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Description of Case-Mix Adjusters by the Severity of Illness Working Group of The Society of Hospital Epidemiologists of America (SHEA)

Published online by Cambridge University Press:  02 January 2015

Peter A. Gross ,
B. Eugene Beyt Jr. ,
Michael D. Decker ,
Richard A. Garibaldi ,
Walter J. Hierholzer Jr. ,
William R. Jarvis ,
Elaine Larson ,
Bryan Simmons ,
William E. Scheckler  and
Lorraine M. Harkavy
Peter A. Gross*
Affiliation:
Hackensack Medical Center, Hackensack, New Jersey and New Jersey Medical School, Newark, New Jersey
B. Eugene Beyt Jr.
Affiliation:
Louisiana State University School of Medicine, New Orleans, LA
Michael D. Decker
Affiliation:
Vanderbilt University School of Medicine, Nashville, Tennessee
Richard A. Garibaldi
Affiliation:
University of Connecticut Health Center, Farmington, Connecticut
Walter J. Hierholzer Jr.
Affiliation:
Yale-New Haven Hospital and Yale University School of Medicine, New Haven, Connecticut
William R. Jarvis
Affiliation:
Hospital Infections Program, Centers for Disease Control, Atlanta, Georgia
Elaine Larson
Affiliation:
The Johns Hopkins University School of Nursing, Baltimore, Maryland
Bryan Simmons
Affiliation:
Methodist Hospitals and University of Tennessee School of Medicine, Memphis, Tennessee
William E. Scheckler
Affiliation:
St. Marys Hospital and University of Wisconsin Medical School, Madison, Wisconsin
Lorraine M. Harkavy
Affiliation:
Association for Practitioners in Infection Control, Potomac, Maryland
*
Department of Internal Medicine, Hackensack Medical Center, 30 Prospect Avenue, Hackensack, NJ 07601
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Abstract

Hospitals, insurance companies, and federal and state governments are increasingly concerned about reducing patient cost expenditures while maintaining high quality patient care. One method of reducing expenditures has been to tie hospital reimbursement with a prospective payment system based on diagnosis-related groups (DRGs). However, reimbursement under the DRG system is not acceptable for all patients in all hospitals because it is neither an accurate predictor of costs nor of clinical outcome. This deficiency poses significant problems for hospitals because DRGs are used nationwide as the prospective payment system for inpatients covered by Medicare. Several case-mix adjusters have been proposed to modify DRGs to improve their accuracy in predicting costs and outcome. We reviewed five of the most widely available indices: Acute Physiologic and Chronic Health Evaluation (APACHE II), Coded Disease Staging, Computerized Severity Index (CSI), Medical Illness Severity Group System (MEDISGROUPS), and Patient Management Categories (PMC). Recommendations for the use of a single case-mix adjuster cannot be made at this time because all indices have not been compared in sufficiently diverse settings and because some are better predictors of costs while others are better predictors of clinical outcome. Hospital epidemiologists and other infection control practitioners should be informed about these indices and their potential applications as they expand their role beyond infection control problems to issues concerning cost containment, quality assurance, and reimbursement.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 9 , Issue 7 , July 1988 , pp. 309 - 316
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1988

