric indicators with high rates tend to do very well
    relative to other indicators.

-   Bias. The effect of age, gender, DRG, and comorbidity risk
    adjustment on the relative ranking of hospitals ― compared to no
    risk adjustment ―was assessed, if applicable. The presence of high
    bias suggests that risk adjustment, using administrative data
    elements, is necessary to interpret provider-level differences in
    the rates of these indicators.

A full report on the literature review and empirical evaluation can be
found in *Measures of Patient Safety Based on Hospital Administrative
Data ― The Patient Safety Indicators* by the UCSF-Stanford EPC,

Detailed coding information for each PSI is provided in the document
*Patient Safety Indicators Technical Specifications*. The software
manuals *Patient Safety Indicators: SAS Software Documentation* and
*Patient Safety Indicators: SPSS Software Documentation* provide
detailed instructions on how to use the PSI software including data
preparation, calculation of the PSI rates, and interpretation of output.

In the SAS and SPSS versions of the software, all provider level
indicators are expressed as rates per 1,000 discharges. To obtain the
standardized rate for each provider level PSIs, the output of the
software should be multiplied by 1,000. The area level indicators are
expressed as rates per 100,000 population. To obtain the standardized
area rate for each area level PSIs, the output of the software should be
multiplied by 100,000.

There is also a Windows version of the software that incorporates all of
the QI modules into a single application. The Windows version, allows
the user to select the unit to be used for rates for both provider-level
and area-level PSIs.

See [Appendix A](#appendix-a-links) for links to documents and tools.

## Complications of Anesthesia (PSI 1)

+------------------+---------------------------------------------------+
| Definition       | Cases of anesthetic overdose, reaction, or        |
|                  | endotrachial tube misplacement per 1,000 surgery  |
|                  | discharges with an operating room procedure.      |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM diagnosis codes for      |
|                  | anesthesia complications in any secondary         |
|                  | diagnosis field.                                  |
+------------------+---------------------------------------------------+
| Denominator      | All surgical discharges, 18 years and older       |
|                  | **or** MDC 14 (pregnancy, childbirth, and         |
|                  | puerperium), defined by specific DRGs and an      |
|                  | ICD-9-CM code for an operating room procedure.    |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM diagnosis codes for anesthesia  |
|                  |     complications in the principal diagnosis      |
|                  |     field                                         |
|                  |                                                   |
|                  | -   with codes for self-inflicted injury,         |
|                  |     poisoning due to anesthetics (E8551,          |
|                  |     9681-4, 9687) **and** any diagnosis code for  |
|                  |     active drug dependence, or active             |
|                  |     non-dependent abuse of drugs                  |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.814 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Not detected, but may be biased in a way    |
|                  | undetectable by empirical tests                   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

Summary

This indicator is intended to capture cases flagged by external
cause-of-injury codes (e-codes) and complications codes for adverse
effects from the administration of therapeutic drugs, as well as the
overdose of anesthetic agents used primarily in therapeutic settings.

Panel Review

Panelists had concerns about the frequency of coding of these
complications, especially since the use of e-codes is considered
voluntary and appears to vary widely among providers. Plausibly, a
"reaction" may be described without attributing it to anesthetic.
Another concern is that some of these cases would be present on
admission (e.g., due to recreational drug use).

Panelists expressed concern about the events that would be assigned to
the code for incorrect placement of endotrachial tube. They noted that
true misplacement does represent medical error, but they were skeptical
about whether this code would be limited to those situations.

Ideally, this indicator would be used with a coding designation that
distinguishes conditions present on admission from those that develop
in-hospital. However, this is not available in the administrative data
used to define this indicator, and so this concern was addressed by
eliminating codes for drugs that are commonly used as recreational
drugs. While this does not eliminate the chance that these codes
represent intentional or accidental overdose on the part of the patient,
it should eliminate many of these cases.

Literature Review

The literature review focused on the validity of complication indicators
based on ICD-9-CM diagnosis or procedure codes. Results of the
literature review indicate no published evidence for the sensitivity or
predictive value of this indicator based on detailed chart review or
prospective data collection. Sensitivity is the proportion of the
patients who suffered an adverse event for whom that event was coded on
a discharge abstract or Medicare claim. Predictive value is the
proportion of patients with a coded adverse event who were confirmed as
having suffered that event.

The project team found no published evidence for this indicator that
supports the following constructs: (1) that hospitals that provide
better processes of care experience fewer adverse events; (2) that
hospitals that provide better overall care experience fewer adverse
events; and (3) that hospitals that offer more nursing hours per patient
day, better nursing skill mix, better physician skill mix, or more
experienced physicians have fewer adverse events.

Empirical Analysis

The project team conducted extensive empirical analyses on the PSIs.
Complications of Anesthesia generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is 75.7%, suggesting that observed differences in
risk-adjusted rates likely reflect true differences across hospitals.

The signal standard deviation for this indicator is 0.00187, indicating
that the systematic differences (signal) among hospitals is lower than
many indicators and less likely associated with hospital
characteristics. The signal share is 0.00563, and is also lower than
many indicators. The signal share is a measure of the share of total
variation (hospital and patient) accounted for by hospitals. The lower
the share, the less important the hospital in accounting for the rate
and the more important other potential factors (e.g., patient
characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Complications of Anesthesia is low,
indicating that the measures are likely not biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.)

Source

A subset of this indicator was originally proposed by Iezzoni et
al.[^43] as part of Complications Screening Program (CSP) (CSP 21,
"Complications relating to anesthetic agents and other CNS depressants")
Their definition also includes poisoning due to centrally acting muscle
relaxants and accidental poisoning by nitrogen oxides, which were
omitted from this PSI. Their definition excludes other codes included in
the PSI, namely, poisoning by other and unspecified general anesthetics
and external cause of injury codes for "endotracheal tube wrongly place
during anesthetic procedure" and adverse effects of anesthetics in
therapeutic use.

## Death in Low-Mortality DRGs (PSI 2)

+------------------+---------------------------------------------------+
| Definition       | In-hospital deaths per 1,000 patients in DRGs     |
|                  | with less than 0.5% mortality.                    |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with disposition of "deceased".        |
+------------------+---------------------------------------------------+
| Denominator      | Patients, 18 years and older **or** MDC 14        |
|                  | (pregnancy, childbirth, and puerperium), in DRGs  |
|                  | with less than 0.5% mortality rate, based on NIS  |
|                  | 2003 low-mortality DRG. If a DRG is divided into  |
|                  | "without/with complications," both DRGs must have |
|                  | mortality rates below 0.5% to qualify for         |
|                  | inclusion.                                        |
|                  |                                                   |
|                  | Exclude patients with any code for trauma,        |
|                  | immunocompromised state, or cancer.               |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.620 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to identify in-hospital deaths in patients
unlikely to die during hospitalization. The underlying assumption is
that when patients admitted for an extremely low-mortality condition or
procedure die, a health care error is more likely to be responsible.
Patients experiencing trauma or having an immunocompromised state or
cancer are excluded, as these patients have higher non-preventable
mortality.

**Panel Review**

This indicator should be evaluated separately by type of DRG when used
as an indicator of quality. For example, the PSI Software reports the
low-mortality DRG rate for all the included DRGs and separately by DRG
type: adult medical, adult surgical (with and without an operating room
procedure), pediatric medical, pediatric surgical (with and without an
operating room procedure), and obstetric and psychiatric. The overall
usefulness of this indicator was rated as favorable by panelists.
Because the denominator includes many heterogeneous patients cared for
by different services, this indicator should be stratified by DRG type
(i.e., medical, surgical, psychiatric, obstetric, pediatric) when used
as an indicator of quality.

Panelists noted that hospital case-mix may affect the rate of death in
low mortality DRGs, and patients referred from skilled nursing
facilities, those with certain comorbidities, and older patients may be
at higher risk of dying. They advocated risk adjustment for
comorbidities and age.

Panelists advocated that this indicator not be subject to public
reporting because of the potential bias and questions about the extent
of preventability.

**Literature Review**

Based on two-stage implicit review of randomly selected deaths, Hannan
et al. found that patients in low-mortality DRGs (\<0.5%) were 5.2 times
more likely than all other patients who died (9.8% versus 1.7%) to have
received "care that departed from professionally recognized standards,"
after adjusting for patient demographic, geographic, and hospital
characteristics.[^44] In 15 of these 26 cases (58%) of substandard care,
the patient's death was attributed at least partially to that care. The
association with substandard care was stronger for the DRG-based
definition of this indicator than for the procedure-based definition
(5.7% versus 1.7%, OR=3.2). The project team was unable to find other
evidence on the validity of this indicator.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Death in Low-mortality DRGs generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is high, relative to other indicators, at 94.2%,
suggesting that observed differences in risk-adjusted rates likely
reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00439, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is high, relative to other indicators,
at 0.04237. The signal share is a measure of the share of total
variation (hospital and patient) accounted for by hospitals. The lower
the share, the less important the hospital in accounting for the rate
and the more important other potential factors (e.g., patient
characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Death in Low-mortality DRGs is high,
indicating that the measures are biased based on the characteristics
observed. (It is possible that characteristics that are not observed
using administrative data may be related to the patient's risk of
experiencing an adverse event.) Risk adjustment is important for this
indicator.

**Source**

This indicator was originally proposed by Hannan et al. as a criterion
for targeting "cases that would have a higher percentage of quality of
care problems than cases without the criterion, as judged by medical
record review."[^45] An alternative form of this indicator focused on
"primary surgical procedures," rather than DRGs, with less than 0.5%
inpatient mortality.

## Decubitus Ulcer (PSI 3)

+------------------+---------------------------------------------------+
| Definition       | Cases of decubitus ulcer per 1,000 discharges     |
|                  | with a length of stay greater than 4 days.        |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code of decubitus ulcer  |
|                  | in any secondary diagnosis field.                 |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges 18 years and  |
|                  | older defined by specific DRGs.                   |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with length of stay of less than 5 days       |
|                  |                                                   |
|                  | -   with ICD-9-CM code of decubitus ulcer in the  |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | -   MDC 9 (Skin, Subcutaneous Tissue, and Breast) |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | -   with any diagnosis of hemiplegia, paraplegia, |
|                  |     or quadriplegia                               |
|                  |                                                   |
|                  | -   with an ICD-9-CM diagnosis code of spina      |
|                  |     bifida or anoxic brain damage                 |
|                  |                                                   |
|                  | -   with an ICD-9-CM procedure code for           |
|                  |     debridement or pedicle graft **before or on   |
|                  |     the same day** as the major operating room    |
|                  |     procedure (surgical cases only)               |
|                  |                                                   |
|                  | -   admitted from a long-term care facility       |
|                  |     (Admission Source=3)                          |
|                  |                                                   |
|                  | -   transferred from an acute care facility       |
|                  |     (Admission Source=2)                          |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 22.661 per 1,000          |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of in-hospital decubitus
ulcers. Its definition is limited to decubitus ulcer as a secondary
diagnosis to better screen out cases that may be present on admission.
In addition, this indicator excludes patients who have a length of stay
of 4 days or less, as it is unlikely that a decubitus ulcer would
develop within this period of time. Finally, this indicator excludes
patients who are particularly susceptible to decubitus ulcer, namely
patients with major skin disorders (MDC 9) and paralysis.

**Panel Review**

The overall usefulness of this indicator was rated as very favorable by
panelists. Concerns regarding the systematic screening for ulcers and
reliability of coding, especially for early stage ulcers, brought into
question that assertion. Therefore, this indicator appears to be best
used as a rate-based indicator. Panelists suggested that patients
admitted from a long-term care facility be excluded, as these patients
may have an increased risk of having decubiti present on admission.

Panelists noted that hospitals that routinely screen for decubitus
ulcers as part of a quality improvement program might have an
artificially high rate of ulcers compared to other hospitals, which may
cause this indicator to be somewhat biased.

This indicator includes pediatric patients. Pressure sores are very
unusual in children, except among the most critically ill children (who
may be paralyzed to improve ventilator management) and children with
chronic neurological problems. Age stratification is recommended.

**Literature Review**

*Coding validity.* No evidence on validity is available from CSP
studies. Geraci et al. confirmed only 2 of 9 episodes of pressure ulcers
reported on discharge abstracts of Veterans Affairs (VA) patients
hospitalized in 1987-89 for congestive heart failure (CHF), chronic
obstructive pulmonary disease (COPD), or diabetes.[^46] The sensitivity
for a nosocomial ulcer was 40%. Among Medicare hip fracture patients,
Keeler et al. confirmed 6 of 9 reported pressure ulcers, but failed to
ascertain 89 additional cases (6% sensitivity) using ICD-9-CM
codes.[^47] In the largest study to date, Berlowitz et al. found that
the sensitivity of a discharge diagnosis of pressure ulcer among all
patients transferred from VA hospitals to VA nursing homes in 1996 was
31% overall, or 54% for stage IV (deep) ulcers.[^48] The overall
sensitivity increased modestly since 1992 (26.0%), and was slightly but
statistically significantly better among medical patients than among
surgical patients (33% versus 26%).

*Construct validity.* Needleman and Buerhaus found that nurse staffing
was inconsistently associated with the occurrence of pressure ulcers
among medical patients, and was independent of pressure ulcers among
major surgery patients.[^49] As was expected, nursing skill mix (RN
hours/licensed nurse hours) was significantly associated with the
pressure ulcer rate.[^50] Total licensed nurse hours per acuity-adjusted
patient day were inconsistently associated with the rate of pressure
ulcers.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Decubitus Ulcer generally performs well on several different dimensions,
including reliability, bias, relatedness of indicators, and persistence
over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is high, relative to other indicators, at 85.6%,
suggesting that observed differences in risk-adjusted rates likely
reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.0147, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.01067. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Decubitus Ulcer is high, indicating
that the measure is biased based on the characteristics observed. (It is
possible that characteristics that are not observed using administrative
data may be related to the patient's risk of experiencing an adverse
event.) Risk adjustment is important for this indicator.

**Source**

This indicator was originally proposed by Iezzoni et al.[^51] as part of
the Complications Screening Program (CSP 6, "cellulitis or decubitus
ulcer"). Needleman and Buerhaus identified decubitus ulcer as an
"outcome potentially sensitive to nursing"[^52] The American Nurses
Association, its State associations, and the California Nursing Outcomes
Coalition have identified the total prevalence of inpatients with Stage
I, II, III, or IV pressure ulcers as a "nursing-sensitive quality
indicator for acute care settings."[^53]

## Failure to Rescue (PSI 4)

+------------------+---------------------------------------------------+
| Definition       | Deaths per 1,000 patients having developed        |
|                  | specified complications of care during            |
|                  | hospitalization.                                  |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with a disposition of "deceased".      |
+------------------+---------------------------------------------------+
| Denominator      | Discharges 18 years and older with potential      |
|                  | complications of care listed in failure to rescue |
|                  | definition (i.e., pneumonia, DVT/PE, sepsis,      |
|                  | acute renal failure, shock/cardiac arrest, or GI  |
|                  | hemorrhage/acute ulcer).                          |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   age 75 years and older                        |
|                  |                                                   |
|                  | -   neonatal patients in MDC 15                   |
|                  |                                                   |
|                  | -   transferred to an acute care facility         |
|                  |     (Discharge Disposition = 2)                   |
|                  |                                                   |
|                  | -   transferred from an acute care facility       |
|                  |     (Admission Source = 2)                        |
|                  |                                                   |
|                  | -   admitted from a long-term care facility       |
|                  |     (Admission Source=3)                          |
|                  |                                                   |
|                  | > Additional exclusion criteria specific to each  |
|                  | > diagnosis.                                      |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 127.687 per 1,000         |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to identify patients who die following the
development of a complication. The underlying assumption is that good
hospitals identify these complications quickly and treat them
aggressively.

