area’s performance on the indicator, holding patient covariates constant. In most of the empirical Hannan EL, Kilburn H, Jr., Bernard H, et al.
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AHRQ Quality Indicators Guide to Prevention Quality Indicators :Hospital Admission for Ambulatory Care Sensitive ConditionsDepartment of Health and Human Services Agency for Healthcare Research and Quality www.ahrq.gov October 2001 AHRQ Pub. No. 02-R0203 Revision 1 (April 17, 2002)
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iThe programs for the Prevention Quality Indicators (PQIs) can be downloaded from http://www.ahr q.gov/. Instructions on how to use the programs to calculate the PQI rates are contained in the companion text, Prevention Quality Indicators: Software Documentation. PrefaceIn 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, 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 a multidimensional, accessible family of 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 either 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. During 2001 and 2002, AHRQ will be publishing the three modules as a series. Full technical information on the first two modules can be found in Evidence Report for Refinement of the HCUP Quality Indicators , prepared by the UCSF-Stanford EPC. It can be accessed at AHRQ™s Web site at This first module focuses on preventive care servicesŠoutpatient services geared to staying healthy and living with illness. Researchers and policy makers have agreed for some time that inpatient data offer a useful window on the quality of preventive care in the community. Inpatient data provide information on admissions for ﬁambulatory care sensitive conditionsﬂ that evidence suggests could have been avoided, at least in part, through better outpatient care. Hospitals, community leaders, and policy makers can then use such data to identify community need levels, target resources, and track the impact of programmatic and policy interventions. One of the most important ways we can improve the quality of health care in America is to reduce the need for some of that care by providing appropriate, high-quality preventive services. For this to happen, however, we need to be able to track not only the level of outpatient services but also the outcome of the services people do or do not receive. 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. Irene Fraser, Ph.D., Director Center for Organization and Delivery Studies
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iiAcknowledgments 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 new Quality Indicators: Core Project Team Mark McClellan, M.D., Ph.D., principal investigator Jeffrey Geppert, J.D. Kathryn M. McDonald, M.M., EPC coordinator Patrick Romano, M.D., M.P.H. Sheryl M. Davies, M.A. Kaveh G. Shojania, M.D. Other Contributors Amber Barnato, M.D.Kristine McCoy, M.P.H. Paul Collins, B.A.Suzanne Olson, M.A. Bradford Duncan M.D.L. LaShawndra Pace, B.A. Michael Gould, M.D., M.S.Mark Schleinitz, M.D. Paul Heidenreich, M.D.Herb Szeto, M.D. Corinna Haberland, M.D.Carol Vorhaus, M.B.A Paul Matz, M.D.Peter Weiss, M.D. Courtney Maclean, B.A.Meghan Wheat, B.A. Susana Martins, M.D. Consultants Douglas Staiger, Ph.D. The following staff from Social & Scientific Systems , Inc. , developed this software product, documentation, and guide: ProgrammersTechnical Writer Leif KarellPatricia Caldwell Kathy McMillan Graphics Designer Laura SpoffordContributors from the Agency for Healthcare Research and Quality :Anne Elixhauser, Ph.D. H. Joanna Jiang, Ph.D. Margaret Coopey, R.N., M.G.A, M.P.S. We also wish to acknowledge the contribution of the peer reviewers of the evidence report and the beta-testers of the software products, whose input was invaluable.
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iiiTable of Contents Prefacei Acknowledgments ii Introduction to the AHRQ Prevention Quality Indicators .1 What Are the Prevention Quality Indicators? .1 How Can the PQIs be Used in Quality Assessment? 2 What does this Guide Contain? ..3 Origins and Background of the Quality Indicators 4 Development of the AHRQ Quality Indicators 4 AHRQ Quality Indicator Modules .5 Methods of Identifying, Selecting, and Evaluating the Quality Indicators ..6 Summary Evidence on the Prevention Quality Indicators ..12 Strengths and Limitations in Using the PQIs 16 Questions for Future Work ..17 Detailed Evidence for Prevention Quality Indicators .19 Bacterial Pneumonia Admission Rate ..21 Dehydration Admission Rate 23 Pediatric Gastroenteritis Admission Rate 25 Urinary Tract Infection Admission Rate .27 Perforated Appendix Admission Rate ..29 Low Birth Weight Rate ..31 Angina without Procedure Admission Rate .33 Congestive Heart Failure Admission Rate ..35 Hypertension Admission Rate ..37 Adult Asthma Admission Rate ..39 Pediatric Asthma Admission Rate ..41 Chronic Obstructive Pulmonary Disease Admission Rate ..43 Uncontrolled Diabetes Admission Rate .45 Diabetes Short-Term Complications Admission Rate ..47 Diabetes Long-Term Complications Admission Rate ..49 Rate of Lower-Extremity Amputation among Patients with Diabetes ..51 References ..53 Appendix A: Prevention Quality Indicator Definitions Appendix B: Detailed Methods
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Individual hospitals that are sole providers for communities and that are involved in outpatient 1care may be able to use the PQI programs. Managed care organizations and health care providers with responsibility for a specified enrolled population can use the PQI programs but must provide their own population denominator data. 2Although other factors outside the direct control of the health care system, such as poor environmental conditions or lack of patient adherence to treatment recommendations, can result in hospitalization, the PQIs provide a good starting point for assessing quality of health services in the community. Because the PQIs are calculated using readily available hospital administrative data, they are an easy-to-use and inexpensive screening tool. They can be used to provide a window into the communityŠto identify unmet community heath care needs, to monitor how well complications from a number of common conditions are being avoided in the outpatient setting, and to compare performance of local health care systems across communities. How Can the PQIs be Used in Quality Assessment? While these indicators use hospital inpatient data, their focus is on outpatient health care. Except in the case of patients who are readmitted soon after discharge from a hospital, the quality of inpatient care is unlikely to be a significant determinant of admission rates for ambulatory care sensitive conditions. Rather, the PQIs assess the quality of the health care system as a whole, and especially the quality of ambulatory care, in preventing medical complications. As a result, these measures are likely to be of the greatest value when calculated at the population level and when used by public health groups, State data organizations, and other organizations concerned with the health of populations. 