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References

1.Jenks, SF, Dobson, A, Willis, P, et al: Evaluating and improving the measurement of hospital case mix. Health Care Financing Rev 1984;6(suppl):1–11.Google Scholar
2.Jenks, SF, Dobson, A: Refining case-mix adjustment: The research evidence. N Engl J Med 1987;317:679–686.Google Scholar
3.Hornbrook, MC: Hospital case mix: Its definition, measurement, and use: Part II, a review of alternative measures. Med Care Rev 1982;39:75–123.Google Scholar
4.Gertman, PM, Lowenstein, S: A research paradigm for severity of illness: Issues for the diagnosis-related group system. Health Care Financing Rev 1984;6(suppl):79–90.Google Scholar
5.Horn, SD, Bulkley, G, Sharkey, PD, et al: Interhospital differences in severity of illness. N Engl J Med 1985;313:20–24.CrossRefGoogle ScholarPubMed
6.Kominski, GF, Williams, SV, Mays, RB, et al: Unrecognized redistributions of revenue in diagnosis-related group-based prospective payment svstems. Health Care Financing Rev 1984;6(suppl):57–69.Google Scholar
7.Mullin, RL: Diagnosis-related groups and severity. 1CD-9-CM. the real problem. JAMA 1985;254:1208–1210.Google Scholar
8.Smits, HL, Fetter, RB, McMahon, LF: Variation in resource use within diagnosis-related groups: The severity issue. Health Care Financing Rev 1984;6(suppl):71–78.Google Scholar
9.Shakno, RJ (ed): Physician's Guide to DRGs. Chicago. Pluribus Press, Inc. 1984. p232.Google Scholar
10.Horn, SD, Horn, RA, Sharkev, PD, et al: Misclassifitation problems in diagnosis-related groups: Cystic fibrosis as an example. N Engl J Med 1986;314:484–487.Google Scholar
11.Counts, GW: The relationship between APIC and SHEA: “Closely watched trains.” editorial. Infect Control 1987;8:404–406.Google Scholar
12.Thomas, JW, Ashcralt, MLF, Zimmerman, J: An evaluation ol alternative severity of illness measures for use by university hospitals, in Vol I: Management Summary. Ann Arbor, Dept. of Health Services Management and Policv, School of Public Health, The University of Michigan. 1986, pp 1–13.Google Scholar
13.Wagner, DP, Knaus, WA, Draper, EA: Statistical validation of a severity of illness measure. Am J Public Health 1983;73:878–884.CrossRefGoogle ScholarPubMed
14.Wagner, DP, Draper, EA: Acute physiology and chronic health evaluation (APACHE II) and Medicare reimbursement. Health Care Financing Rev 1984;6(suppl):91–105.Google Scholar
15.Knaus, WA, Draper, EA, Wagner, DP, et al: An evaluation of outcome from intensive care in major medical centers. Ann Intern Med 1986;104:410–118.Google Scholar
16.Knaus, WA, Zimmerman, JE, Wagner, , et al: APACHE—Acute physiology and chronic health evaluation: A physiologically based classification system. Crit Care Med 1981;9:591–597.Google Scholar
17.Knaus, WA, Draper, EA, Wagner, DP, et al: APACHE II: A severity of disease classification system. Crit Care Med 1985;13:818–829.Google Scholar
18.Cof fee, KM, Goldfarb, MKG: DRGs and disease staging tot- reimbursing medicare patients. Hospital Studies Program. Working Paper No. 1. US Department of Health and Human Services. 1984, p 32.Google Scholar
19.Conklin, JE, Lieberman, JV, Barnes, CA, et al: Disease staging: Implications for hospital reimbursement and management. Health Care Financing Rev 1984;6(suppl):13–22.Google Scholar
20.Ament, RP, Dreachslin, JL, Kobrinski, EJ, et al: Three case-type classifications: Suitability for use in reimbursing hospitals. Med Care 1982;5:460–467.Google Scholar
21.Gonnella, JS, Hornbrook, MG, Louis, DZ: Staging of disease. A case-mix measurement. JAMA 1984;251:637–646.CrossRefGoogle ScholarPubMed
22.Gonnella, JS (ed): Disease Staging Ctmiral Criteria, ed 3. Santa Barbara, California. SysteMetrics, McGraw-Hill, 1986. p 624.Google Scholar
23.Horn, SD, Sharkey, PD, Chambers, AF, et al: Severity of illness within DRGs: Impact on prospective payment. Am J Public Health 1985;75:1195–1199.CrossRefGoogle ScholarPubMed
24.Horn, SD, Horn, RA: The computerized severity index: A new tool for case-mix management. J Med Syst 1986; 10:73–78.Google Scholar
25.Mullet, C: Paving hospitals: How does a severity measure help? Am J Public Health 1983;73:14–15.Google Scholar
26.Horn, SD, Sharkey, PD, Bertram, DA: Measuring severity of illness: Homogenous case mix groups. Med Care 1983;21:14–25.CrossRefGoogle ScholarPubMed
27.Bitwster, AC, Jacobs, CM, Bradbury, RC: Classifying severity of illness by using clinical findings. Health Care Financing Rev 1984;6(suppl):107–108.Google Scholar
28.Brewster, AC, Karlin, BG, Hyde, LA, et al: MEDISGROUPS: A clinically based approach to classifying hospital patients at admission. Inquiry 1985;12:377–387.Google Scholar
29.Lezzoni, LI, Ash, AS, Moskowitz, MA: MEDISGROUPS: A clinical and analytic assessment. Research report. Section of General Internal Medicine, The University Hospital, Boston University School of Medicine. 1987, p 203.Google Scholar
30.Young, WA: Incorporating severity of illness and comorbidity in case-mix measurement. Health Care Financing Rev 1984:6(suppl):23–31.Google Scholar
31.Blue Cross of Western Pennsylvania: Patient Management Categories Final Report. Pittsburgh, The Pittsburgh Research Institute. 1985, p 51.Google Scholar
32.Yeh, TS, Pollack, MM, Holbrook, PR, et al: Assessment of pediatric intensive care: Application of the therapeutic intervention scoring system. Crit Care Med 1982;10:497–500.CrossRefGoogle ScholarPubMed
33.Pollack, MM, Ruttimann, UE, Getson, PR, et al: Accurate prediction of the outcome of pediatric intensive care. A new quantitative method. N Engl J Med 1987;316:134–162.CrossRefGoogle ScholarPubMed
34.Yeh, TS, Pollack, MM, Ruttimann, UE, et al: Validation of a physiologic stability index for use in critically ill infants and children. Pediatr Res 1984:18:445–451.Google Scholar
35.Snyder, JV, McGuirk, M, Grenvik, A, et al: Outcome of intensive care. An application of a predictive model. Crit Care Med 1981;9:598–603.Google Scholar
36.Trunkey, DD. Panel: Current status of trauma severity indices. J Trauma 1983:23:185–201.Google Scholar
37.Morris, J, Auerbach, PS, Marshall, GA, et al: The trauma score as a triage tool in the prehospital setting. JAMA 1986:256:1319–1325.Google Scholar
38.Clemmer, TP, Orme, JF, Thomas, F, et al: Prospective evaluation of the CRAMS scale for tracing major trauma. J Trauma 1985;25:188–191.Google Scholar
39.Baker, SP, O'Neill, B, Haddon, W, et al: The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974;14:187–196.CrossRefGoogle ScholarPubMed
40.Goldberg, JL, Goldberg, J, Levy, PS, et al: Measuring the severity of injury: The validity of the revised estimated survival probability index. J Trauma 1984;24:420–427.CrossRefGoogle ScholarPubMed
41.Thompson, JO: The measurement of nursing intensity. Health Care Financing Rev 1984;6(suppl):47–55.Google Scholar
42.Wennberg, JE, Roos, N, Sola, L, et al: Use of claims data systems to evaluate health care outcomes. Mortality and reoperation following prostatectomy. JAMA 1987;257:933–936.Google Scholar
43.McGowan, JE Jr: Infection control and hospital epidemiology: Five changes forced by prospective reimbursement. J Nosoccmial Infect 1987;4:8-10,21–23.Google Scholar