Failure to Rescue may be fundamentally different than other indicators
reviewed in this report, as it may reflect different aspects of quality
of care (effectiveness in rescuing a patient from a complication versus
preventing a complication). This indicator includes pediatric patients.
It is important to note that children beyond the neonatal period
inherently recover better from physiological stress and thus may have a
higher rescue rate.

**Panel Review**

Panelists expressed concern regarding patients with "do not resuscitate"
(DNR) status. In cases where this DNR status is not a direct result of
poor quality of care, it would be contrary to patient desire and poor
quality of care to rescue a patient. In addition, very old patients―or
patients with advanced cancer or HIV―may not desire or may be
particularly difficult to rescue from these complications. As a result,
this indicator definition was modified to exclude those patients age 75
years and older. In addition, panelists suggested the exclusion of
patients admitted from long-term care facilities.

Panelists noted that several adverse incentives may be introduced by
implementing this indicator. In particular, since some type of
adjustment may be desirable, this indicator may encourage the upcoding
of complications and comorbidities to inflate the denominator or
manipulate risk adjustment. Others noted that this indicator could
encourage irresponsible resource use and allocation, although this is
likely to be a controversial idea. Finally, panelists emphasized that
this indicator should be used internally by hospitals, as it is not
validated for public reporting.

**Literature Review**

*Construct validity.* Silber and colleagues have published a series of
studies establishing the construct validity of failure-to-rescue rates
through their associations with hospital characteristics and other
measures of hospital performance. Among patients admitted for
cholecystectomy and transurethral prostatectomy, failure to rescue was
independent of severity of illness at admission, but was significantly
associated with the presence of surgical house staff and a lower
percentage of board-certified anesthesiologists.[^54] The adverse
occurrence rate was independent of this hospital characteristic. In a
larger sample of patients who underwent general surgical procedures,
lower failure-to-rescue rates were found at hospitals with high ratios
of registered nurses to beds.[^55] Failure rates were strongly
associated with risk-adjusted mortality rates, as expected, but not with
complication rates.[^56]

More recently, Needleman and Buerhaus confirmed that higher registered
nurse staffing (RN hours/adjusted patient day) and better nursing skill
mix (RN hours/licensed nurse hours) were consistently associated with
lower failure-to-rescue rates, even using administrative data to define
complications.[^57]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Failure to Rescue generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
66.6%, suggesting that observed differences in risk-adjusted rates may
reflect true differences across hospitals.

The signal standard deviation for this indicator is also high, relative
to other indicators, at 0.04617, indicating that the systematic
differences (signal) among hospitals is high and more likely associated
with hospital characteristics. The signal share is lower than many
indicators, at 0.01450. The signal share is a measure of the share of
total variation (hospital and patient) accounted for by hospitals. The
lower the share, the less important the hospital in accounting for the
rate and the more important other potential factors (e.g., patient
characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Failure to Rescue is high,
indicating that the measures are biased based on the characteristics
observed. (It is possible that characteristics that are not observed
using administrative data may be related to the patient's risk of
experiencing an adverse event.) Risk adjustment is important for this
indicator.

**Source**

This indicator was originally proposed by Silber et al. as a more
powerful tool than the risk-adjusted mortality rate to detect true
differences in patient outcomes across hospitals.[^58] The underlying
premise was that better hospitals are distinguished not by having fewer
adverse occurrences but by more successfully averting death among (i.e.,
rescuing) patients who experience such complications. More recently,
Needleman and Buerhaus adapted Failure to Rescue to administrative data
sets, hypothesizing that this outcome might be sensitive to nurse
staffing.[^59]

## Foreign Body Left During Procedure, Provider Level (PSI 5)

Provider Level Definition (only secondary diagnosis)

+------------------+---------------------------------------------------+
| Definition       | Discharges with foreign body accidentally left in |
|                  | during procedure per 1,000 discharges.            |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM codes for foreign body   |
|                  | left in during procedure in any secondary         |
|                  | diagnosis field.                                  |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges, 18 years and |
|                  | older **or** MDC 14 (pregnancy, childbirth, and   |
|                  | puerperium), defined by specific DRGs.            |
|                  |                                                   |
|                  | Exclude patients with ICD-9-CM codes for foreign  |
|                  | body left in during procedure in the principal    |
|                  | diagnosis field.                                  |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.084 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Did not undergo empirical testing of bias   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Foreign Body Left During Procedure, Area Level (PSI 21)

Area Level Definition (principal or secondary diagnosis)

  ------------------- ---------------------------------------------------
  Definition          Discharges with foreign body accidentally left in
                      during procedure per 100,000 population.

  Numerator           Discharges, 18 years and older **or** MDC 14
                      (pregnancy, childbirth, and puerperium), with
                      ICD-9-CM codes for foreign body left in during
                      procedure in any diagnosis field (principal or
                      secondary) of medical and surgical discharges
                      defined by specific DRGs.

  Denominator         Population of county or Metro Area associated with
                      FIPS code of patient's residence or hospital
                      location.

  Type of Indicator   Area level

  Empirical           Population Rate (2003): 1.452 per 100,000
  Performance         population

  Risk Adjustment     No risk adjustment
  ------------------- ---------------------------------------------------

**Summary**

This indicator is intended to flag cases of a foreign body accidentally
left in a patient during a procedure. This indicator is defined on both
a provider level (by restricting cases to those flagged by a secondary
diagnosis or procedure code) and an area level (by including all cases).

**Panel Review**

Panelists believed that this indicator was useful in identifying cases
of a foreign body left in during a procedure. However, they suggested
that each case identified be examined carefully by the hospital, because
this indicator was likely to yield few cases and some automated systems
report this complication when a foreign body is left in intentionally.

Panelists also noted that the population at risk included both medical
and surgical patients, but not all of these patients are at risk. The
panelists felt that limiting the population at risk to surgical patients
would decrease the sensitivity of this indicator substantially. Since
not all patients in the denominator are actually at risk, some hospitals
may appear to have a lower rate if they have fewer medical patients who
have undergone invasive procedures.

**Literature Review**

The literature review focused on the validity of complication indicators
based on ICD-9-CM diagnosis or procedure codes. Results of the
literature review indicate no published evidence for the sensitivity or
predictive value of this indicator based on detailed chart review or
prospective data collection. Sensitivity is the proportion of the
patients who suffered an adverse event for whom that event was coded on
a discharge abstract or Medicare claim. Predictive value is the
proportion of patients with a coded adverse event who were confirmed as
having suffered that event.

The project team found no published evidence for this indicator that
supports the following constructs: (1) that hospitals that provide
better processes of care experience fewer adverse events; (2) that
hospitals that provide better overall care experience fewer adverse
events; and (3) that hospitals that offer more nursing hours per patient
day, better nursing skill mix, better physician skill mix, or more
experienced physicians have fewer adverse events.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Foreign Body Left During Procedure generally performs well on several
different dimensions, including reliability, bias, relatedness of
indicators, and persistence over time. Due to the rarity of this
diagnosis, reliability and bias were not assessed.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program (CSP "sentinel events").[^60] It was
also included as one component of a broader indicator ("adverse events
and iatrogenic complications") in AHRQ's original HCUP Quality
Indicators.[^61] It was proposed by Miller et al. in the "Patient Safety
Indicator Algorithms and Groupings."[^62] Based on expert consensus
panels, McKesson Health Solutions included this indicator in its
CareEnhance Resource Management Systems, Quality Profiler Complications
Measures Module.

## Iatrogenic Pneumothorax, Provider Level (PSI 6)

Provider Level Definition (only secondary diagnosis)

+------------------+---------------------------------------------------+
| Definition       | Cases of iatrogenic pneumothorax per 1,000        |
|                  | discharges.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code of 512.1 in any     |
|                  | secondary diagnosis field.                        |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges 18 years and  |
|                  | older defined by specific DRGs.                   |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM code of 512.1 in the principal  |
|                  |     diagnosis field                               |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | -   with an ICD-9-CM diagnosis code of chest      |
|                  |     trauma or pleural effusion                    |
|                  |                                                   |
|                  | -   with an ICD-9-CM procedure code of            |
|                  |     diaphragmatic surgery repair                  |
|                  |                                                   |
|                  | -   with any code indicating thoracic surgery or  |
|                  |     lung or pleural biopsy or assigned to cardiac |
|                  |     surgery DRGs                                  |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.562 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Some bias demonstrated                      |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Iatrogenic Pneumothorax, Area Level (PSI 22)

Area Level Definition (principal or secondary diagnosis)

+------------------+---------------------------------------------------+
| Definition       | Cases of iatrogenic pneumothorax per 100,000      |
|                  | population.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges 18 years and older with ICD-9-CM code  |
|                  | of 512.1 in any diagnosis field (principal or     |
|                  | secondary) of medical and surgical discharges     |
|                  | defined by specific DRGs.                         |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | -   with an ICD-9-CM diagnosis code of chest      |
|                  |     trauma or pleural effusion                    |
|                  |                                                   |
|                  | -   with an ICD-9-CM procedure code of            |
|                  |     diaphragmatic surgery repair                  |
|                  |                                                   |
|                  | -   with any code indicating thoracic surgery or  |
|                  |     lung or pleural biopsy or assigned to cardiac |
|                  |     surgery DRGs                                  |
+------------------+---------------------------------------------------+
| Denominator      | Population of county or Metro Area associated     |
|                  | with FIPS code of patient's residence or hospital |
|                  | location.                                         |
+------------------+---------------------------------------------------+
| Type of          | Area level                                        |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 7.921 per 100,000         |
| Performance      | population                                        |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of pneumothorax caused by
medical care. This indicator is defined on both a provider level (by
including cases of iatrogenic pneumothorax occurring as a secondary
diagnosis during hospitalization) and on an area level (by including all
cases of iatrogenic pneumothorax).

Iatrogenic pneumothorax excludes all trauma patients because these
patients may be more susceptible to non-preventable iatrogenic
pneumothorax or may be miscoded for traumatic pneumothorax. The smaller
anatomy of children, especially neonates, may increase the technical
complexity of these procedures in this population (however, these
procedures are less likely to be performed in unmonitored settings).

**Panel Review**

Panelists rated the overall usefulness of this indicator favorably. The
denominator of the definition that the panelists rated was limited to
patients receiving a central line, Swan-Ganz catheter, or thorocentesis.
However, exploratory empirical analyses found that this definition could
not be operationalized using administrative data, as these procedures
appeared to be under-reported. Although the panelists noted that this
complication, given the definition rated, reflected medical error, the
actual final definition of this indicator includes cases that may be
less reflective of medical error. Specifically, this indicator includes
patients in whom a pneumothorax resulted from barotrauma, including
patients with acute respiratory distress syndrome.

Panelists expressed concern that some approaches of placing a central
line (e.g., subclavian) may be more likely to result in pneumothorax
than other approaches (e.g., internal jugular). However, other
complications―such as complications of the carotid artery―would be more
common with internal jugular approaches. Thus, if providers simply
change approach, they may have a decrease in pneumothorax but an
increase in other unmeasured complications.

**Literature Review**

The literature review focused on the validity of complication indicators
based on ICD-9-CM diagnosis or procedure codes. Results of the
literature review indicate no published evidence for the sensitivity or
predictive value of this indicator based on detailed chart review or
prospective data collection. Sensitivity is the proportion of the
patients who suffered an adverse event for whom that event was coded on
a discharge abstract or Medicare claim. Predictive value is the
proportion of patients with a coded adverse event who were confirmed as
having suffered that event.

The project team found no published evidence for this indicator that
supports the following constructs: (1) that hospitals that provide
better processes of care experience fewer adverse events; (2) that
hospitals that provide better overall care experience fewer adverse
events; and (3) that hospitals that offer more nursing hours per patient
day, better nursing skill mix, better physician skill mix, or more
experienced physicians have fewer adverse events.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Iatrogenic Pneumothorax generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
79.9%, suggesting that observed differences in risk-adjusted rates may
reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00143, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00183. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Iatrogenic Pneumothorax is moderate,
indicating that the measures may or may not be substantially biased
based on the characteristics observed.

**Source**

This diagnosis code was proposed by Miller et al. as one component of a
broader indicator ("iatrogenic conditions") in the "Patient Safety
Indicator Algorithms and Groupings."[^63] It was also included as one
component of a broader indicator ("adverse events and iatrogenic
complications") in AHRQ's Version 1.3 HCUP Quality Indicators.

## Selected Infections Due to Medical Care, Provider Level (PSI 7)

**Provider Level Definition (only secondary diagnosis)**

+------------------+---------------------------------------------------+
| Definition       | Cases of ICD-9-CM codes 9993 or 99662 per 1,000   |
|                  | discharges.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code of 9993 or 99662 in |
|                  | any secondary diagnosis field.                    |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges, 18 years and |
|                  | older **or** MDC 14 (pregnancy, childbirth, and   |
|                  | puerperium), defined by specific DRGs.            |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM code of 9993 or 99662 in the    |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | -   with length of stay less than 2 days          |
|                  |                                                   |
|                  | -   with any diagnosis code for immunocompromised |
|                  |     state or cancer                               |
|                  |                                                   |
|                  | -   with Cancer DRG                               |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 2.1371 per 1,000          |
| Performance      | population at risk\                               |
|                  | Bias: Some bias demonstrated                      |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Selected Infections Due to Medical Care, Area Level (PSI 23)

Area Level Definition (principal or secondary diagnosis)

+------------------+---------------------------------------------------+
| Definition       | Cases of ICD-9-CM codes 9993 or 99662 per 100,000 |
|                  | population.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges, 18 years and older **or** MDC 14      |
|                  | (pregnancy, childbirth, and puerperium), with     |
|                  | ICD-9-CM code of 9993 or 99662 in any diagnosis   |
|                  | field (principal or secondary) of medical and     |
|                  | surgical discharges defined by specific DRGs.     |
|                  |                                                   |
|                  | Exclude patients with any diagnosis code for      |
|                  | immunocompromised state or cancer.                |
+------------------+---------------------------------------------------+
| Denominator      | Population of county or Metro Area associated     |
|                  | with FIPS code of patient's residence or hospital |
|                  | location.                                         |
+------------------+---------------------------------------------------+
| Type of          | Area level                                        |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 29.463 per 100,000        |
| Performance      | population                                        |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of infection due to medical
care, primarily those related to intravenous (IV) lines and catheters.
This indicator is defined both on a provider level (by including cases
based on secondary diagnosis associated with the same hospitalization)
and on an area level (by including all cases of such infection).
Patients with potential immunocompromised states (e.g., AIDS, cancer,
transplant) are excluded, as they may be more susceptible to such
infection.

This indicator includes children and neonates. It should be noted that
high-risk neonates are at particularly high risk for catheter-related
infections.

**Panel Review**

Panelists expressed particular interest in tracking IV and
catheter-related infections, despite the potential for bias due to
charting or under-reporting. For the most part, they felt that these
complications were important to track. As with other indicators tracking
infections, concern regarding the potential overuse of prophylactic
antibiotics remains.

**Literature Review**

The literature review focused on the validity of complication indicators
based on ICD-9-CM diagnosis or procedure codes. Results of the
literature review indicate no published evidence for the sensitivity or
predictive value of this indicator based on detailed chart review or
prospective data collection. Sensitivity is the proportion of the
patients who suffered an adverse event for whom that event was coded on
a discharge abstract or Medicare claim. Predictive value is the
proportion of patients with a coded adverse event who were confirmed as
having suffered that event.

