PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of hsresearchLink to Publisher's site
 
Health Serv Res. 1994 February; 28(6): 689–712.
PMCID: PMC1069976

Case-mix adjustment using objective measures of severity: the case for laboratory data.

Abstract

OBJECTIVE. We evaluate the use of routinely gathered laboratory data to subclassify surgical and nonsurgical major diagnostic categories into groups homogeneous with respect to length of stay (LOS). DATA SOURCES AND STUDY SETTING. The source of data is the Combined Patient Experience database (COPE), created by merging data from computerized sources at the University of California San Francisco (UCSF) Medical Center and Stanford University Medical Center for a total sample size of 73,117 patient admissions. STUDY DESIGN. The study is cross-sectional and retrospective. All data were extracted from COPE consecutive admissions; the unit of analysis is an admission. The outcome variable LOS proxies hospital resource utilization for an inpatient stay. Nine (candidate) predictor variables were derived from seven lab tests (WBC, Na, K, C02, BUN, ALB, HCT) by recording the whole-stay minimum or maximum test result. DATA COLLECTION/EXTRACTION METHODS. Patient groups were formed by first assigning to major diagnostic categories (MDCs) all 73,117 admissions. Each MDC was then partitioned into medical and surgical subgroups (sub-MDCs). The 13 sub-MDCs selected for study define a study population of 32,599 patients that represents approximately 45 percent of inpatients. Within each of the 13 sub-MDCs, patients were randomly assigned to one of two data sets in a ratio of 2:1. The first set was used to create, the second to validate, three different LOS predictors. Predictive accuracies of individual DRG classes were compared with those of two alternative classification schemes, one formed by recursive partitioning (the sub-MDC) using only lab test results, the other by partitioning with both lab test results and individual DRGs. PRINCIPAL FINDINGS. For the eight largest sub-MDCs (81 percent of study population), individual DRGs explained 23 percent of the within sub-MDC variance in LOS, laboratory data classes explained 31 percent, and classes derived by considering individual DRGs and laboratory data explained 37 percent. (Each result is a weighted average R2. The average number of LOS classes into which the eight largest sub-MDCs were partitioned were 20, 10, and 10, respectively. Within six of the eight, partitioning on the basis of laboratory data alone explained more within sub-MDC variance than did partitioning into individual DRGs. CONCLUSIONS. Routine lab test data improve the accuracy of LOS prediction over that possible using DRG classes. We note that the improvements do not result from overfitting the data, since the numbers of LOS classes we use to predict LOS are considerably fewer than the numbers of individual DRGs.

Full text

Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (2.1M), or click on a page image below to browse page by page. Links to PubMed are also available for Selected References.

