Systems for Comparing Actual and Predicted Mortality Rates: Characteristics To Promote Cooperation in Improving Hospital Care
Authors: Harry P. Selker
Journal: Annals of Internal Medicine (1993)

Editorials15 May 1993Systems for Comparing Actual and Predicted Mortality Rates: Characteristics To Promote Cooperation in Improving Hospital CareHarry P. Selker, MD, MSPHHarry P. Selker, MD, MSPHNew England Medical Center, Tufts University School of Medicine, Boston, MA 02111.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-118-10-199305150-00010 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Mortality rates have been increasingly proposed as a measure of quality of hospital care. However, in themselves, mortality rates, whether used alone or in conjunction with severity-adjusted predicted rates, will not improve care. Only appropriate responses to mortality rates by clinicians, hospital administrators, consumers, and health care payers will improve care. For severity-adjusted mortality predictors to have their desired effect, they must have characteristics attractive to all of these groups. These characteristics are listed in this editorial.During the past decade, mortality rates have been increasingly proposed as a measure of quality of hospital care. In 1986, the federal government ...References1. U.S. Health Care Financing Administration. Medicare hospital mortality information. Washington, DC; Government Printing Office; 1987. Google Scholar2. U.S. Health Care Financing Administration. Medicare hospital mortality information. Washington, DC; Government Printing Office; 1988. Google Scholar3. Green J, Wintfeld N, Sharkey P, Passman LJ. The importance of severity of illness in assessing hospital mortality. JAMA. 1990; 263: 241-6. Google Scholar4. Berwick DM, Wald DL. Hospital leaders' opinions of the HCFA mortality data. JAMA. 1990; 263:247-9. Google Scholar5. Park RE, Brook RH, Kosecoff J, Keesey J, Rubenstein L, Keeler E, et al. Explaining variations in hospital death rates. Randomness, severity of illness, quality of care. JAMA. 1990; 264:484-90. Google Scholar6. Hadorn DC, Keiler EB, Brook RH, Rogers WB. Assessing the performance of mortality predictive models. Santa Monica, California: Rand; 1993. RAND Publication # MR-181-HCFA. Google Scholar7. Knaus WA, Wagner DP, Zimmerman JE, Draper EA. Variations in hospital mortality and length of stay from intensive care. Ann Intern Med. 1993; 118:753-761. Google Scholar8. Knaus WA, Nash DB. Predicting and evaluating patient outcomes (Editorial). Ann Intern Med. 1988; 109:521-2. Google Scholar9. Kahn KL, Brook RH, Draper D, Keeler EB, Rubenstein LV, Rogers WH, et al. Interpreting hospital mortality data. How can we proceed? JAMA. 1988; 24:3625-8. Google Scholar10. Greenfield S, Aronow HU, Elashoff RM, Watanabe D. Flaws in mortality data. The hazards of ignoring comorbid disease. JAMA. 1988; 260:2253-5. Google Scholar11. Selker HP, Griffith JL, D'Agostino RB. A time-insensitive predictive instrument for acute myocardial infarction mortality: a multicenter study. Med Care. 1991; 29:1196-211. Google Scholar12. Selker HP, Griffith JL, D'Agostino RB. A tool for judging coronary care unit admission appropriateness valid for both real-time and retrospective use. A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: a multicenter study. Med Care. 1991; 29:610-27. Google Scholar13. Iezzoni LI, Moskowitz MA. A clinical assessment of MedisGroups. JAMA. 1988; 260:3159-63. Google Scholar14. Iezzoni LI, Burnside S, Sickles L, Moskowitz MA, Sawitz E, Levine PA. Coding of acute myocardial infarction. Clinical and policy implications. Ann Intern Med. 1988; 109:745-51. Google Scholar15. Knaus WA, Draper EA, Wagner DP, et al. APACHE II: A severity of disease classification system. Crit Care Med. 1985; 13:818-29. Google Scholar16. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. An evaluation of outcome from intensive care in major medical centers. Ann Intern Med. 1986; 104:410-8. Google Scholar17. Daley J, Jencks SF, Draper D, Lenhart G, Thomas N, Walker J. Predicting hospital-associated mortality for medicare patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure. JAMA. 1988; 260:3617-24. Google Scholar18. Teres D, Lemeshow S, Avrunin SJ, Pastides H. Validation of the mortality prediction model for ICU patients. Crit Care Med. 1987; 15:208-13. Google Scholar19. Jencks SF, Daley J, Draper D, Thomas N, Lenhart G, Walker J. Interpreting hospital mortality data: the role of clinical risk adjustment. JAMA. 1988; 260:3611-6. Google Scholar20. Schroeder SA. Outcome assessment 70 years later: are we ready? (Editorial). N Engl J Med. 1987; 316:160-2. Google Scholar21. Brewster AC, Karlin BG, Hyde LA, Jacobs CM, Bradbury RC, Chae YM. MEDISGRPS: A clinically based approach to classifying hospital patients at admission. Inquiry. 1985; 12:377-87. Google Scholar22. Horn SD, Horn RA. The Computerized Severity Index. A new tool for case-mix management. J Med Syst. 1986; 10:73-78. Google Scholar23. Selker HP. Coronary care unit triage decision aids: how do we know when they work? (Editorial). Am J Med. 1989; 87:491-93. Google Scholar Author, Article, and Disclosure InformationAuthors: Harry P. Selker, MD, MSPHAffiliations: New England Medical Center, Tufts University School of Medicine, Boston, MA 02111.Corresponding Author: Harry P. Selker, MD, MSPH, Center for Cardiovascular Health Services Research, New England Medical Center, 1031, 750 Washington Street, Boston, MA 02111. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoVariations in Mortality and Length of Stay in Intensive Care Units William A. Knaus , Douglas P. Wagner , Jack E. Zimmerman , and Elizabeth A. Draper Metrics Cited byThe Predictive Approaches to Treatment effect Heterogeneity (PATH) StatementDavid M. 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