The project team found no published evidence for this indicator that
supports the following constructs: (1) that hospitals that provide
better processes of care experience fewer adverse events; (2) that
hospitals that provide better overall care experience fewer adverse
events; and (3) that hospitals that offer more nursing hours per patient
day, better nursing skill mix, better physician skill mix, or more
experienced physicians have fewer adverse events.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Selected Infections Due to Medical Care generally performs well on
several different dimensions, including reliability, bias, relatedness
of indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
70.8%, suggesting that observed differences in risk-adjusted rates may
reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00134, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00095. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Selected Infections Due to Medical
Care is moderate, indicating that the measures may or may not be
substantially biased based on the characteristics observed. (It is
possible that characteristics that are not observed using administrative
data may be related to the patient's risk of experiencing an adverse
event.)

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program (CSP 11, "miscellaneous
complications").[^64] The University HealthSystem Consortium adopted the
CSP indicator for major (#2933) and minor (#2961) surgery patients. A
much narrower definition, including only 9993 ("other infection after
infusion, injection, transfusion, vaccination"), was proposed by Miller
et al. in the "Patient Safety Indicator Algorithms and Groupings."[^65]
The American Nurses Association and its State associations have
identified the number of laboratory-confirmed bacteremic episodes
associated with central lines per critical care patient day as a
"nursing-sensitive quality indicator for acute care settings."[^66]

## Postoperative Hip Fracture (PSI 8)

+------------------+---------------------------------------------------+
| Definition       | Cases of in-hospital hip fracture per 1,000       |
|                  | surgical discharges with an operating room        |
|                  | procedure.                                        |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for hip fracture in |
|                  | any secondary diagnosis field.                    |
+------------------+---------------------------------------------------+
| Denominator      | All surgical discharges 18 years and older        |
|                  | defined by specific DRGs and an ICD-9-CM code for |
|                  | an operating room procedure.                      |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM code for hip fracture in the    |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | -   where the only operating room procedure is    |
|                  |     hip fracture repair                           |
|                  |                                                   |
|                  | -   where a procedure for hip fracture repair     |
|                  |     occurs before [or on the same                 |
|                  |     day]{.underline} as the first operating room  |
|                  |     procedure                                     |
|                  |                                                   |
|                  | *Note: If day of procedure is not available in    |
|                  | the input data file, the rate may be slightly     |
|                  | lower than if the information was available*      |
|                  |                                                   |
|                  | -   with diseases and disorders of the            |
|                  |     musculoskeletal system and connective tissue  |
|                  |     (MDC 8)                                       |
|                  |                                                   |
|                  | -   with principal diagnosis codes for seizure,   |
|                  |     syncope, stroke, coma, cardiac arrest,        |
|                  |     poisoning, trauma, delirium and other         |
|                  |     psychoses, or anoxic brain injury             |
|                  |                                                   |
|                  | -   with any diagnosis of metastatic cancer,      |
|                  |     lymphoid malignancy or bone malignancy, or    |
|                  |     self-inflicted injury                         |
|                  |                                                   |
|                  | -   MDC14 (Pregnancy, Childbirth and the          |
|                  |     Puerperium)                                   |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.276 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Some bias demonstrated                      |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to capture cases of in-hospital
fracture―specifically, hip fractures. This indicator limits diagnosis
codes to secondary diagnosis codes to eliminate fractures that were
present on admission. It further excludes patients in MDC 8
(musculoskeletal disorders) and patients with indications for trauma or
cancer, or principal diagnoses of seizure, syncope, stroke, coma,
cardiac arrest, or poisoning, as these patients may have a fracture
present on admission. This indicator is limited to surgical cases since
previous research suggested that these codes in medical patients often
represent conditions present on admission (see Literature Review).

**Panel Review**

Although this indicator was initially presented as \"In-hospital hip
fracture and fall,\" panelists unanimously suggested that falls should
be eliminated from this indicator and that all in-hospital fractures
should be included. The resulting indicator was termed \"In-hospital
fracture possibly related to falls.\" Children were excluded after
empirical analysis revealed that they did not have a substantial number
of cases in the numerator.

Panelists noted that this indicator may be slightly biased for hospitals
that care for more of the elderly and frail, because they have weaker
bones and are more susceptible to falls.

Panelists were interested in capturing all fractures occurring
in-hospital, although it was not possible to operationalize this
suggestion.

**Literature Review**

*Coding validity.* The original CSP definition had an adequate
confirmation rate among major surgical cases in Medicare inpatient
claims files (57% by coders' review, 71% by physicians' review), but a
very poor confirmation rate among medical cases (11% by both coders' and
physicians' review).[^67] [^68] This problem was attributable to the
fact that most hip fractures among medical inpatients were actually
comorbid diagnoses present at admission rather than complications of
hospital care. Nurse reviews were not performed.

*Construct validity.* Explicit process of care failures in the CSP
validation study were relatively frequent among cases with CSP 25 (76%
of major surgery patients, 54% of medical patients), after excluding
patients who had hip fractures at admission, but unflagged controls were
not evaluated on the same criteria.[^69] Physician reviewers identified
potential quality problems in 24% of major surgery patients and 5% of
medical patients with CSP 25 (versus 2% of unflagged controls for each
risk group).[^70]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Hip Fracture generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
67.1%, suggesting that observed differences in risk-adjusted rates may
reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00184, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00403. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Hip Fracture is
moderate, indicating that the measures may or may not be substantially
biased based on the characteristics observed. (It is possible that
characteristics that are not observed using administrative data may be
related to the patient's risk of experiencing an adverse event.)

**Source**

This indicator was originally proposed by Iezzoni et al.[^71] as part of
the Complications Screening Program (CSP 25, "in-hospital hip fracture
or fall"). Their definition also includes any documented fall, based on
external cause of injury codes. Needleman and Buerhaus considered
in-hospital hip fracture as an "Outcome Potentially Sensitive to
Nursing," but discarded it because the "event rate was too low to be
useful."[^72] The American Nurses Association, its State associations,
and the California Nursing Outcomes Coalition have identified the number
of patient falls leading to injury per 1,000 patient days (based on
clinical data collection) as a "nursing-sensitive quality indicator for
acute care settings."[^73]

## Postoperative Hemorrhage or Hematoma (PSI 9)

Provider Level Definition

+------------------+---------------------------------------------------+
| Definition       | Cases of hematoma or hemorrhage requiring a       |
|                  | procedure per 1,000 surgical discharges with an   |
|                  | operating room procedure.                         |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM codes for postoperative  |
|                  | hemorrhage or postoperative hematoma in any       |
|                  | secondary diagnosis field and code for            |
|                  | postoperative control of hemorrhage or drainage   |
|                  | of hematoma (respectively) in any procedure code  |
|                  | field.                                            |
+------------------+---------------------------------------------------+
| Denominator      | All surgical discharges 18 years and older        |
|                  | defined by specific DRGs and an ICD-9-CM code for |
|                  | an operating room procedure.                      |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for postoperative         |
|                  |     hemorrhage or postoperative hematoma in the   |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | -   where the only operating room procedure is    |
|                  |     postoperative control of hemorrhage or        |
|                  |     drainage of hematoma                          |
|                  |                                                   |
|                  | -   where a procedure for postoperative control   |
|                  |     of hemorrhage or drainage of hematoma occurs  |
|                  |     before the first operating room procedure.    |
|                  |                                                   |
|                  | > *Note: If day of procedure is not available in  |
|                  | > the input data file, the rate may be slightly   |
|                  | > lower than if the information was available.*   |
|                  |                                                   |
|                  | -   MDC 14 (Pregnancy, Childbirth and the         |
|                  |     Puerperium)                                   |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 2.121 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Not detected in empirical tests             |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Postoperative Hemorrhage or Hematoma (PSI 27)

Area Level Definition

+------------------+---------------------------------------------------+
| Definition       | Cases of hematoma or hemorrhage requiring a       |
|                  | procedure per 100,000 population.                 |
+------------------+---------------------------------------------------+
| Numerator        | All surgical discharges 18 years and older        |
|                  | defined by specific DRGs and an ICD-9-CM code for |
|                  | an operating room procedure.                      |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for postoperative         |
|                  |     hemorrhage or postoperative hematoma in the   |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | -   where the only operating room procedure is    |
|                  |     postoperative control of hemorrhage or        |
|                  |     drainage of hematoma                          |
|                  |                                                   |
|                  | -   where a procedure for postoperative control   |
|                  |     of hemorrhage or drainage of hematoma occurs  |
|                  |     before the first operating room procedure.    |
|                  |                                                   |
|                  | > *Note: If day of procedure is not available in  |
|                  | > the input data file, the rate may be slightly   |
|                  | > lower than if the information was available.*   |
|                  |                                                   |
|                  | -   MDC 14 (Pregnancy, Childbirth and the         |
|                  |     Puerperium)                                   |
+------------------+---------------------------------------------------+
| Denominator      | Population of county or Metro Area associated     |
|                  | with FIPS code of patient's residence or hospital |
|                  | location.                                         |
+------------------+---------------------------------------------------+
| Type of          | Area level                                        |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 13.008 per 100,000        |
| Performance      | population                                        |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to capture cases of hemorrhage or hematoma
following a surgical procedure. This indicator limits hemorrhage and
hematoma codes to secondary procedure and diagnosis codes, respectively,
to isolate those hemorrhages that can truly be linked to a surgical
procedure.

**Panel Review**

Panelists noted that some patients may be at higher risk for developing
a postoperative hemorrhage or hematoma. Specifically, they were
concerned about patients with coagulopathies and those on
anticoagulants. They suggested that where possible, this indicator be
stratified for patients with underlying clotting differences. They also
noted that patients admitted for trauma may be at a higher risk for
developing postoperative hemorrhage or may have a hemorrhage diagnosed
that occurred during the trauma. They also suggested that this indicator
be stratified for trauma and non-trauma patients.

**Literature Review**

*Coding validity.* The original CSP definition had a relatively high
confirmation rate among major surgical cases (83% by coders' review, 57%
by physicians' review, 52% by nurse-abstracted clinical documentation,
and 76% if nurses also accepted physicians' notes as adequate
documentation).[^74] [^75] [^76] Hartz and Kuhn estimated the validity
of hemorrhage codes using a gold standard based on transfusion
"requirement." [^77] They identified only 26% of episodes of bleeding
(defined as requiring return to surgery or transfusion of at least six
units of blood products) by applying this indicator (9981) to Medicare
patients who underwent coronary artery bypass surgery; the predictive
value was 75%.

*Construct Validity.* Explicit process of care failures in the CSP
validation study were relatively frequent among major surgical cases
with CSP 24, but not among medical cases (66% and 13%, respectively),
after excluding patients who had hemorrhage or hematoma at
admission.[^78] Cases flagged on this indicator and unflagged controls
did not differ significantly on a composite of 17 generic process
criteria. Similarly, cases flagged on this indicator and unflagged
controls did not differ significantly on a composite of four specific
process criteria for major surgical cases and two specific process
criteria for medical cases in the earlier study of elderly Medicare
beneficiaries.[^79]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Hemorrhage or Hematoma generally performs well on several
different dimensions, including reliability, bias, relatedness of
indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is lower than most indicators, at 8.6%, suggesting
that observed differences in risk-adjusted rates may not reflect true
differences across hospitals.

The signal standard deviation for this indicator is lower than most
indicators, at 0.00039, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00006. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Hemorrhage or Hematoma
is low, indicating that the measures are likely not biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.)

**Source**

This indicator was originally proposed by Iezzoni et al.[^80] as part of
the Complications Screening Program (CSP 24, "post-procedural hemorrhage
or hematoma"), although their definition allowed either procedure or
diagnosis codes. By contrast, the current definition requires a
hemorrhage or hematoma diagnosis with an associated procedure to either
control the hemorrhage or drain the hematoma. It was also included as
one component of a broader indicator ("adverse events and iatrogenic
complications") in AHRQ's original HCUP Quality Indicators.[^81]

## Postoperative Physiologic and Metabolic Derangement (PSI 10)

+------------------+---------------------------------------------------+
| Definition       | Cases of specified physiological or metabolic     |
|                  | derangement per 1,000 elective surgical           |
|                  | discharges with an operating room procedure.      |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM codes for physiologic    |
|                  | and metabolic derangements in any secondary       |
|                  | diagnosis field.                                  |
|                  |                                                   |
|                  | Discharges with acute renal failure (subgroup of  |
|                  | physiologic and metabolic derangements) must be   |
|                  | accompanied by a procedure code for dialysis      |
|                  | (3995, 5498).                                     |
+------------------+---------------------------------------------------+
| Denominator      | All elective\* surgical discharges age 18 and     |
|                  | older defined by specific DRGs and an ICD-9-CM    |
|                  | code for an operating room procedure. \*Defined   |
|                  | by admit type.                                    |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for physiologic and       |
|                  |     metabolic derangements in the principal       |
|                  |     diagnosis field                               |
|                  |                                                   |
|                  | -   with a principal ICD-9-CM code for chronic    |
|                  |     renal failure                                 |
|                  |                                                   |
|                  | -   with acute renal failure where a procedure    |
|                  |     for dialysis occurs **before or on the same   |
|                  |     day** as the first operating room procedure   |
|                  |                                                   |
|                  | *Note: If day of procedure is not available in    |
|                  | the input data file, the rate may be slightly     |
|                  | lower than if the information was available*      |
|                  |                                                   |
|                  | -   with both a diagnosis code of ketoacidosis,   |
|                  |     hyperosmolarity, or other coma (subgroups of  |
|                  |     physiologic and metabolic derangements        |
|                  |     coding) **and** a principal diagnosis of      |
|                  |     diabetes                                      |
|                  |                                                   |
|                  | -   with both a secondary diagnosis code for      |
|                  |     acute renal failure (subgroup of physiologic  |
|                  |     and metabolic derangements coding) **and** a  |
|                  |     principal diagnosis of acute myocardial       |
|                  |     infarction, cardiac arrhythmia, cardiac       |
|                  |     arrest, shock, hemorrhage, or                 |
|                  |     gastrointestinal hemorrhage                   |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth and the         |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | -   MDC 4 (diseases/disorders of respiratory      |
|                  |     system)                                       |
|                  |                                                   |
|                  | -   MDC 5 (diseases/disorders of circulatory      |
|                  |     system)                                       |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 1.043 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Some bias demonstrated                      |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of postoperative metabolic or
physiologic complications. The population at risk is limited to elective
surgical patients, because patients undergoing non-elective surgery may
develop less preventable derangements. In addition, each diagnosis has
specific exclusions, designed to reduce the number of flagged cases in
which the diagnosis was present on admission or was more likely to be
non-preventable.

**Panel Review**

Panelists expressed concern that acute renal failure suffers from the
problem of varied definition: what one doctor may call acute renal
failure, another may not. To ensure that the only renal failure cases
that are picked up are those that are clinically severe, the panel
suggested that acute renal failure be included only when it is paired
with a procedure code for dialysis.

Panelists noted that coding of relatively transient metabolic and
physiologic complications may be lacking, such as in cases of diabetic
ketoacidosis. Conversely, some physicians may capture non-clinically
significant events in this indicator.

This indicator includes pediatric patients, which was not specifically
discussed by the panel. The incidence of these complications is a
function of the underlying prevalence of diabetes and renal impairment,
which are less common among children than among adults.