1These indicators serve as a screening tool rather than as definitive measures of quality problems. They can provide initial information about potential problems in the community that may require further, more in-depth analysis. Policy makers and health care providers can use the PQIs to answer questions such as: 3How does the low birth weight rate in my State compare with the national average? 3What can the pediatric indicators in the PQIs tell me about the adequacy of pediatric primary care in my community? 3Does the admission rate for diabetes complications in my community suggest a problem in the provision of appropriate outpatient care to this population? 3How does the admission rate for congestive heart failure vary over time and from one region of the country to another? State policy makers and local community organizations can use the PQIs to assess and improve community health care. For example, an official at a State health department wants to gain a better understanding of the quality of care provided to people with diabetes in her State. She selects the four PQIs related to diabetes and applies the statistical programs downloaded from the AHRQ Web site to hospital discharge abstract data collected by her State. Based on output from the programs, she examines the age- and sex-adjusted admission rates for these diabetes PQIs for her State as a whole and for communities within her State. The programs provide output that she uses to compare different population subgroups, defined by age, ethnicity, or gender. She finds that admission rates for short-term diabetes
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HCUPnet can be found at and provides instant access to 2national and regional data from the Healthcare Cost and Utilization Project, a Federal-State-industry partnership in health data maintained by the Agency for Healthcare Research and Quality. 3complications and uncontrolled diabetes are especially high in a major city in her State and that there are differences by race/ethnicity. She also applies the PQI programs to multiple years of her State™s data to track trends in hospital admissions over time. She discovers that the trends for these two PQIs are increasing in this city but are stable in the rest of the State. She then compares the figures from her State to national and regional averages on these PQIs using HCUPnetŠan online query system providing access to statistics based on HCUP data. The 2State average is slightly higher than the regional and national averages, but the averages for this city are substantially higher. After she has identified disparities in admission rates in this community and in specific patient groups, she further investigates the underlying reasons for those disparities. She attempts to obtain information on the prevalence of diabetes across the State to determine if prevalence is higher in this city than in other communities. Finding no differences, she consults with the State medical association to begin work with local providers to discern if quality of care problems underlie these disparities. She contacts hospitals and physicians in this community to determine if community outreach programs can be implemented to encourage patients with diabetes to seek care and to educate them on lifestyle modifications and diabetes self- management. She then helps to develop specific interventions to improve care for people with diabetes and reduce preventable complications and resulting hospitalizations. What does this Guide Contain? This guide provides background information on the PQIs. 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 Quality Indicators. Third, the guide summarizes the PQIs specifically, describes strengths and limitations of the indicators, documents the evidence that links the PQIs to the quality of outpatient health care services, and then provides in-depth two-page descriptions of each PQI. Finally, two appendices present additional technical background information. The first appendix outlines the specific definitions of each PQI, with complete ICD-9-CM coding specifications. The second appendix provides the details of the empirical methods used to explore the PQIs.
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Ball JK, Elixhauser A, Johantgen M, et al. HCUP Quality Indicators, Methods, Version 1.1: 3Outcome, 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. Impact: Case Studies Notebook Œ Documented Impact and Use of AHRQ’s Research .4Compiled by Division of Public Affairs, Office of Health Care Information, Agency for Healthcare Research and Quality. 4Origins 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 dataŠdiagnoses and 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 sourceŠadministrative data based on hospital claimsŠand quality measures that had been reported elsewhere. The 33 HCUP QIs included 3measures 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. For example, one 4hospital that detected high rates of admissions for diabetes complications investigated the underlying reasons for the rates and established a center of excellence to strengthen outpatient services for patients with diabetes. 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
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5discharge 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 will be distributed as three separate modules: 3Prevention Quality Indicators . These indicators consist of ﬁambulatory care sensitive conditions,ﬂ 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. 3Inpatient 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. 3Patient Safety Indicators . These indicators also reflect quality of care inside hospitals, but focus on surgical complications and other iatrogenic events.
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