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Apr-Drgs: a Severity Adjustment Methodology

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Apr-Drgs: a Severity Adjustment Methodology
Running head: APR-DRGs: A SEVERITY ADJUSTMENT METHODOLOGY

APR-DRGs:
A Severity Adjustment Methodology Quality Management In Healthcare

Abstract All Patient Refined Diagnostic Related Groups (APR-DRGs) currently represent one of the most widely used systems for severity adjustment of hospital outcome comparisons. Severity Adjustment is the method used to account for differences in patient characteristics such as age, severity of illness, and risk of mortality independent of the actual medical treatment given. As a result, it’s possible to make better comparisons of hospital data and allow facilities to substantiate the notion that “my patients are sicker.” Data sources for severity adjustment currently can be clinical (e.g., a medical record) or administrative (e.g., discharge abstracts). APR-DRGs are a refinement of Medicare’s DRG system and incorporate severity of illness and risk of mortality measures. Studies show APR-DRGs perform about as well, and in some cases better, than competing severity adjustment systems based on administrative data. Overall, their widespread usage can be explained by their low cost, flexibility, and methodology.

APR-DRGs: A Severity Adjustment Methodology
Introduction
All Patient Refined Diagnostic Related Groups (APR-DRGs) are a patient classification system developed by 3M Health Information Systems. They are a refinement of Medicare’s current DRG system to include severity of illness and risk of mortality measures. In APR-DRGs, severity of illness is defined as the extent of organ system loss of function or physiologic decompensation, while risk of mortality is the likelihood of dying (Averill, 2002). According to the Centers for Medicare and Medicaid Services (CMS), APR-DRGs are one of the most widely used severity adjustment methodologies for comparative hospital performance (Medicare Program, 2006). Therefore, the purpose of this paper is to define what severity adjustment is, why it’s important, and the data



References: Averill, R. F., Goldfield, N. I., Muldoon, J., Steinbeck, B. A., & Grant, T. M. (2002). A closer look at all-patient refined drgs. Journal of AHIMA, 73 (1), 46-50. Boucher, A., Bowman, S., Piselli, C., & Scichilone, R. (2006). The evolution of drgs. Journal of AHIMA, 77 (7), 68A-C. Bowman, S. (2006). New drg system for ipps: cms proposes severity-adjustment drg system based on apr-drgs. Journal of AHIMA, 77 (7), 18,20. Hall, D., & Siegal, B. (2005). Taking the measure of measures: quality data initiatives and the challenge of effective and efficient data. Journal of AHIMA, 76 (2), 42-46. Iezzoni, L. I., et al. (1995). Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes. Annals of Internal medicine, 123 (10), 763-770. Iezzoni, L. I., et al. (1996). Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method. American Journal of Public Health, 86 (10), 1379-1387. Iezzoni, L. I., (1997). The risk of risk adjustment. Journal of the American Medical Association, 278 (19), 1600-1607. Maurer, W. G., & Hebert, C. J., (2006). Cleveland Clinic Journal of Medicine, 73, S30-S35. Medicare Program: Changes to Hospital Inpatient Prospective Payment Systems and Fiscal Year 2007 Rates, 71 Fed. Reg. 47898 (2006) (to be codified at 42 C.F.R § 409, 410, 412, et al.) McGlynn, E 3M Health Information Systems. (2003). All Patient Refined Diagnosis Related Groups (APR-DRGs); Methodology Overview. Wallingford, CT: Author.

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