### Literature Review

*Coding validity.* No evidence on validity is available from CSP
studies. Geraci et al.[^82] confirmed only 5 of 15 episodes of acute
renal failure and 12 of 34 episodes of hypoglycemia reported on
discharge abstracts of VA patients hospitalized for CHF, COPD, or
diabetes. Romano reported no false positives in episodes of acute renal
failure or hypoglycemia using discharge abstracts of diskectomy
patients.[^83] ICD-9-CM diagnoses (585 or 7885) had a sensitivity of 8%
and a predictive value of 4% in comparison with the VA's National
Surgical Quality Improvement Program database, which defines renal
failure as requiring dialysis within 30 days after surgery.[^84]

*Construct Validity.* After adjusting for patient demographic,
geographic, and hospital characteristics, Hannan et al. reported that
cases with a secondary diagnosis of fluid and electrolyte disorders were
no more likely to have received care that departed from professionally
recognized standards than cases without that code (2.2% versus 1.7%,
OR=1.13).[^85] However, these ICD-9-CM codes were omitted from the
accepted AHRQ PSIs.

### Empirical Evidence

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Physiologic and Metabolic Derangement generally performs
well on several different dimensions, including reliability, bias,
relatedness of indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is lower than many indicators, at 20.9%, suggesting
that observed differences in risk-adjusted rates may not reflect true
differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00054, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00033. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Physiologic and
Metabolic Derangement is moderate, indicating that the measures may or
may not be substantially biased based on the characteristics observed.
(It is possible that characteristics that are not observed using
administrative data may or may not be related to the patient's risk of
experiencing an adverse event.)

### Source

This indicator was originally proposed by Iezzoni et al.[^86] as part of
the CSP (CSP 20, "postoperative physiologic and metabolic
derangements"). The University HealthSystem Consortium adopted the CSP
indicator for major surgery patients (#2945).

## Postoperative Respiratory Failure (PSI 11)

+------------------+---------------------------------------------------+
| Definition       | Cases of acute respiratory failure per 1,000      |
|                  | elective surgical discharges with an operating    |
|                  | room procedure.                                   |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM codes for acute          |
|                  | respiratory failure (518.81) in any secondary     |
|                  | diagnosis field (After 1999, include 518.84).     |
|                  |                                                   |
|                  | ICD-9-CM procedure codes for postoperative        |
|                  | reintubation procedure based on number of days    |
|                  | after the major operating procedure code: 96.04   |
|                  | ≥1 day, 96.70 or 96.71 ≥2 days, or 96.72 ≥0 days. |
+------------------+---------------------------------------------------+
| Denominator      | All elective\* surgical discharges age 18 and     |
|                  | over defined by specific DRGs and an ICD-9-CM     |
|                  | code for an operating room procedure. \*Defined   |
|                  | by admit type.                                    |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for acute respiratory     |
|                  |     failure in the principal diagnosis field      |
|                  |                                                   |
|                  | -   with an ICD-9-CM diagnosis code of            |
|                  |     neuromuscular disorder                        |
|                  |                                                   |
|                  | -   where a procedure for tracheostomy is the     |
|                  |     only operating room procedure or tracheostomy |
|                  |     occurs before the first operating room        |
|                  |     procedure                                     |
|                  |                                                   |
|                  | > *Note: If day of procedure is not available in  |
|                  | > the input data file, the rate may be slightly   |
|                  | > lower than if the information was available*.   |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | ```{=html}                                        |
|                  | <!-- -->                                          |
|                  | ```                                               |
|                  | -   MDC 4 (diseases/disorders of respiratory      |
|                  |     system)                                       |
|                  |                                                   |
|                  | ```{=html}                                        |
|                  | <!-- -->                                          |
|                  | ```                                               |
|                  | -   MDC 5 (diseases/disorders of circulatory      |
|                  |     system)                                       |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 9.285 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of postoperative respiratory
failure. This indicator limits the code for respiratory failure to
secondary diagnosis codes to eliminate respiratory failure that was
present on admission. It further excludes patients who have major
respiratory or circulatory disorders and limits the population at risk
to elective surgery patients.

**Panel Review**

Panelists rated the overall usefulness of this indicator as relatively
favorable. They felt that only acute respiratory failure should be
retained in this indicator and noted that this clinically significant
event is at least partially preventable.

**Literature Review**

*Coding Validity.* CSP 3 had a relatively high confirmation rate among
major surgical cases in the FY1994 Medicare inpatient claims files from
California and Connecticut (72% by coders' review, 75% by physicians'
review).[^87] [^88] Nurse reviews were not performed.

Geraci et al. confirmed 1 of 2 episodes of respiratory failure reported
on discharge abstracts of VA patients hospitalized for CHF or diabetes;
the sensitivity for respiratory decompensation requiring mechanical
ventilation was 25%.[^89]

*Construct Validity.* Explicit process of care failures in the CSP
validation study were slightly but not significantly more frequent among
major surgical cases with CSP 3 than among unflagged controls (52%
versus 46%).[^90] Indeed, cases flagged on this indicator were
significantly less likely than unflagged controls (24% versus 64%) to
have at least one of four specific process-of-care problems in the
earlier study of elderly Medicare beneficiaries.[^91]

Needleman and Buerhaus found that nurse staffing was independent of the
occurrence of pulmonary failure among major surgery patients.[^92]
However, Kovner and Gergen reported that having more registered nurse
hours per adjusted patient day was associated with a lower rate of
"pulmonary compromise" after major surgery.[^93]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Respiratory Failure generally performs well on several
different dimensions, including reliability, bias, relatedness of
indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is lower than many indicators, at 46.6%, suggesting
that observed differences in risk-adjusted rates may not reflect true
differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00230, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00187. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Respiratory Failure is
high, indicating that the measures likely are biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.) Risk adjustment is important for
this indicator.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
CSP (CSP 3, "postoperative pulmonary compromise").[^94] Their definition
also includes pulmonary congestion, other (or postoperative) pulmonary
insufficiency, and acute pulmonary edema, which were omitted from this
PSI. The University HealthSystem Consortium (#2927) and AHRQ's original
HCUP Quality Indicators adopted the CSP indicator for major surgery
patients.[^95] Needleman and Buerhaus identified postoperative pulmonary
failure as an "Outcome Potentially Sensitive to Nursing," using the
original CSP definition.[^96]

## Postoperative Pulmonary Embolism or Deep Vein Thrombosis (PSI 12)

+------------------+---------------------------------------------------+
| Definition       | Cases of deep vein thrombosis (DVT) or pulmonary  |
|                  | embolism (PE) per 1,000 surgical discharges with  |
|                  | an operating room procedure.                      |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM codes for deep vein      |
|                  | thrombosis or pulmonary embolism in any secondary |
|                  | diagnosis field.                                  |
+------------------+---------------------------------------------------+
| Denominator      | All surgical discharges age 18 and older defined  |
|                  | by specific DRGs and an ICD-9-CM code for an      |
|                  | operating room procedure.                         |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for deep vein thrombosis  |
|                  |     or pulmonary embolism in the principal        |
|                  |     diagnosis field                               |
|                  |                                                   |
|                  | ```{=html}                                        |
|                  | <!-- -->                                          |
|                  | ```                                               |
|                  | -   where a procedure for interruption of vena    |
|                  |     cava is the only operating room procedure     |
|                  |                                                   |
|                  | ```{=html}                                        |
|                  | <!-- -->                                          |
|                  | ```                                               |
|                  | -   where a procedure for interruption of vena    |
|                  |     cava occurs before or on the same day as the  |
|                  |     first operating room procedure                |
|                  |                                                   |
|                  | *Note: If day of procedure is not available in    |
|                  | the input data file, the rate may be slightly     |
|                  | lower than if the information was available.*     |
|                  |                                                   |
|                  | -   MDC 14 (Pregnancy, Childbirth and the         |
|                  |     Puerperium)                                   |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 9.830 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to capture cases of postoperative venous
thromboses and embolism―specifically, pulmonary embolism and deep venous
thrombosis. This indicator limits vascular complications codes to
secondary diagnosis codes to eliminate complications that were present
on admission. It further excludes patients who have principal diagnosis
of DVT, as these patients are likely to have had PE/DVT present on
admission.

**Panel Review**

Panelists rated the overall usefulness of this indicator relatively
highly as compared to other indicators. They noted that preventative
techniques should decrease the rate of this indicator. This indicator
includes pediatric patients. In the absence of specific thrombophilic
disorders, postoperative thromboembolic complications in children are
most likely to be secondary to venous catheters rather than venous
stasis in the lower extremities.

Because the risk for DVT/PE varies greatly according to the type of
procedure performed, panelists suggested that this indicator be adjusted
or stratified according to surgical procedure types.

**Literature Review**

*Coding validity.* Geraci et al. confirmed only 1 of 6 episodes of DVT
or PE reported on discharge abstracts of VA patients for CHF, COPD, or
diabetes; the sensitivity was 100%.[^97] Among Medicare hip fracture
patients, by contrast, Keeler et al. confirmed 88% of reported PE cases,
and failed to ascertain just 6 cases (65% sensitivity) using ICD-9-CM
codes.[^98] For DVT, they found just 1 of 6 cases using ICD-9-CM codes
(but no false positive codes). Other studies have demonstrated that
ICD-9-CM codes for DVT and PE have high predictive value when listed as
the principal diagnosis for readmissions after major orthopedic surgery
(100%) or after inferior vena cava filter placement (98%).[^99] However,
these findings do not directly address the validity of DVT/PE as a
secondary diagnosis among patients treated by anticoagulation.

*Construct validity.* Explicit process of care failures in the CSP
validation study were relatively frequent among both major surgical and
medical cases with CSP 22 (72% and 69%, respectively), after
disqualifying cases in which DVT/PE was actually present at
admission.[^100] Needleman and Buerhaus found that nurse staffing was
independent of the occurrence of DVT/PE among both major surgical or
medical patients.[^101] However, Kovner and Gergen reported that having
more registered nurse hours and non-RN hours was associated with a lower
rate of DVT/PE after major surgery.[^102]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative PE or DVT generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
72.6%, suggesting that observed differences in risk-adjusted rates
likely reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00633, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00511. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative PE or DVT is high,
indicating that the measures likely are biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.) Risk adjustment is important for
this indicator.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program (CSP 22, "venous thrombosis and
pulmonary embolism")[^103] and was one of AHRQ's original HCUP Quality
Indicators for major surgery and invasive vascular procedure
patients.[^104] A code that maps to this indicator in the final AHRQ PSI
was proposed by Miller et al. as one component of a broader indicator
("iatrogenic conditions").[^105]

## Postoperative Sepsis (PSI 13)

+------------------+---------------------------------------------------+
| Definition       | Cases of sepsis per 1,000 elective surgery        |
|                  | patients with an operating room procedure and a   |
|                  | length of stay of 4 days or more.                 |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for sepsis in any   |
|                  | secondary diagnosis field.                        |
+------------------+---------------------------------------------------+
| Denominator      | All elective\* surgical discharges age 18 and     |
|                  | older defined by specific DRGs and an ICD-9-CM    |
|                  | code for an operating room procedure. \*Defined   |
|                  | by admit type.                                    |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM codes for sepsis in the         |
|                  |     principal diagnosis field                     |
|                  |                                                   |
|                  | ```{=html}                                        |
|                  | <!-- -->                                          |
|                  | ```                                               |
|                  | -   with a principal diagnosis of infection, or   |
|                  |     any code for immunocompromised state, or      |
|                  |     cancer                                        |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
|                  |                                                   |
|                  | -   with a length of stay of less than 4 days     |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 10.872 per 1,000          |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of nosocomial postoperative
sepsis. This indicator limits the code for sepsis to secondary diagnosis
codes to eliminate sepsis that was present on admission. This indicator
also excludes patients who have a principal diagnosis of infection,
patients with a length of stay of less than 4 days, and patients with
potential immunocompromised states (e.g., AIDS, cancer, transplant).

**Panel Review**

Panelists rated the overall usefulness of this indicator favorably,
although they were less sure that this complication was reflective of
medical error.

This indicator includes pediatric patients. High-risk neonates are at
particularly high risk for catheter-related infections.

**Literature Review**

*Coding validity.* No evidence on validity is available from CSP
studies. Barbour reported that only 38% of discharge abstracts with a
diagnosis of sepsis actually had hospital-acquired sepsis.[^106]
However, this review was not limited to cases with a secondary diagnosis
of sepsis, and sensitivity could not be evaluated. Geraci et al.
confirmed (by blood culture) only 2 of 15 episodes of sepsis or "other
infection" reported on discharge abstracts of VA patients hospitalized
for CHF, COPD, or diabetes; the sensitivity for a positive blood culture
was 50%.[^107] In comparison with the VA's National Surgical Quality
Improvement Program database, in which "systemic sepsis" is defined by a
positive blood culture and systemic manifestations of sepsis within 30
days after surgery, the ICD-9-CM diagnosis had a sensitivity of 37% and
a predictive value of 30%.[^108]

*Construct validity.* Needleman and Buerhaus found that nurse staffing
was independent of the occurrence of sepsis among both major surgical or
medical patients.[^109]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Sepsis generally performs well on several different
dimensions, including reliability, bias, relatedness of indicators, and
persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is lower than many indicators, at 53.9%, suggesting
that observed differences in risk-adjusted rates may not reflect true
differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00869, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00790. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Sepsis is high,
indicating that the measures likely are biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.) Risk adjustment is important for
this indicator.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program (CSP 7, "septicemia").[^110] Needleman
and Buerhaus identified sepsis as an "Outcome Potentially Sensitive to
Nursing" using the same CSP definition.[^111]

## Postoperative Wound Dehiscence, Provider Level (PSI 14)

**Provider Level Definition**

+------------------+---------------------------------------------------+
| Definition       | Cases of reclosure of postoperative disruption of |
|                  | abdominal wall per 1,000 cases of abdominopelvic  |
|                  | surgery.                                          |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for reclosure of    |
|                  | postoperative disruption of abdominal wall        |
|                  | (54.61) in any procedure field.                   |
+------------------+---------------------------------------------------+
| Denominator      | All abdominopelvic surgical discharges.           |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   where a procedure for reclosure of            |
|                  |     postoperative disruption of abdominal wall    |
|                  |     occurs before or on the same day as the first |
|                  |     abdominopelvic surgery procedure              |
|                  |                                                   |
|                  | *Note: If day of procedure is not available in    |
|                  | the input data file, the rate may be slightly     |
|                  | lower than if the information was available*      |
|                  |                                                   |
|                  | -   where length of stay is less than 2 days      |
|                  |                                                   |
|                  | -   with immunocompromised state                  |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium).                                  |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 1.998 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Some bias demonstrated                      |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Postoperative Wound Dehiscence, Area Level (PSI 24)

Area Level Definition

+------------------+---------------------------------------------------+
| Definition       | Cases of reclosure of postoperative disruption of |
|                  | abdominal wall per 100,000 population.            |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for reclosure of    |
|                  | postoperative disruption of abdominal wall (5461) |
|                  | in any procedure field.                           |
|                  |                                                   |
|                  | Exclude patients with immunocompromised state and |
|                  | MDC 14 (pregnancy, childbirth, and puerperium).   |
+------------------+---------------------------------------------------+
| Denominator      | Population of county or Metro Area associated     |
|                  | with FIPS code of patient's residence or hospital |
|                  | location.                                         |
+------------------+---------------------------------------------------+
| Type of          | Area level                                        |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 2.688 per 100,000         |
| Performance      | population at risk                                |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of wound dehiscence in patients
who have undergone abdominal and pelvic surgery. This indicator is
defined both on a provider level (by including cases based on secondary
diagnosis associated with the same hospitalization) and on an area level
(by including all cases of wound dehiscence).

**Panel Review**

Panelists suggested that postoperative wound disruption be excluded from
the indicator and that trauma, cancer, and immunocompromised patients be
included. They also reported that the risk of developing wound
dehiscence varies with patient factors such as age and comorbidities.

