1 / 30

Understanding Risk Adjustment in Health Services Research

Learn about different confounding adjustment methods in Health Services Research, including proprietary and non-proprietary tools like DRGs, ACGs, comorbidity indices. Explore the value of standard risk adjustment tools. Background reading references included.

paige
Télécharger la présentation

Understanding Risk Adjustment in Health Services Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EPI235: Epi Methods in HSR March 31, 2005 L2 Evaluating Health Services using administrative data 1: Introduction to Risk Adjustment (Dr. Schneeweiss) This lecture gives an overview of the various approaches of adjusting for confounding typical to Health Services Research. The purpose and mechanics of proprietary and non-proprietary risk-adjustment tools for clinical and administrative data, including DRGs, ACGs, and comorbidity indices will be discussed. Students will explore the value of standard tools for risk adjustment in Health Services studies. Background reading: Iezzoni LI: Risk and outcomes. In: Iezzoni LI (ed.): Risk adjustment for measuring healthcare outcomes. Health Administration Press, Chicago, 1997. Schneeweiss S, Maclure M: Use of Comorbidity Scores for Control of Confounding in Studies using Administrative Databases. Int J Epidemiol 2000,29:891-898.

  2. Remember: A confounder is an independent risk factor that is unbalanced between exposure groups. (16*920)/(80*184) = 1.0 1.0 2.0 68% 10.0 90%

  3. The Charlson Index:

  4. The Charlson Index for claims data:

  5. The Chronic Disease Score (CDS):

  6. Comorbid conditions according to Elixhauser et al.

  7. CDS revisited:

  8. DRGs:

  9. DRGs: DRG 159 DRG 161 DRG 160 DRG 162

  10. DRGs:

  11. => Prediction of future ambulatory care is easier than prediction of health outcomes: Prior care is a very strong predictor of future care all things equal

More Related