Selected References

These references are in PubMed. This may not be the complete list of references from this article.
  • Burnum JF. The misinformation era: the fall of the medical record. Ann Intern Med. 1989 Mar 15;110(6):482–484. [PubMed]
  • 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 Winter;22(4):377–387. [PubMed]
  • Chang RW. Individual outcome prediction models for intensive care units. Lancet. 1989 Jul 15;2(8655):143–146. [PubMed]
  • Daley J, Jencks S, Draper D, Lenhart G, Thomas N, Walker J. Predicting hospital-associated mortality for Medicare patients. A method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure. JAMA. 1988 Dec 23;260(24):3617–3624. [PubMed]
  • Daniels M, Schroeder SA. Variation among physicians in use of laboratory tests. II. Relation to clinical productivity and outcomes of care. Med Care. 1977 Jun;15(6):482–487. [PubMed]
  • Fitzgerald JF, Moore PS, Dittus RS. The care of elderly patients with hip fracture. Changes since implementation of the prospective payment system. N Engl J Med. 1988 Nov 24;319(21):1392–1397. [PubMed]
  • Ginsburg PB, Carter GM. Medicare case-mix index increase. Health Care Financ Rev. 1986 Summer;7(4):51–65. [PubMed]
  • Ginzberg E. A hard look at cost containment. N Engl J Med. 1987 Apr 30;316(18):1151–1154. [PubMed]
  • Goldman ES, Easterling MJ, Sheiner LB. Improving the homogeneity of diagnosis-related groups (DRGs) by using clinical laboratory, demographic, and discharge data. Am J Public Health. 1989 Apr;79(4):441–444. [PubMed]
  • Gonnella JS, Hornbrook MC, Louis DZ. Staging of disease. A case-mix measurement. JAMA. 1984 Feb 3;251(5):637–644. [PubMed]
  • Horn SD, Horn RA, Sharkey PD, Chambers AF. Severity of illness within DRGs. Homogeneity study. Med Care. 1986 Mar;24(3):225–235. [PubMed]
  • Horn SD, Horn RA, Sharkey PD. The Severity of Illness Index as a severity adjustment to diagnosis-related groups. Health Care Financ Rev. 1984;Suppl:33–45. [PubMed]
  • Horn SD, Horn RA. Reliability and validity of the Severity of Illness Index. Med Care. 1986 Feb;24(2):159–178. [PubMed]
  • Horn SD, Horn RA. The Computerized Severity Index. A new tool for case-mix management. J Med Syst. 1986 Feb;10(1):73–78. [PubMed]
  • Horn SD, Sharkey PD, Bertram DA. Measuring severity of illness: homogeneous case mix groups. Med Care. 1983 Jan;21(1):14–30. [PubMed]
  • Hornbrook MC. Hospital case mix: its definition, measurement and use: Part I. The conceptual framework. Med Care Rev. 1982 Spring;39(1):1–43. [PubMed]
  • Easman CF, Ison CA, Woodford N. Spectinomycin and resistant Neisseria gonorrhoeae. N Engl J Med. 1988 Feb 4;318(5):325–326. [PubMed]
  • Iezzoni LI, Moskowitz MA. A clinical assessment of MedisGroups. JAMA. 1988 Dec 2;260(21):3159–3163. [PubMed]
  • Iglehart JK. Health policy report: the new era of prospective payment for hospitals. N Engl J Med. 1982 Nov 11;307(20):1288–1292. [PubMed]
  • Jencks SF, Dobson A. Refining case-mix adjustment. The research evidence. N Engl J Med. 1987 Sep 10;317(11):679–686. [PubMed]
  • Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct;13(10):818–829. [PubMed]
  • Linn BS, Robinson DS. The possible impact of DRGs on nutritional status of patients having surgery for cancer of the head and neck. JAMA. 1988 Jul 22;260(4):514–518. [PubMed]
  • Lloyd SS, Rissing JP. Physician and coding errors in patient records. JAMA. 1985 Sep 13;254(10):1330–1336. [PubMed]
  • Phillips A, Shaper AG, Whincup PH. Association between serum albumin and mortality from cardiovascular disease, cancer, and other causes. Lancet. 1989 Dec 16;2(8677):1434–1436. [PubMed]
  • Russell LB, Manning CL. The effect of prospective payment on Medicare expenditures. N Engl J Med. 1989 Feb 16;320(7):439–444. [PubMed]
  • Schumacher DN, Parker B, Kofie V, Munns JM. Severity of Illness Index and the Adverse Patient Occurrence Index. A reliability study and policy implications. Med Care. 1987 Aug;25(8):695–704. [PubMed]
  • Schwab CW, Young G, Civil I, Ross SE, Talucci R, Rosenberg L, Shaikh K, O'Malley K, Camishion RC. DRG reimbursement for trauma: the demise of the trauma center (the use of ISS grouping as an early predictor of total hospital cost). J Trauma. 1988 Jul;28(7):939–946. [PubMed]
  • Simborg DW. DRG creep: a new hospital-acquired disease. N Engl J Med. 1981 Jun 25;304(26):1602–1604. [PubMed]
  • Vladeck BC, Kramer PS. Case mix measures: DRGs and alternatives. Annu Rev Public Health. 1988;9:333–359. [PubMed]
  • Young WW, Swinkola RB, Zorn DM. The measurement of hospital case mix. Med Care. 1982 May;20(5):501–512. [PubMed]

Articles from Health Services Research are provided here courtesy of Health Research & Educational Trust