**Literature Review**

*Coding validity.* No evidence on validity is available from CSP
studies. Hawker et al. found that the sensitivity and predictive value
of wound dehiscence were both 100%.[^112] Faciszewski et al. aggregated
wound dehiscence with postoperative hemorrhage or hematoma and reported
a pooled confirmation rate of 17% with 3% sensitivity of coding among
patients who underwent spinal fusion.[^113] In comparison with the VA's
National Surgical Quality Improvement Program database, in which
dehiscence is defined as fascial disruption within 30 days after
surgery, the ICD-9-CM diagnosis of wound disruption had a sensitivity of
25% and a predictive value of 23%.[^114] This code (9983) was ultimately
removed from the accepted PSI, because the clinical panel was concerned
that the diagnosis definition was too broad and failed to distinguish
skin from fascial separation.

*Construct validity.* Based on two-stage review of randomly selected
deaths, Hannan et al. reported that cases with a secondary diagnosis of
wound disruption were 3.0 times more likely to have received care that
departed from professionally recognized standards than cases without
that code (4.3% versus 1.7%), after adjusting for patient demographic,
geographic, and hospital characteristics.[^115]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Postoperative Wound Dehiscence generally performs well on several
different dimensions, including reliability, bias, relatedness of
indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is related to systematic differences
(signal) in hospital performance rather than random variation (noise)―is
low, at 35.6%, suggesting that observed differences in risk-adjusted
rates may not reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00188, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00171. Signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Postoperative Wound Dehiscence is
moderate, indicating that the measures may or may not be substantially
biased based on the characteristics observed.

**Source**

An indicator on this topic (9983) was originally proposed by Hannan et
al. to target "cases that would have a higher percentage of quality of
care problems than cases without the criterion, as judged by medical
record review."[^116] The same code was included within a broader
indicator ("adverse events and iatrogenic complications") in AHRQ's
original HCUP Quality Indicators.[^117] Iezzoni et al. identified an
associated procedure code for reclosure of an abdominal wall dehiscence
(5461), and included both codes in the Complications Screening
Program.[^118] Miller et al. suggested the use of both codes (as "wound
disruption") in the original "AHRQ PSI Algorithms and Groupings."[^119]

## Accidental Puncture or Laceration, Provider Level (PSI 15)

**Provider Level Definition (only secondary diagnosis)**

+------------------+---------------------------------------------------+
| Definition       | Cases of technical difficulty (e.g., accidental   |
|                  | cut or laceration during procedure) per 1,000     |
|                  | discharges.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges 18 years and older with ICD-9-CM code  |
|                  | denoting technical difficulty (e.g., accidental   |
|                  | cut, puncture, perforation, or laceration) in any |
|                  | secondary diagnosis field.                        |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges defined by    |
|                  | specific DRGs.                                    |
|                  |                                                   |
|                  | Exclude cases:                                    |
|                  |                                                   |
|                  | -   with ICD-9-CM code denoting technical         |
|                  |     difficulty (e.g., accidental cut, puncture,   |
|                  |     perforation, or laceration) in the principal  |
|                  |     diagnosis field                               |
|                  |                                                   |
|                  | -   MDC 14 (pregnancy, childbirth, and            |
|                  |     puerperium)                                   |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 3.549 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Substantial bias; should be risk-adjusted   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age, sex, DRG, comorbidity categories             |
+------------------+---------------------------------------------------+

## Accidental Puncture or Laceration, Area Level (PSI 25)

Area Level Definition (principal or secondary diagnosis)

+------------------+---------------------------------------------------+
| Definition       | Cases of technical difficulty (e.g., accidental   |
|                  | cut or laceration during procedure) per 100,000   |
|                  | population.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges 18 years and older with ICD-9-CM code  |
|                  | denoting technical difficulty (e.g., accidental   |
|                  | cut, puncture, perforation, or laceration) in any |
|                  | diagnosis field (principal or secondary) of all   |
|                  | medical and surgical discharges defined by        |
|                  | specific DRGs.                                    |
|                  |                                                   |
|                  | Exclude MDC 14 (pregnancy, childbirth, and        |
|                  | puerperium).                                      |
+------------------+---------------------------------------------------+
| Denominator      | Population of county or Metro Area associated     |
|                  | with FIPS code of patient's residence or hospital |
|                  | location.                                         |
+------------------+---------------------------------------------------+
| Type of          | Area level                                        |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 46.072 per 100,000        |
| Performance      | population at risk                                |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of complications that arise due
to technical difficulties in medical care―specifically, those involving
an accidental puncture or laceration.

**Panel Review**

Panelists were unsure about how the culture of quality improvement in a
hospital would affect the coding of this complication. Some physicians
may be reluctant to record the occurrence of this complication for fear
of punishment. Panelists also noted that some of these occurrences are
not preventable.

**Literature Review**

*Coding validity.* No evidence on validity is available from CSP
studies. A study of laparoscopic cholecystectomy found that 95% of
patients with an ICD-9 code of accidental puncture or laceration had a
confirmed injury to the bile duct or gallbladder.[^120] However, only
27% had a clinically significant injury that required any intervention;
sensitivity of reporting was not evaluated. A similar study of
cholecystectomies reported that these two ICD-9 codes had a sensitivity
of 40% and a predictive value of 23% in identifying bile duct
injuries.[^121] Among 185 total knee replacement patients, Hawker et al.
found that the sensitivity and predictive value of codes describing
"miscellaneous mishaps during or as a direct result of surgery"
(definition not given) were 86% and 55%, respectively.[^122] Romano et
al. identified 19 of 45 episodes of accidental puncture, laceration, or
related procedure using discharge abstracts of diskectomy patients;
there was one false positive.[^123]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Accidental Puncture or Laceration generally performs well on several
different dimensions, including reliability, bias, relatedness of
indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
82.9%, suggesting that observed differences in risk-adjusted rates most
likely reflect true differences across hospitals.

The signal standard deviation for this indicator is lower than many
indicators, at 0.00279, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00241. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The project team assessed the effect of age, gender,
DRG, and comorbidity risk adjustment on the relative ranking of
hospitals compared to no risk adjustment. They measured (1) the impact
of adjustment on the assessment of relative hospital performance, (2)
the relative importance of the adjustment, (3) the impact on hospitals
with the highest and lowest rates, and (4) the impact throughout the
distribution. The detected bias for Accidental Puncture or Laceration is
high, indicating that the measures likely are biased based on the
characteristics observed. (It is possible that characteristics that are
not observed using administrative data may be related to the patient's
risk of experiencing an adverse event.) Risk adjustment is important for
this indicator.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program, although unlike the final PSI, its
codes were split between two CSP indicators (CSP 27, "technical
difficulty with medical care," and "sentinel events").[^124] It was also
included as one component of a broader indicator ("adverse events and
iatrogenic complications") in AHRQ's original HCUP Quality
Indicators.[^125] The University HealthSystem Consortium adopted CSP 27
as an indicator for medical (#2806) and major surgery (#2956) patients.
Miller et al. also split this set of ICD-9-CM codes into two broader
indicators ("miscellaneous misadventures" and "E codes") in the original
"AHRQ PSI Algorithms and Groupings."[^126] Based on expert consensus
panels, McKesson Health Solutions included one component of this PSI
(Accidental Puncture or Laceration) in its CareEnhance Resource
Management Systems, Quality Profiler Complications Measures Module.

## Transfusion Reaction, Provider Level (PSI 16)

**Provider Level Definition (only secondary diagnosis)**

+------------------+---------------------------------------------------+
| Definition       | Cases of transfusion reaction per 1,000           |
|                  | discharges.                                       |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for transfusion     |
|                  | reaction in any secondary diagnosis field.        |
+------------------+---------------------------------------------------+
| Denominator      | All medical and surgical discharges, 18 years and |
|                  | older **or** MDC 14 (pregnancy, childbirth, and   |
|                  | puerperium), defined by specific DRGs.            |
|                  |                                                   |
|                  | Exclude patients with ICD-9-CM code for           |
|                  | transfusion reaction in the principal diagnosis   |
|                  | field.                                            |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 0.005 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Did not undergo empirical testing of bias   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | No risk adjustment                                |
+------------------+---------------------------------------------------+

## Transfusion Reaction, Area Level (PSI 26)

Area Level Definition (principal or secondary diagnosis)

  ------------------- ---------------------------------------------------
  Definition          Cases of transfusion reaction per 100,000
                      population.

  Numerator           Discharges, 18 years and older **or** MDC 14
                      (pregnancy, childbirth, and puerperium), with
                      ICD-9-CM code for transfusion reaction in any
                      diagnosis field (principal or secondary ) of all
                      medical and surgical discharges defined by specific
                      DRGs.

  Denominator         Population of county or Metro Area associated with
                      FIPS code of patient's residence or hospital
                      location.

  Type of Indicator   Area level

  Empirical           Population Rate (2003): 0.076 per 100,000
  Performance         population

  Risk Adjustment     No risk adjustment
  ------------------- ---------------------------------------------------

**Summary**

This indicator is intended to flag cases of major reactions due to
transfusions (ABO and Rh). This indicator is defined both on a provider
level (by including cases based on secondary diagnosis associated with
the same hospitalization) and on an area level (by including all cases
of transfusion reactions).

**Panel Review**

The overall usefulness of this indicator was rated as very favorable by
panelists. This indicator includes only those events that result in
additional medical care. Some minor reactions may be missed, although
the panel suggested that these minor reactions are less clearly due to
medical error than the Rh or ABO reactions included in the indicator.

**Literature Review**

The project team was unable to find evidence on validity from prior
studies, most likely because this complication is quite rare.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Given the low rates or occurrences for Transfusion Reaction, the project
team did not measure reliability or minimum bias. The indicator could
not be risk-adjusted due to the small number of numerator cases. Users
of the PSI software should note the output will only contain observed
rates for Transfusion Reaction.

**Source**

This indicator was originally proposed by Iezzoni et al. as part of the
Complications Screening Program (CSP "sentinel events").[^127] It was
also included as one component of a broader indicator ("adverse events
and iatrogenic complications") in AHRQ's original HCUP Quality
Indicators.[^128] It was proposed by Miller et al. in the original "AHRQ
PSI Algorithms and Groupings." [^129]

## Birth Trauma―Injury to Neonate (PSI 17)

+------------------+---------------------------------------------------+
| Definition       | Cases of birth trauma, injury to neonate, per     |
|                  | 1,000 liveborn births.                            |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for birth trauma in |
|                  | any diagnosis field.                              |
|                  |                                                   |
|                  | Exclude infants                                   |
|                  |                                                   |
|                  | -   with a subdural or cerebral hemorrhage        |
|                  |     (subgroup of birth trauma coding) **and** any |
|                  |     diagnosis code of pre-term infant (denoting   |
|                  |     birth weight of less than 2,500 grams and     |
|                  |     less than 37 weeks gestation or 34 weeks      |
|                  |     gestation or less).                           |
|                  |                                                   |
|                  | -   with injury to skeleton (767.3, 767.4)        |
|                  |     **and** any diagnosis code of osteogenesis    |
|                  |     imperfecta (756.51).                          |
+------------------+---------------------------------------------------+
| Denominator      | Neonates in Metro Area or county with age at      |
|                  | admission of 0 to 28 days, with ICD-9-CM          |
|                  | diagnosis code for in-hospital live birth.        |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 5.531 per 1,000           |
| Performance      | population at risk\                               |
|                  | Bias: Did not undergo empirical testing of bias   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Sex                                               |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of birth trauma for infants
born alive in a hospital. The indicator excludes patients born pre-term,
as birth trauma in these patients may be less preventable than for
full-term infants.

**Panel Review**

The overall usefulness of this indicator was rated as favorable by
panelists

**Literature Review**

*Coding validity.* A study of newborns that had a discharge diagnosis of
birth trauma found that only 25% had sustained a significant injury to
the head, neck, or shoulder.[^130] The remaining patients either had
superficial injuries or injuries inferior to the neck. The project team
was unable to find other evidence on the validity of this indicator.
Towner et al. linked California maternal and infant discharge abstracts
from 1992 through 1994, but they used only infant discharge abstracts to
describe the incidence of neonatal intracranial injury, and they did not
report the extent of agreement between the two data sets.[^131]

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Birth Trauma generally performs well on several different dimensions,
including reliability, relatedness of indicators, and persistence over
time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is high, relative to other indicators, at 97.0%,
suggesting that observed differences in risk-adjusted rates reflect true
differences across hospitals.

The signal standard deviation for this indicator is also high, relative
to other indicators, at 0.04128, indicating that the systematic
differences (signal) among hospitals is high and more likely associated
with hospital characteristics. The signal share is also high, relative
to other indicators, at 0.13603. The signal share is a measure of the
share of total variation (hospital and patient) accounted for by
hospitals. The lower the share, the less important the hospital in
accounting for the rate and the more important other potential factors
(e.g., patient characteristics).

*Minimum bias*. The bias for Birth Trauma was not measured, since
adequate risk adjustment was not available.

**Source**

This indicator has been widely used in the obstetric community, although
it is most commonly based on chart review rather than administrative
data. It was proposed by Miller et al. in the original "AHRQ PSI
Algorithms and Groupings."[^132] Based on expert consensus panels,
McKesson Health Solutions included a broader version of this indicator
in its CareEnhance Resource Management Systems, Quality Profiler
Complications Measures Module.

## Obstetric Trauma―Vaginal Delivery with Instrument (PSI 18)

  ------------------- ---------------------------------------------------
  Definition          Cases of obstetric trauma (3^rd^ or 4^th^ degree
                      lacerations) per 1,000 instrument-assisted vaginal
                      deliveries.

  Numerator           Discharges with ICD-9-CM code for 3^rd^ and 4^th^
                      degree obstetric trauma in any diagnosis or
                      procedure field.

  Denominator         All vaginal delivery discharges with any procedure
                      code for instrument-assisted delivery.

  Type of Indicator   Provider level

  Empirical           Population Rate (2003): 191.006 per 1,000
  Performance         population at risk\
                      Bias: Did not undergo empirical testing of bias

  Risk Adjustment     Age
  ------------------- ---------------------------------------------------

**Summary**

This indicator is intended to flag cases of potentially preventable
trauma during vaginal delivery with instrument.

**Panel Review**

The overall usefulness of an Obstetric trauma indicator was rated as
favorable by panelists. After initial review, the indicator was
eventually split into three separate Obstetric Trauma indicators:
Vaginal Delivery with Instrument, Vaginal Delivery without Instrument,
and Cesarean Delivery.

**Literature Review**

*Coding validity.* In a stratified probability sample of vaginal and
Cesarean deliveries, the weighted sensitivity and predictive value of
coding for third- and fourth-degree lacerations and vulvar/perineal
hematomas (based on either diagnosis or procedure codes) were 89% and
90%, respectively.^158^ The authors did not report coding validity for
third- and fourth-degree lacerations separately. The project team was
unable to find other evidence on validity from prior studies.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Obstetric Trauma―Vaginal Delivery with Instrument generally performs
well on several different dimensions, including reliability, relatedness
of indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is moderately high, relative to other indicators, at
69.9%, suggesting that observed differences in risk-adjusted rates
likely reflect true differences across hospitals.

The signal standard deviation for this indicator is also high, relative
to other indicators, at 0.09794, indicating that the systematic
differences (signal) among hospitals is high and more likely associated
with hospital characteristics. The signal share is high, relative to
other indicators, at 0.05539. The signal share is a measure of the share
of total variation (hospital and patient) accounted for by hospitals.
The lower the share, the less important the hospital in accounting for
the rate and the more important other potential factors (e.g., patient
characteristics).

*Minimum bias*. The bias for Obstetric Trauma―Vaginal Delivery with
Instrument was not measured, since adequate risk adjustment was not
available.

