Literature DB >> 2494894

Improving the homogeneity of diagnosis-related groups (DRGs) by using clinical laboratory, demographic, and discharge data.

E S Goldman1, M J Easterling, L B Sheiner.   

Abstract

For 48 of the most common diagnosis-related groups (DRGs) at our hospital, we examined the ability of clinical laboratory tests, demographic data, and ICD-9-CM codes, which provide a measure of severity of illness, to predict patients' length of stay (LOS) more accurately than DRGs alone. For 10 of 20 medical DRGs and 13 of 23 surgical DRGs examined, we were able to increase the ability to predict LOS by at least 10 per cent. The laboratory tests that proved most predictive of LOS over all DRGs were the mean serum sodium, potassium, bicarbonate, and albumin. The system is data driven, objective, and flexible, thus ensuring its utility for the purpose of equitable reimbursement.

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Year:  1989        PMID: 2494894      PMCID: PMC1349971          DOI: 10.2105/ajph.79.4.441

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  8 in total

Review 1.  Hospital case mix: its definition, measurement and use. Part II: Review of alternative measures.

Authors:  M C Hornbrook
Journal:  Med Care Rev       Date:  1982

2.  The computerized severity index. A new sophisticated tool to measure hospital quality of care.

Authors:  J Backofen; M A Ashworth; S D Horn
Journal:  Healthc Forum       Date:  1987 Mar-Apr

3.  Severity of illness within DRGs. Homogeneity study.

Authors:  S D Horn; R A Horn; P D Sharkey; A F Chambers
Journal:  Med Care       Date:  1986-03       Impact factor: 2.983

4.  Measuring severity of illness: homogeneous case mix groups.

Authors:  S D Horn; P D Sharkey; D A Bertram
Journal:  Med Care       Date:  1983-01       Impact factor: 2.983

5.  The sensitivity and specificity of nutrition-related variables in relationship to the duration of hospital stay and the rate of complications.

Authors:  C F Anderson; K Moxness; J Meister; M F Burritt
Journal:  Mayo Clin Proc       Date:  1984-07       Impact factor: 7.616

6.  Nutritional assessment: a comparison of clinical judgement and objective measurements.

Authors:  J P Baker; A S Detsky; D E Wesson; S L Wolman; S Stewart; J Whitewell; B Langer; K N Jeejeebhoy
Journal:  N Engl J Med       Date:  1982-04-22       Impact factor: 91.245

7.  Will payment based on diagnosis-related groups control hospital costs?

Authors:  J E Wennberg; K McPherson; P Caper
Journal:  N Engl J Med       Date:  1984-08-02       Impact factor: 91.245

8.  Measuring severity of illness: comparisons across institutions.

Authors:  S D Horn
Journal:  Am J Public Health       Date:  1983-01       Impact factor: 9.308

  8 in total
  1 in total

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

Authors:  B Mozes; M J Easterling; L B Sheiner; K L Melmon; R Kline; E S Goldman; A N Brown
Journal:  Health Serv Res       Date:  1994-02       Impact factor: 3.402

  1 in total

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