title: Optimization of decision support tool using medication regimens to assess rehospitalization risks.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Biomedical Health Informatics, University of Minnesota , Minneapolis, Minnesota.
authors.forename: C H
authors.initials: CH
authors.lastname: Olson
authors.affiliation: School of Nursing, University of Minnesota , Minneapolis, Minnesota.
authors.forename: M
authors.initials: M
authors.lastname: Dierich
authors.affiliation: Pharmaceutical Care & Health Systems, University of Minnesota Minneapolis , Minnesota.
authors.forename: T
authors.initials: T
authors.lastname: Adam
authors.affiliation: School of Nursing, University of Minnesota , Minneapolis, Minnesota.
authors.forename: B L
authors.initials: BL
authors.lastname: Westra
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control*; D018574:Home Care Agencies / Q000706:statistics & numerical data*; D006801:Humans; D057970:Inappropriate Prescribing / Q000517:prevention & control* / Q000706:statistics & numerical data*; D015994:Incidence; D010359:Patient Readmission / Q000706:statistics & numerical data*; D019338:Polypharmacy; D015995:Prevalence; D018570:Risk Assessment / Q000379:methods; D040242:Risk Reduction Behavior; D014481:United States / Q000453:epidemiology; D019564:Unnecessary Procedures / Q000706:statistics & numerical data
keywords: Patient readmission; ROC curve; home care agencies; medication adherence; polypharmacy