**Source**

An overlapping subset of this indicator (third- or fourth-degree
perineal laceration) has been adopted by the Joint Commission for the
Accreditation of Healthcare Organizations (JCAHO) as a core performance
measure for "pregnancy and related conditions" (PR-25). Based on expert
consensus panels, McKesson Health Solutions included the JCAHO indicator
in its CareEnhance Resource Management Systems, Quality Profiler
Complications Measures Module. Fourth Degree Laceration, one of the
codes mapped to this PSI, was included as one component of a broader
indicator ("obstetrical complications") in AHRQ's original HCUP Quality
Indicators.[^133]

## Obstetric Trauma―Vaginal Delivery without Instrument (PSI 19)

+------------------+---------------------------------------------------+
| Definition       | Cases of obstetric trauma (3^rd^ or 4^th^ degree  |
|                  | lacerations) per 1,000 vaginal deliveries without |
|                  | instrument assistance.                            |
+------------------+---------------------------------------------------+
| Numerator        | Discharges with ICD-9-CM code for 3^rd^ and 4^th^ |
|                  | degree obstetric trauma in any diagnosis or       |
|                  | procedure field per 1,000 vaginal deliveries      |
|                  | without instrument assistance.                    |
+------------------+---------------------------------------------------+
| Denominator      | All vaginal delivery discharges.                  |
|                  |                                                   |
|                  | Exclude instrument-assisted delivery.             |
+------------------+---------------------------------------------------+
| Type of          | Provider level                                    |
| Indicator        |                                                   |
+------------------+---------------------------------------------------+
| Empirical        | Population Rate (2003): 46.340 per 1,000          |
| Performance      | population at risk\                               |
|                  | Bias: Did not undergo empirical testing of bias   |
+------------------+---------------------------------------------------+
| Risk Adjustment  | Age                                               |
+------------------+---------------------------------------------------+

**Summary**

This indicator is intended to flag cases of potentially preventable
trauma during a vaginal delivery without instrument.

**Panel Review**

The overall usefulness of an Obstetric Trauma Indicator was rated as
favorable by panelists. After initial review, the indicator was split
into three separate Obstetric Trauma indicators: Vaginal Delivery with
Instrument, Vaginal Delivery without Instrument, and Cesarean Delivery.

**Literature Review**

*Coding validity.* In a stratified probability sample of vaginal and
Cesarean deliveries, the weighted sensitivity and predictive value of
coding for third- and fourth-degree lacerations and vulvar/perineal
hematomas (based on either diagnosis or procedure codes) were 89% and
90%, respectively.^158^ The authors did not report coding validity for
third- and fourth-degree lacerations separately. The project team was
unable to find other evidence on validity from prior studies.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Obstetric Trauma―Vaginal Delivery without Instrument generally performs
well on several different dimensions, including reliability, relatedness
of indicators, and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is high, relative to other indicators, at 86.4%,
suggesting that observed differences in risk-adjusted rates reflect true
differences across hospitals.

The signal standard deviation for this indicator is also high, relative
to other indicators, at 0.04314, indicating that the systematic
differences (signal) among hospitals is high and more likely associated
with hospital characteristics. The signal share is lower than many other
indicators, at 0.02470. The signal share is a measure of the share of
total variation (hospital and patient) accounted for by hospitals. The
lower the share, the less important the hospital in accounting for the
rate and the more important other potential factors (e.g., patient
characteristics).

*Minimum bias*. The bias for Obstetric Trauma―Vaginal Delivery without
Instrument was not measured, since adequate risk adjustment was not
available.

**Source**

An overlapping subset of this indicator (third- or fourth-degree
perineal laceration) has been adopted by the Joint Commission for the
Accreditation of Healthcare Organizations (JCAHO) as a core performance
measure for "pregnancy and related conditions" (PR-25). Based on expert
consensus panels, McKesson Health Solutions included the JCAHO indicator
in its CareEnhance Resource Management Systems, Quality Profiler
Complications Measures Module. Fourth-Degree Laceration, one of the
codes mapped to this PSI, was included as one component of a broader
indicator ("obstetrical complications") in AHRQ's original HCUP Quality
Indicators.[^134]

## Obstetric Trauma―Cesarean Delivery (PSI 20)

  ------------------- ---------------------------------------------------
  Definition          Cases of obstetric trauma (3^rd^ or 4^th^ degree
                      lacerations) per 1,000 Cesarean deliveries.

  Numerator           Discharges with ICD-9-CM code for obstetric trauma
                      in any diagnosis or procedure field per 1,000
                      Cesarean deliveries.

  Denominator         All Cesarean delivery discharges.

  Type of Indicator   Provider level

  Empirical           Population Rate (2003): 4.315 per 1,000 population
  Performance         at risk\
                      Bias: Did not undergo empirical testing of bias

  Risk Adjustment     Age
  ------------------- ---------------------------------------------------

**Summary**

This indicator is intended to flag cases of potentially preventable
trauma during Cesarean delivery.

**Panel Review**

The overall usefulness of an Obstetric Trauma Indicator was rated as
favorable by panelists. After initial review, the indicator was
eventually split into three separate Obstetric Trauma indicators:
Vaginal Delivery with Instrument, Vaginal Delivery without Instrument,
and Cesarean Delivery.

**Literature Review**

*Coding validity.* In a stratified probability sample of vaginal and
Cesarean deliveries, the weighted sensitivity and predictive value of
coding for third- and fourth-degree lacerations and vulvar/perineal
hematomas (based on either diagnosis or procedure codes) were 89% and
90%, respectively.^158^ The authors did not report coding validity for
third- and fourth-degree lacerations separately. The project team was
unable to find other evidence on validity from prior studies.

**Empirical Analysis**

The project team conducted extensive empirical analyses on the PSIs.
Obstetric Trauma―Cesarean Delivery generally performs well on several
different dimensions, including reliability, relatedness of indicators,
and persistence over time.

*Reliability.* The signal ratio―measured by the proportion of the total
variation across hospitals that is truly related to systematic
differences (signal) in hospital performance rather than random
variation (noise)―is lower than many indicators, at 45.9%, suggesting
that observed differences in risk-adjusted rates may not reflect true
differences across hospitals.

The signal standard deviation for this indicator is also lower than many
indicators, at 0.00590, indicating that the systematic differences
(signal) among hospitals is low and less likely associated with hospital
characteristics. The signal share is lower than many indicators, at
0.00576. The signal share is a measure of the share of total variation
(hospital and patient) accounted for by hospitals. The lower the share,
the less important the hospital in accounting for the rate and the more
important other potential factors (e.g., patient characteristics).

*Minimum bias*. The bias for Obstetric Trauma―Cesarean Delivery was not
measured, since adequate risk adjustment was not available.

**Source**

An overlapping subset of this indicator (third- or fourth-degree
perineal laceration) has been adopted by the Joint Commission for the
Accreditation of Healthcare Organizations (JCAHO) as a core performance
measure for "pregnancy and related conditions" (PR-25). Based on expert
consensus panels, McKesson Health Solutions included the JCAHO indicator
in its CareEnhance Resource Management Systems, Quality Profiler
Complications Measures Module. Fourth Degree Laceration, one of the
codes mapped to this PSI, was included as one component of a broader
indicator ("obstetrical complications") in AHRQ's original HCUP Quality
Indicators.[^135]

# Using Different Types of QI Rates

When should you use the observed, expected, risk adjusted, and/or
smoothed rates generated by the AHRQ QI software? Here are some
guidelines.

If the user's primary interest is to identify cases for further
follow-up and quality improvement, then the observed rate would help to
identify them. The observed rate is the raw rate generated by the QI
software from the data the user provided. Areas for improvement can be
identified by the magnitude of the observed rate compared to available
benchmarks and/or by the number of patients impacted.

Additional breakdowns by the default patient characteristics used in
stratified rates (e.g., age, gender, or payer) can further identify the
target population. Target populations can also be identified by
user-defined patient characteristics supplemented to the case/discharge
level flags. Trend data can be used to measure change in the rate over
time.

Another approach to identify areas to focus on is to compare the
observed and expected rates. The expected rate is the rate the provider
would have if it performed the same as the reference population given
the provider's actual case-mix (e.g., age, gender, DRG, and comorbidity
categories).

If the observed rate is higher than the expected rate (i.e., the ratio
of observed/expected is greater than 1.0, or observed minus expected is
positive), then the implication is that the provider performed worse
than the reference population for that particular indicator. Users may
want to focus on these indicators for quality improvement.

If the observed rate is lower than the expected rate (i.e., the ratio of
observed/expected is less than 1.0, or observed minus expected is
negative), then the implication is that the provider performed better
than the reference population. Users may want to focus on these
indicators for identifying best practices.

Users can also compare the expected rate to the population rate reported
in the detailed evidence section of the IQI, PQI, or PSI Guide to
determine how their case-mix compares to the reference population. If
the population rate is higher than the expected rate, then the
provider's case-mix is less severe than the reference population. If the
population rate is lower than the expected rate, then the provider's
case-mix is more severe than the reference population.

We use this difference between the population rate and the expected rate
to "adjust" the observed rate to account for the difference between the
case-mix of the reference population and the provider's case-mix. This
is the provider's risk-adjusted rate.

If the provider has a less severe case-mix, then the adjustment is
positive (population rate \> expected rate) and the risk-adjusted rate
is higher than the observed rate. If the provider has a more severe
case-mix, then the adjustment is negative (population rate \< expected
rate) and the risk-adjusted rate is lower than the observed rate. The
risk-adjusted rate is the rate the provider would have if it had the
same case-mix as the reference population given the provider's actual
performance.

Finally, users can compare the risk-adjusted rate to the smoothed or
"reliability-adjusted" rate to determine whether this difference between
the risk-adjusted rate and reference population rate is likely to remain
in the next measurement period. Smoothed rates are weighted averages of
the population rate and the risk-adjusted rate, where the weight
reflects the reliability of the provider's risk-adjusted rate.

A ratio of (smoothed rate - population rate) / (risk-adjusted rate -
population rate) greater than 0.80 suggests that the difference is
likely to persist (whether the difference is positive or negative). A
ratio less than 0.80 suggests that the difference may be due in part to
random differences in patient characteristics (patient characteristics
that are not observed and controlled for in the risk-adjustment model).
In general, users may want to focus on areas where the differences are
more likely to persist.

# References

Ball JK, Elixhauser A, Johantgen M, et al. HCUP Quality Indicators,
Methods, Version 1.1: Outcome, Utilization, and Access Measures for
Quality Improvement. (AHCPR Publication No. 98-0035). Healthcare Cost
and Utilization project (HCUP-3) Research notes: Rockville, MD: Agency
for Health Care Policy and Research, 1998.

Barbour GL. Usefulness of a discharge diagnosis of sepsis in detecting
iatrogenic infection and quality of care problems. Am J Med Qual
1993;8(1):2-5.

Berlowitz D, Brand H, Perkins C. Geriatric syndromes as outcome measures
of hospital care: Can administrative data be used? JAGS 1999;47:692-696.

Best W, Khuri S, Phelan M, Hur K, Henderson W, Demakis J, et al.
Identifying patient preoperative risk factors and postoperative adverse
events in administrative databases: Results from the Department of
Veterans Affairs National Surgical Quality Improvement Program. J Am
Coll Surg 2002;194(3):257-266.

Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers AG, et
al. Incidence of adverse events and negligence in hospitalized patients.
Results of the Harvard Medical Practice Study I. N Engl J Med
1991;324(6):370-6.

Christiansen CL, Morris CN. Improving the statistical approach to health
care provider profiling. Ann Intern Med 1997;127(8 Pt 2):764-8).

Davies S, Geppert J, McClellan M, McDonald KM, Romano PS, Shojania KG.
Refinement of the HCUP Quality Indicators. Technical Review Number 4.
Rockville, MD: (Prepared by UCSF-Stanford Evidence-based Practice Center
under Contract No. 290-97-0013) Agency for Healthcare Research and
Quality; 2001. Report No.: 01-0035.

EMBASE. In. The Netherlands: Elsevier Science Publishers B.V.

Envisioning the National Health Care Quality Report. Washington, DC:
Institute of Medicine; 2001.

Faciszewski T, Johnson L, Noren C, Smith MD. Administrative databases'
complication coding in anterior spinal fusion procedures. What does it
mean? Spine 1995;20(16):1783-8.

Fitch K, Bernstein J, Aguilar MD, Burnand B, LaCalle JR, Lazaro P, et
al. the RAND/UCLA Appropriateness Method User's Manual: RAND; 2001.

Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L. In-hospital
complications among survivors of admission for congestive heart failure,
chronic obstructive pulmonary disease, or diabetes mellitus. J Gen
Intern Med 1995;10(6):307-14.

Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L. International
Classification of Diseases, 9^th^ Revision, Clinical Modification codes
in discharge abstracts are poor measures of complication occurrence in
medical inpatients. Med Care 1997;35(6):589-602.

Green L, Lewis F. measurement and Evaluation in Health Education and
Health Promotion. Mountain View, CA: Mayfield Publishing Company; 1998.

Hannan EL, Bernard HR, O'Donnell JF, Kilburn H, Jr. A methodology for
targeting hospital cases for quality of care record reviews. Am J Public
Health 1989;79(4):430-6.

Hartz AJ, Kuhn EM. Comparing hospitals that perform coronary artery
bypass surgery: The effect of outcome measures and data sources. Am J
Public Health 1994;84(10):1609-14.

Hawker BA, Coyte PC, Wright JG, Paul JE, Bombardier C. Accuracy of
administrative data for assessing outcomes after knee replacement
surgery. J Clin Epidemiol 1997;50(3):265-73.

Hofer TP, Hayward RA, Greenfield S, Wagner EH, Kaplan SH, Manning WG.
The unreliability of individual physician "report cards" for assessing
the costs and quality of care of a chronic disease JAMA
1999;281(22):2098-105.

Hughes C, Harley E, Milmoe G, Bala R, Martorella A. Birth trauma in the
head and neck. Arch Otolaryngol Head Neck Surg 1999;125:193-199.

Iezzoni L, Lawthers A, Petersen L, McCarthy E, Palmer R, Cahalane M, et
al. Project to validate the Complications Screening Program: Health Care
Financing Administration; 1998 March 31. Report No: HCFA Contract
500-94-0055.

Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et al.
Identifying complications of care using administrative data. Med Care
1994;32(7):700-15.

Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K, et al.
Does the Complications Screening Program flag cases with process of care
problems? Using explicit criteria to judge processes. Int J Qual Health
Care 1999;11(2):107-18.

Iezzoni LI, Foley SM, Heeren T, Daley J, Duncan CC, Fisher ES, et al. A
method for screening the quality of hospital care using administrative
data: preliminary validation results. QRB Qual Rev Bull
1992;18(11):361-71.

Impact: Case Studies Notebook -- Documented Impact and Use of AHRQ\'s
Research. Compiled by Division of Public Affairs, Office of Health Care
Information, Agency for Healthcare Research and Quality.

Institute of Medicine. To Err is Human: Building a Safer Health System.
Kohn LT, Corrigan JM, Donaldson MS (eds.) Washington DC: National
Academy Press, 2000.

Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR. Quality
indicators using hospital discharge data: state and national
applications. Jt Comm J Qual Improv 1998;24(2):88-105.

Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR. Quality
indicators using hospital discharge data: state and national
applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published erratum
appears in Jt Comm J Qual Improv 1998;24(6):341.

Keeler E, Kahn K, Bentow S. Assessing quality of care for hospitalized
Medicare patients with hip fracture using coded diagnoses from the
Medicare Provider Analysis and Review file. Springfield, VA: NTIS; 1991.

Kovner C, Gergen PH. Nurse staffing levels and adverse events following
surgery in U.S. hospitals. Image J Nurs Sch 1998;30(4):315-21.

