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Optimization of decision support tool using medication regimens to assess rehospitalization risks
C H Olson1, M Dierich2, T Adam3
1Biomedical Health Informatics, University of Minnesota , Minneapolis, Minnesota.
Applied Clinical Informatics
|October 10, 2014
Summary
An improved algorithm accurately predicts elderly patients at high risk for hospital readmission using medication data. Optimizing criteria enhances prediction accuracy for this vulnerable population.
Area of Science:
- Gerontology
- Health Informatics
- Pharmacoeconomics
Background:
- Unnecessary hospital readmissions pose a significant financial burden on the U.S. healthcare system.
- An automated algorithm was previously developed to identify elderly patients at high risk for rehospitalization based on medication regimens.
Purpose of the Study:
- To enhance an existing algorithm for predicting elderly patients' hospital readmission risk.
- To optimize the sensitivity of medication criteria within the predictive algorithm.
Main Methods:
- Utilized Outcome and Assessment Information Set (OASIS) and medication data from 911 patients across 15 home health agencies.
- Employed Odds Ratio analyses, literature reviews, and clinical judgment to refine High Risk Medication Regimens (HRMR) scoring.
- Applied Receiver Operating Characteristic (ROC) analysis to assess improvements in predictive accuracy.
Main Results:
- Evaluated HRMR components: polypharmacy (AUC .68 excluding supplements), potentially inappropriate medications (PIM) (AUC .60), and Medication Regimen Complexity Index (MRCI) (AUC .69).
- Modified the MRCI 'cut point' from 20 to 33 to better indicate medication-related readmission risk.
- Identified polypharmacy definition adjustments and MRCI cut point modifications as key improvements.
Conclusions:
- The refined automated algorithm effectively predicts elderly patients at risk of hospital readmission.
- Modifications to polypharmacy definition and MRCI cut point significantly improved the algorithm's predictive capabilities.
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