Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks
23 April 2014
accepted: 16 July 2014
19 December 2017 (online)
Background: Unnecessary hospital readmissions are costly for the U.S. health care system. An automated algorithm was developed to target this problem and proven to predict elderly patients at greater risk of rehospitalization based on their medication regimens.
Objective: Improve the algorithm for predicting elderly patients’ risks for readmission by optimizing the sensitivity of its medication criteria.
Methods: Outcome and Assessment Information Set (OASIS) and medication data were reused from a study that defined and tested an algorithm for assessing rehospitalization risks of 911 patients from 15 Medicare-certified home health care agencies. Odds Ratio analyses, literature reviews and clinical judgments were used to adjust the scoring of patients’ High Risk Medication Regimens (HRMRs). Receiver Operating Characteristic (ROC) analysis evaluated whether these adjustments improved the predictive strength of the algorithm’s components.
Results: HRMR scores are composed of polypharmacy (number of drugs), potentially inappropriate medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, dose frequency, instructions or administration). Strongest ROC results for the HRMR components were Areas Under the Curve (AUC) of .68 for polypharmacy when excluding supplements; and .60 for PIM and .69 for MRCI using the original HRMR criteria. The “cut point” identifying MRCI scores as indicative of medication-related readmission risk was increased from 20 to 33.
Conclusion: The automated algorithm can predict elderly patients at risk of hospital readmissions and its underlying criteria is improved by a modification to its polypharmacy definition and MRCI cut point.
Citation: Olson CH, Dierich M, Adam T, Westra BL. Optimization of decision support tool using medication regimens to assess rehospitalization risks. Appl Clin Inf 2014; 5: 773–788
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