Lawthers A, McCarthy E, Davis R, Peterson L, Palmer R, Iezzoni L.
Identification of in-hospital complications from claims data: is it
valid? Medical Care 2000;38(8):785-795.

Lichtig LK, Knauf RA, Hilholland DK. Some impacts of nursing on acute
care hospital outcomes. J Nurs Adm 1999;29(2):25-33.

McCarthy EP, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamael MB, et
al. Does clinical evidence support ICD-9-CM diagnosis coding of
complications? Med Care 2000;38(8);868-876.

McDonald KM, Romano PS, Geppert J, Davies S, Duncan BW, Shojania KG.
Measures of Patient Safety Based on Hospital Administrative Data-The
Patient Safety Indicators. Technical Review 5 (Prepared by the
University of California San Francisco-Stanford Evidence-based Practice
Center under Contract No. 290-97-0013). AHRQ Publication No. 02-0038 .
Rockville, MD: Agency for Healthcare Research and Quality. August 2002.
(<http://www.qualityindicators.ahrq.gov/downloads.htm>)

Measuring the Quality of Health Care: A statement of the National
Roundtable on Healthcare Quality Division of Healthcare Services:
National Academy Press; 1999.

MEDLINE \[database online\]. In. Bethesda (MD): National Library of
Medicine.

Miller M, Elixhauser A, Zhan C, Meyer G, Patient Safety Indicators:
Using administrative data to identify potential patient safety concerns.
Health Services Research 2001;36(6 Part II):110-132.

National Roundtable on Healthcare Quality, 1999.

Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K. Nurse
Staffing and Patient Outcomes in Hospitals. Boston, MA: Health Resources
Services Administration; 2001 February 28. Report No.: 230-88-0021.

Nursing-Sensitive Quality Indicators for Acute Care Settings and ANA's
Safety & Quality Initiative. In: American Nurses Association; 1999.

Romano P. Can administrative data be used to ascertain clinically
significant postoperative complications. American Journal of Medical
Quality Press.

Shojania KG, Duncan BW, McDonald KM, Wachter RM. Making health care
safer: A critical analysis of patient safety practices. Evidence
Report/Technology Assessment No. 43 (Prepared by the University of
California at San Francisco-Stanford Evidence-based Practice Center
under Contract No. 290-97-0013). Rockville, MD: Agency for Healthcare
Research and Quality; 2001. Report No.: AHRQ Publication No. 01-E058.

Silber J, Rosenbaum P, Ross R. Comparing the contributions of groups of
predictors: Which outcomes vary with hospital rather than patient
characteristics? J Am Stat Assoc 1995;90:7-18.

Silber JH, Rosenbaum PR, Williams SV, Ross RN, Schwartz JS. The
relationship between choice of outcome measure and hospital rank in
general surgical procedures: Implications for quality assessment. Int J
Qual Health Care 1997;9(3):193-200.

Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient
characteristics associated with death after surgery. A study of adverse
occurrence and failure to rescue. Med Care 1992;30(7):615-29.

Taylor B. Common bile duct injury during laparoscopic cholecystectomy in
Ontario: Does ICD-9 coding indicate true incidence? CMAJ
1998;158(4):481-5.

Towner D, Castro MA, Eby-Wilkens E, Gilbert WM. Effect of mode of
delivery in nulliparous women on neonatal intracranial injury. N Engl J
Med 1999;341(23):1709-14.

Valinsky LJ, Hockey RI, Hobbs MS, Fletcher DR, Pikora TJ, Parsons RW, et
al. Finding bile duct injuries using record linkage: A validated study
of complications following cholecystectomy. J Clin Epidemiol
1999;52(9):893-901.

Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, et
al. Use of administrative data to find substandard care: Validation of
the Complications Screening Program. Med Care 2000;38(8):796-806.

White RH, Romano P, Zhou H, Rodrigo J, Barger W. Incidence and time
course of thromboembolic outcomes following total hip or knee
arthroplasty. Arch Intern Med 1998;158(14):1525-31

.

####### 

# Appendix A: Links {#appendix-a-links .HeadOther}

The following links may be helpful to users of the AHRQ Patient Safety
Indicators.

**Patient Safety Indicators Version 3.0 Documents and Software**

Available at <http://www.qualityindicators.ahrq.gov/psi_download.htm>

+---------------------+------------------------------------------------+
| ***Title***         | ***Description***                              |
+=====================+================================================+
| *Guide to Patient   | Describes how the PSIs were developed and      |
| Safety Indicators*  | provides detailed evidence for each indicator. |
+---------------------+------------------------------------------------+
| *Patient Safety     | Provides detailed definitions of each PSI,     |
| Indicators          | including all ICD-9-CM and DRG codes that are  |
| Technical           | included in or excluded from the numerator and |
| Specifications*     | denominator. Note that exclusions from the     |
|                     | denominator are automatically applied to the   |
|                     | numerator.                                     |
+---------------------+------------------------------------------------+
| *PSI Covariates     | Tables for each PSI provide the stratification |
| used in Risk        | and coefficients used to calculate the         |
| Adjustment*         | risk-adjusted rate for each strata.            |
+---------------------+------------------------------------------------+
| *SAS® PSI Software  | This software documentation provides detailed  |
| Documentation*      | instructions on how to use the SAS ® version   |
|                     | of the PSI software including data             |
|                     | preparation, calculation of the PSI rates, and |
|                     | interpretation of output.                      |
+---------------------+------------------------------------------------+
| *SPSS® PSI Software | This software documentation provides detailed  |
| Documentation*      | instructions on how to use the SPSS® version   |
|                     | of the PSI software including data             |
|                     | preparation, calculation of the PSI rates, and |
|                     | interpretation of output.                      |
+---------------------+------------------------------------------------+
| *Change Log to PSI  | The Change Log document provides a cumulative  |
| Documents and       | summary of all changes to the PSI software,    |
| Software*           | software documentation, and other documents    |
|                     | made since the release of version 2.1 of the   |
|                     | software in March 2003. Changes to indicator   |
|                     | specifications that were not a result of new   |
|                     | ICD-9-CM and DRG codes, are also described in  |
|                     | the Change Log.                                |
+---------------------+------------------------------------------------+
| *Fiscal year 2006   | This document summarizes the changes to the    |
| Coding Changes*     | indicator definitions resulting from FY 2006   |
|                     | changes to ICD-9-CM coding and DRG changes.    |
|                     | These changes will only affect data from FY    |
|                     | 2006 (October 1, 2005) or later.               |
+---------------------+------------------------------------------------+
| SAS® PSI Software   | Requires the SAS® statistical program          |
|                     | distributed by the SAS Institute, Inc. The     |
|                     | company may be contacted directly regarding    |
|                     | the licensing of its products:                 |
|                     |                                                |
|                     | > [http://www.sas.com](http://www.sas.com/)    |
+---------------------+------------------------------------------------+
| SPSS® PSI Software  | Requires the SPSS® statistical program         |
|                     | distributed by SPSS, Inc. The company may be   |
|                     | contacted directly regarding the licensing of  |
|                     | its products:                                  |
|                     |                                                |
|                     | > [http://www.spss.com](http://www.spss.com/)  |
+---------------------+------------------------------------------------+

**AHRQ QI Windows Application**

The AHRQ QI Windows Application calculates rates for all of the AHRQ
Quality Indicators modules and does not require either SAS® or SPSS®. It
is available at:

> <http://www.qualityindicators.ahrq.gov/winqi_download.htm>

**Additional Documents**

The following documents are available within the \"Documentation\"
section of the AHRQ QI Downloads Web page:

> ([[http://www.qualityindicators.ahrq.gov/downloads.htm]{.underline}](http://www.qualityindicators.ahrq.gov/downloads.htm)).

-   *Refinement of the HCUP Quality Indicators (Technical Review), May
    2001*

-   *Refinement of the HCUP Quality Indicators (Summary), May 2001*

-   *Measures of Patient Safety Based on Hospital Administrative Data -
    The Patient Safety Indicators, August 2002*

-   *Measures of Patient Safety Based on Hospital Administrative Data -
    The Patient Safety Indicators (Summary), August 2002*

In addition, these documents may be accessed at the AHRQ QI
Documentation Web page:

> <http://www.qualityindicators.ahrq.gov/documentation.htm>

-   *Guidance for Using the AHRQ Quality Indicators for Hospital-level
    Public Reporting or Payment, August 2004*

-   *AHRQ Summary Statement on Comparative Hospital Public Reporting,
    December 2005*

-   *Appendix A: Current Uses of AHRQ Quality Indicators and
    Considerations for Hospital-level*

-   *Comparison of Recommended Evaluation Criteria in Five E*xisting
    National Frameworks

The following documents can be viewed or downloaded from the page:

> <http://www.qualityindicators.ahrq.gov/newsletter.htm>

-   *2006 Area Level Indicator Changes*

-   *Considerations in Public Reporting for the AHRQ QIs*

-   *June 2005 Newsletter -* Contains the article, \"Using Different
    Types of QI Rates\"

**Other Tools and Information**

The PSI SAS software no longer incorporates the AHRQ Comorbidity
software. Before running the PSI software, the user will need to
download and run program available at:

> <http://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp>

That program will create the comorbidity variables in the user's data
file. These variables are only needed if the user intends to calculate
risk-adjusted rates using PSSASP3.

Area indicators can be calculated using the modified Federal Information
Processing Standards (FIPS) State/county code. A list of codes is
available at:

> <http://www.census.gov/popest/geographic/codes02.pdf>

AHRQ provides a free, on-line query system based on HCUP data that
provides access to health statistics and information on hospital stays
at the national, regional, and State level. It is available at:

> <http://hcup.ahrq.gov/HCUPnet.asp>

[^1]: .
    <http://www.cms.hhs.gov/NationalHealthExpendData/downloads/nheprojections2004-2014.pdf>:
    Table 2 National Health Expenditure Amounts, and Annual Percent
    Change by Type of Expenditure: Selected Calendar Years 1998-2014.

[^2]: Strunk BC, Ginsburg PB, Gabel JR. Tracking Health Care Costs.
    Health Affairs, 26 September 2001 (Web exclusive).

[^3]: Institute of Medicine. To Err is Human: Building a Safer Health
    System. Kohn LT, Corrigan JM, Donaldson MS (eds.) Washington DC:
    National Academy Press, 2000.

[^4]: Institute of Medicine. Crossing the Quality Chasm: A New Health
    System for the 21^st^ Century. Committee of Quality of Care in
    America. Washington DC: National Academy Press, 2001.

[^5]: Agency for Healthcare Research and Quality. *National Healthcare
    Quality Report*. Rockville, MD, U.S. Department of Health and Human
    Services, Agency for Healthcare Research and Quality, December 2003.

[^6]: Agency for Healthcare Research and Quality. *National Healthcare
    Disparities Report*. Rockville, MD, U.S. Department of Health and
    Human Services, Agency for Healthcare Research and Quality, July
    2003.

[^7]: The 2005 NHQR and NHDR reports are available at
    <http://www.qualitytools.ahrq.gov/>.

[^8]: Texas Center for Health Statistics. *Indicators of Inpatient Care
    in Texas Hospitals, 2003*.
    <http://www.dshs.state.tx.us/THCIC/Publications/Hospitals/IQIReport2003/IQIReport2003.shtm>.
    Accessed January 2006.

[^9]: Niagara Health Quality Coalition. *2005 New York State Hospital
    Report Card,*
    .<http://www.myhealthfinder.com/newyork05/glancechoose.htm>.
    Accessed January 2006.

[^10]: Centers for Medicare & Medicaid Services. *The Premier Hospital
    Quality Incentive Demonstration*.
    <http://www.cms.hhs.gov/HospitalQualityInits/downloads/HospitalPremierFactSheet.pdf>.
    Accessed January 2006.

[^11]: AHRQ Summary Statement on Hospital Public Reporting.
    <http://www.qualityindicators.ahrq.gov/news/AHRQSummaryStatement.pdf>.

[^12]: Remus D, Fraser I. *Guidance for Using the AHRQ Quality
    Indicators for Hospital-level Public Reporting or Payment*.
    Rockville, MD: Department of Health and Human Services, Agency for
    Healthcare Research and Quality; 2004. AHRQ Pub. No. 04-0086-EF. The
    document may be downloaded from the AHRQ Quality Indicator website
    at <http://www.qualityindicators.ahrq.gov/documentation.htm>.

[^13]: Ball JK, Elixhauser A, Johantgen M, et al. *HCUP Quality
    Indicators, Methods, Version 1.1: Outcome, Utilization, and Access
    Measures for Quality Improvement*. (AHCPR Publication No. 98-0035).
    Healthcare Cost and Utilization project (HCUP-3) Research notes:
    Rockville, MD: Agency for Health Care Policy and Research, 1998.

[^14]: *Impact: Case Studies Notebook -- Documented Impact and Use of
    AHRQ\'s Research*. Compiled by Division of Public Affairs, Office of
    Health Care Information, Agency for Healthcare Research and Quality.

[^15]: Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers
    AG, et al. Incidence of adverse events and negligence in
    hospitalized patients. Results of the Harvard Medical Practice Study
    I. N Engl J Med 1991;324(6):370-6.

[^16]: Institute of Medicine, 2000.

[^17]: Envisioning the National Health Care Quality Report. Washington,
    DC: Institute of Medicine; 2001.

[^18]: Brennan et al., 1991.

[^19]: Measuring the Quality of Health Care: A statement of the National
    Roundtable on Healthcare Quality Division of Healthcare Services:
    National Academy Press; 1999.

[^20]: National Roundtable on Healthcare Quality, 1999.

[^21]: Davies S, Geppert J, McClellan M, McDonald KM, Romano PS,
    Shojania KG. Refinement of the HCUP Quality Indicators. Technical
    Review Number 4. Rockville, MD: (Prepared by UCSF-Stanford
    Evidence-based Practice Center under Contract No. 290-97-0013)
    Agency for Healthcare Research and Quality; 2001. Report No.:
    01-0035.

[^22]: McDonald KM, Romano PS, Geppert J, Davies S, Duncan BW, Shojania
    KG. Measures of Patient Safety Based on Hospital Administrative
    Data-The Patient Safety Indicators. Technical Review 5 (Prepared by
    the University of California San Francisco-Stanford Evidence-based
    Practice Center under Contract No. 290-97-0013). AHRQ Publication
    No. 02-0038 . Rockville, MD: Agency for Healthcare Research and
    Quality. August 2002.

[^23]: McDonald et al., 2002.

[^24]: Iezzoni LI, Foley SM, Heeren T, Daley J, Duncan CC, Fisher ES, et
    al. A method for screening the quality of hospital care using
    administrative data: preliminary validation results. QRB Qual Rev
    Bull 1992;18(11):361-71.

[^25]: EMBASE. In. The Netherlands: Elsevier Science Publishers B.V.

[^26]: MEDLINE \[database online\]. In. Bethesda (MD): National Library
    of Medicine.

[^27]: Iezzoni et al., 1992.

[^28]: Lawthers A, McCarthy E, Davis R, Peterson L, Palmer R, Iezzoni L.
    Identification of in-hospital complications from claims data: is it
    valid? Medical Care 2000;38(8):785-795.

[^29]: McCarthy EP, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamael
    MB, et al. Does clinical evidence support ICD-9-CM diagnosis coding
    of complications? Med Care 2000;38(8);868-876.

[^30]: Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel
    MB, et al. Use of administrative data to find substandard care:
    validation of the complications screening program Med Care
    2000;38(8):796-806.

[^31]: Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K,
    et al. Does the Complications Screening Program flag cases with
    process of care problems? Using explicit criteria to judge
    processes. Int J Qual Health Care 1999;11(2):107-18.

