Relevance of Outlier Cases in Case Mix Systems and Evaluation of Trimming Methods

Overview

Journal Health Care Manag Sci

Specialty Health Services

Date 2003 Mar 18

PMID 12638924

Citations 13

Authors

Francesc Cots

David Elvira

Xavier Castells

Marc Saez

Affiliations

Soon will be listed here.

Abstract

Objectives: To determine the most appropriate outlier trimming method when the main source of information for case mix classification is length of stay (LOS) because cost information is unavailable.

Methods: Discharges (35,262) from two public hospitals were analysed. LOS and cost outliers were calculated using different trimming methods. The agreement between cost and LOS trimming was analysed.

Results: The trimming method using the geometric mean with two standard deviations (GM2) showed the highest level of agreement between cost and LOS and revealed the greatest proportion of extreme costs. Nearly 5% of cases were outliers, containing 16% of total LOS. This was the best approximation to 18% of extreme cost because when GM2 was applied to LOS, 88% of outlier cost was revealed.

Conclusions: The methods were analysed because they are the most frequently used but the same methodology could be employed to compare other outlier determination methods. Outliers should be calculated because they ought to be valued differently from inlier cases.

Citing Articles

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Variation in batch ordering of imaging tests in the emergency department and the impact on care delivery.

Jameson J, Saghafian S, Huckman R, Hodgson N Health Serv Res. 2024; 60(1):e14406.

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Cyganska M, Kludacz-Alessandri M, Pyke C Int J Environ Res Public Health. 2023; 20(2).

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Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.

Xu Z, Zhao C, Scales Jr C, Henao R, Goldstein B BMC Med Inform Decis Mak. 2022; 22(1):110.

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References

Soderlund N . Product definition for healthcare contracting: an overview of approaches to measuring hospital output with reference to the UK internal market. J Epidemiol Community Health. 1994; 48(3):224-31. PMC: 1059951. DOI: 10.1136/jech.48.3.224. View

Soderlund N, Gray A, Milne R, Raftery J . Case mix measurement in English hospitals: an evaluation of five methods for predicting resource use. J Health Serv Res Policy. 1995; 1(1):10-9. DOI: 10.1177/135581969600100104. View

Cots Reguant F, Castells Oliveres X, Garcia Altes A, Saez Zafra M . [Relation of direct hospitalization costs with length of stay]. Gac Sanit. 1998; 11(6):287-95. DOI: 10.1016/s0213-9111(97)71309-8. View

Calore K, Iezzoni L . Disease staging and PMCs. Can they improve DRGs?. Med Care. 1987; 25(8):724-37. DOI: 10.1097/00005650-198708000-00006. View

Iezzoni L, Ash A, Shwartz M, Daley J, HUGHES J, Mackiernan Y . Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method. Am J Public Health. 1996; 86(10):1379-87. PMC: 1380647. DOI: 10.2105/ajph.86.10.1379. View

Briggs A, Gray A . The distribution of health care costs and their statistical analysis for economic evaluation. J Health Serv Res Policy. 1998; 3(4):233-45. DOI: 10.1177/135581969800300410. View

Young D, Pearlman L . Managing the stages of hospital cost accounting. Healthc Financ Manage. 1993; 47(4):58, 60, 63-4 passim. View

Butler J, Furnival C, Hart R . Estimating treatment cost functions for progressive diseases: a multiproduct approach with an application to breast cancer. J Health Econ. 1995; 14(3):361-85. DOI: 10.1016/0167-6296(95)00007-5. View

Thomas J, Ashcraft M . Measuring severity of illness: six severity systems and their ability to explain cost variations. Inquiry. 1991; 28(1):39-55. View

10.

Carter G, Jacobson P, Kominski G, Perry M . Use of diagnosis-related groups by non-Medicare payers. Health Care Financ Rev. 1994; 16(2):127-58. PMC: 4193495. View

11.

Udpa S . Activity-based costing for hospitals. Health Care Manage Rev. 1996; 21(3):83-96. View

12.

Elixhauser A, Steiner C, Harris D, Coffey R . Comorbidity measures for use with administrative data. Med Care. 1998; 36(1):8-27. DOI: 10.1097/00005650-199801000-00004. View

13.

Beaver C, Zhao Y, McDermid S, Hindle D . Casemix-based funding of Northern Territory public hospitals: adjusting for severity and socio-economic variations. Health Econ. 1998; 7(1):53-61. DOI: 10.1002/(sici)1099-1050(199802)7:1<53::aid-hec308>3.0.co;2-v. View

14.

Sanderson H, Anthony P, Mountney L . Healthcare resource groups--version 2. J Public Health Med. 1995; 17(3):349-54. View

15.

Cots F, Elvira D, Castells X, Dalmau E . Medicare's DRG-weights in a European environment: the Spanish experience. Health Policy. 2000; 51(1):31-47. DOI: 10.1016/s0168-8510(99)00074-3. View

16.

Keeler E, Carter G, Trude S . Insurance aspects of DRG outlier payments. J Health Econ. 1988; 7(3):193-214. DOI: 10.1016/0167-6296(88)90025-2. View

17.

Cots F, Castells X . [Public hospital payment system in Spain: the experience of Catalonia and Andalusia]. Gac Sanit. 2001; 15(2):172-8. DOI: 10.1016/s0213-9111(01)71538-5. View

18.

Iezzoni L, Ash A, Shwartz M, Landon B, Mackiernan Y . Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions?. Med Care. 1998; 36(1):28-39. DOI: 10.1097/00005650-199801000-00005. View