[^32]: Miller M, Elixhauser A, Zhan C, Meyer G. Patient Safety
    Indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^33]: Shojania KG, Duncan BW, McDonald KM, Wachter RM. Making health
    care safer: A critical analysis of patient safety practices.
    Evidence Report/Technology Assessment No. 43 (Prepared by the
    University of California at San Francisco-Stanford Evidence-based
    Practice Center under Contract No. 290-97-0013). Rockville, MD:
    Agency for Healthcare Research and Quality; 2001. Report No.: AHRQ
    Publication No. 01-E058.

[^34]: Lawthers, et al., 2000.

[^35]: McCarthy, et al., 2000.

[^36]: Weingart et al., 2000.

[^37]: Iezzoni et al., 1999.

[^38]: Fitch K, Bernstein J, Aguilar MD, Burnand B, LaCalle JR, Lazaro
    P, et al. the RAND/UCLA Appropriateness Method User's Manual: RAND;
    2001.

[^39]: Green L, Lewis F. measurement and Evaluation in Health Education
    and Health Promotion. Mountain View, CA: Mayfield Publishing
    Company; 1998.

[^40]: The state data organizations that participated in the 2003 HCUP
    SID: Arizona Department of Health Services; California Office of
    Statewide Health Planning & Development; Colorado Health & Hospital
    Association; Connecticut - Chime, Inc.; Florida Agency for Health
    Care Administration; Georgia: An Association of Hospitals & Health
    Systems; Hawaii Health Information Corporation; Illinois Health Care
    Cost Containment Council; Indiana Hospital & Health Association;
    Iowa Hospital Association; Kansas Hospital Association; Kentucky
    Department for Public Health; Maine Health Data Organization;
    Maryland Health Services Cost Review; Massachusetts Division of
    Health Care Finance and Policy; Michigan Health & Hospital
    Association; Minnesota Hospital Association; Missouri Hospital
    Industry Data Institute; Nebraska Hospital Association; Nevada
    Department of Human Resources; New Hampshire Department of Health &
    Human Services; New Jersey Department of Health & Senior Services;
    New York State Department of Health; North Carolina Department of
    Health and Human Services; Ohio Hospital Association; Oregon
    Association of Hospitals & Health Systems; Pennsylvania Health Care
    Cost Containment Council; Rhode Island Department of Health; South
    Carolina State Budget & Control Board; South Dakota Association of
    Healthcare Organizations; Tennessee Hospital Association; Texas
    Health Care Information Council; Utah Department of Health; Vermont
    Association of Hospitals and Health Systems; Virginia Health
    Information; Washington State Department of Health; West Virginia
    Health Care Authority; Wisconsin Department of Health & Family
    Services.

[^41]: Hofer TP, Hayward RA, Greenfield S, Wagner EH, Kaplan SH, Manning
    WG. The unreliability of individual physician "report cards" for
    assessing the costs and quality of care of a chronic disease JAMA
    1999;281(22):2098-105.

[^42]: Christiansen CL, Morris CN. Improving the statistical approach to
    health care provider profiling. Ann Intern Med 1997;127(8 Pt
    2):764-8.

[^43]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^44]: Hannan EL, Bernard HR, O'Donnell JF, Kilburn H, Jr. A methodology
    for targeting hospital cases for quality of care record reviews. Am
    J Public Health 1989;79(4):430-6.

[^45]: Hannan et al. 1989.

[^46]: Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L.
    International Classification of Diseases, 9^th^ Revision, Clinical
    Modification codes in discharge abstracts are poor measures of
    complication occurrence in medical inpatients. Med Care
    1997;35(6):589-602.

[^47]: Keeler E, Kahn K, Bentow S. Assessing quality of care for
    hospitalized Medicare patients with hip fracture using coded
    diagnoses from the Medicare Provider Analysis and Review file.
    Springfield, VA: NTIS; 1991.

[^48]: Berlowitz D, Brand H, Perkins C. Geriatric syndromes as outcome
    measures of hospital care: Can administrative data be used? JAGS
    1999;47:692-696.

[^49]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston, MA: Health
    Resources Services Administration; 2001 February 28. Report No.:
    230-88-0021.

[^50]: Lichtig LK, Knauf RA, Hilholland DK. Some impacts of nursing on
    acute care hospital outcomes. J Nurs Adm 1999;29(2):25-33.

[^51]: Iezzoni LI, Daley J, Heeren T, Foley SM, Risher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^52]: Needleman et al. 2001.

[^53]: Nursing-Sensitive Quality Indicators for Acute Care Settings and
    ANA's Safety & Quality Initiative. In: American Nurses Association;
    1999.

[^54]: Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and
    patient characteristics associated with death after surgery. A study
    of adverse occurrence and failure to rescue. Med Care
    1992;30(7):615-29.

[^55]: Silber J, Rosenbaum P, Ross R. Comparing the contributions of
    groups of predictors: Which outcomes vary with hospital rather than
    patient characteristics? J Am Stat Assoc 1995;90:7-18.

[^56]: Silber JH, Rosenbaum PR, Williams SV, Ross RN, Schwartz JS. The
    relationship between choice of outcome measure and hospital rank in
    general surgical procedures: Implications for quality assessment.
    Int J Qual Health Care 1997;9(3):193-200.

[^57]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston MA: Health
    Resources and Services Administration; 2001 February 28. Report
    No.:230-99-0021.

[^58]: Silber et al. 1992.

[^59]: Needleman et al. 2001.

[^60]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^61]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: state and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-105.

[^62]: Miller M, Elixhauser A, Zhan C, Meyer G. Patient safety
    indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^63]: Miller M, Elixhauser A, Zhan C, Meyer G. Patient safety
    indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^64]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^65]: Miller M, Elixhauser A, Zhan C, Meyer G. Patient safety
    indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^66]: Nursing-Sensitive Quality Indicators for Acute Care Settings and
    ANA's Safety and Quality Initiative. In: American Nurses
    Association; 1999.

[^67]: Lawthers A, McCarthy E, Davis R, Peterson L, Palmer R, Iezzoni L.
    Identification of in-hospital complications from claims data: Is it
    valid? Med Care 2000;38(8):785-795.

[^68]: Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel
    MB, et al. Use of administrative data to find substandard care:
    Validation of the Complications Screening Program. Med Care
    2000;38(8):796-806.

[^69]: Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K,
    et al. Does the Complications Screening Program flag cases with
    process of care problems: Using explicit criteria to judge
    processes. Int J Qual Health Care 1999;11(2):107-18.

[^70]: Weingart et al. 2000.

[^71]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^72]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston, MA: Health
    Resources Services Administration; 2001 February 28. Report No.:
    230-99-0021.

[^73]: Nursing-Sensitive Quality Indicators for Acute Care Settings and
    ANA's Safety & Quality Initiative. In: American Nurses Association;
    1999.

[^74]: Lawthers A, McCarthy E, Davis R, Peterson L, Palmer R, Iezzoni L.
    Identification of in-hospital complications from claims data: Is it
    valid? Med Care 2000;38(8):785-795.

[^75]: McCarthy EP, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel
    MB, et al. Does clinical evidence support ICD-9-CM diagnosis coding
    of complications? Med Care 2000;38(8):868-876.

[^76]: Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel
    MB, et al. Use of administrative data to find substandard care:
    Validation of the Complications Screening Program. Med Care
    2000;38(8):796-806.

[^77]: Hartz AJ, Kuhn EM. Comparing hospitals that perform coronary
    artery bypass surgery: The effect of outcome measures and data
    sources. Am J Public Health 1994;84(10):1609-14.

[^78]: Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K,
    et al. Does the complications Screening Program flag case with
    process of care problems? Using explicit criteria to judge
    processes. Int J Qual Health Care 1999;11(2):107-18.

[^79]: Iezzoni L, Lawthers A, Petersen L, McCarthy E, Palmer R, Cahalane
    M, et al. Project to validate the Complications Screening Program:
    Health Care Financing Administration; 1998 March 31. Report No: HCFA
    Contract 500-94-0055.

[^80]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^81]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-105. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^82]: Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L.
    International Classification of Diseases, 9^th^ Revision, Clinical
    Modification codes in discharge abstracts are poor measures of
    complication occurrence in medical inpatients. Med Care
    1997;35(6):589-602.

[^83]: Romano P. Can administrative data be used to ascertain clinically
    significant postoperative complications. American Journal of Medical
    Quality Press.

[^84]: Best W, Khuri S, Phelan M, Hur K, Henderson W, Demakis J, et al.
    Identifying patient preoperative risk factors and postoperative
    adverse events in administrative databases: Results from the
    Department of Veterans Affairs National Surgical Quality Improvement
    Program. J Am Coll Surg 2002;194(3):257-266.

[^85]: Hannan EL, Bernard HR, O'Donnell JF, Kilburn H, Jr. A methodology
    for targeting hospital cases for quality of care record reviews. Am
    J Public Health 1989;79(4):430-6.

[^86]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^87]: Lawthers a, McCarthy E, Davis R, Peterson L, Palmer R, Iezzoni L.
    Identification of in-hospital complications from claims data: is it
    valid? Med Care 2000;38(8):785-795.

[^88]: Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel
    MB, et al. Use of administrative data to find substandard care:
    Validation of the Complications Screening Program. Med Care
    2000;38(8):796-806.

[^89]: Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L.
    In-hospital complications among survivors of admission for
    congestive heart failure, chronic obstructive pulmonary disease, or
    diabetes mellitus. J Gen Intern Med 1995;10(6):307-14.

[^90]: Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K,
    et al. Does the Complications Screening Program flag cases with
    process of care problems? Using explicit criteria to judge
    processes. Int J Qual Health Care 1999;11(2):107-18.

[^91]: Hawker GA, Coyte PC, Wright JG, Paul JE, Bombardier C. Accuracy
    of administrative data for assessing outcomes after knee replacement
    surgery. J. Clin Epidimiol 1997;50(3):265-73.

[^92]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston, MA: Health
    Resources Services Administration; 2001 February 28. Report
    No.:230-99-0021.

[^93]: Kovner C, Gergen PJ. Nurse staffing levels and adverse events
    following surgery in U.S. hospitals. Image J Nurs Sch
    1998;30(4):315-21.

[^94]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^95]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^96]: Needleman et al. 2001.

[^97]: Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L.
    In-hospital complications among survivors of admission for
    congestive heart failure, chronic obstructive pulmonary disease, or
    diabetes mellitus. J Gen Intern Med 1995;10(6):307-14.

[^98]: Keeler E, Kahn K, Bentow S. Assessing quality of care for
    hospitalized Medicare patients with hip fracture using coded
    diagnoses from the Medicare Provider Analysis and Review File.
    Springfield, VA: NTIS;1991.

[^99]: White RH, Romano P, Zhou H, Rodrigo J, Barger W. Incidence and
    time course of thromboembolic outcomes following total hip or knee
    arthroplasty. Arch Intern Med 1998;158(14):1525-31.

[^100]: Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal
    K, et al. Does the Complications Screening Program flag cases with
    process of care problems? Using explicit criteria to judge
    processes. Int J Qual Health Care 1999;11(2):107-18.

[^101]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston, MA: Health
    Resources Services Administration; 2001 February 28. Report
    No.:230-99-0021.

[^102]: Kovner C, Gergen PH. Nurse staffing levels and adverse events
    following surgery in U.S. hospitals. Image J Nurs Sch
    1998;30(4):315-21.

[^103]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^104]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^105]: Miller M, Elixhauser A, Zhan C, Meyer G. Patient safety
    indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^106]: Barbour GL. Usefulness of a discharge diagnosis of sepsis in
    detecting iatrogenic infection and quality of care problems. Am J
    Med Qual 1993;8(1):2-5.

[^107]: Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L.
    In-hospital complications among survivors of admission for
    congestive heart failure, chronic obstructive pulmonary disease, or
    diabetes mellitus. J Gen Intern Med 1995;10(6):307-14.

[^108]: Best W, Khuri S, Phelan M, Hur K, Henderson W, Demakis J, et al.
    Identifying patient preoperative risk factors and postoperative
    adverse events in administrative databases: Results from the
    Department of Veterans Affairs national Surgical Quality Improvement
    Program. J Am Coll Surg 2002;194(3):257-266.

[^109]: Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K.
    Nurse Staffing and Patient Outcomes in Hospitals. Boston, MA: Health
    Resources Services Administration; 2001 February 28. Report
    No.:230-99-0021.

[^110]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^111]: Needleman et al., 2001.

[^112]: Hawker BA, Coyte PC, Wright JG, Paul JE, Bombardier C. Accuracy
    of administrative data for assessing outcomes after knee replacement
    surgery. J Clin Epidemiol 1997;50(3):265-73.

[^113]: Faciszewski T, Johnson L, Noren C, Smith MD. Administrative
    databases' complication coding in anterior spinal fusion procedures.
    What does it mean? Spine 1995;20(16):1783-8.

[^114]: Best W, Khuri S, Phelan M, Hur K, Henderson W, Demakis J, et al.
    Identifying patient preoperative risk factors and postoperative
    adverse events in administrative databases: Results from the
    Department of Veterans Affairs national Surgical Quality Improvement
    Program. J Am Coll Surg 2002;194(3):257-266.

[^115]: Hannan EL, Bernard HR, O'Donnell JF, Kilburn H, Jr. A
    methodology for targeting hospital cases for quality of care record
    reviews. Am J Public Health 1989;79(4):430-6.

[^116]: Hannan et al., 1989.

[^117]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: state and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^118]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^119]: Miller M, Elixhauser A, Zhan C, Meyer G, Patient Safety
    Indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^120]: Taylor B. Common bile duct injury during laparoscopic
    cholecystectomy in Ontario: Does ICD-9 coding indicate true
    incidence? CMAJ 1998;158(4):481-5.

[^121]: Valinsky LJ, Hockey RI, Hobbs MS, Fletcher DR, Pikora TJ,
    Parsons RW, et al. Finding bile duct injuries using record linkage:
    A validated study of complications following cholecystectomy. J Clin
    Epidemiol 1999;52(9):893-901.

[^122]: Hawker GA, Coyte PC, Wright JG, Paul JE, Bombardier C. Accuracy
    of administrative data for assessing outcomes after knee replacement
    surgery. J Clin Epidemiol 1997;50(3):265-73.

[^123]: Romano P. Can administrative data be used to ascertain
    clinically significant postoperative complications. American Journal
    of Medical Quality Press.

[^124]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^125]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^126]: Miller M, Elixhauser A, Zhan C, Meyer G, Patient Safety
    Indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^127]: Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et
    al. Identifying complications of care using administrative data. Med
    Care 1994;32(7):700-15.

[^128]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^129]: Miller M, Elixhauser A, Zhan C, Meyer G, Patient safety
    indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^130]: Hughes C, Harley E, Milmoe G, Bala R, Martorella A. Birth trauma
    in the head and neck. Arch Otolaryngol Head Neck Surg
    1999;125:193-199.

[^131]: Towner D, Castro MA, Eby-Wilkens E, Gilbert WM. Effect of mode
    of delivery in nulliparous women on neonatal intracranial injury. N
    Engl J Med 1999;341(23):1709-14.

[^132]: Miller M, Elixhauser A, Zhan C, Meyer G, Patient Safety
    Indicators: Using administrative data to identify potential patient
    safety concerns. Health Services Research 2001;36(6 Part
    II):110-132.

[^133]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^134]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.

[^135]: Johantgen M, Elixhauser A, Bali JK, Goldfarb M, Harris DR.
    Quality indicators using hospital discharge data: State and national
    applications. Jt Comm J Qual Improv 1998;24(2):88-195. Published
    erratum appears in Jt Comm J Qual Improv 1998;24(6):341.