Title

A predictive model to identify skilled nursing facility residents for pharmacist intervention

Document Type

Conference Proceeding

Publication Date

9-1-2018

Abstract

OBJECTIVE: Develop a predictive model to identify patients in a skilled nursing facility (SNF) who require a clinical pharmacist intervention. DESIGN: Retrospective, cross-sectional. SETTING: Nine freestanding SNFs within an integrated health care delivery system. PATIENTS: Patients who received a clinical pharmacist medication review between January 1, 2016, and April 30, 2017. Identified patients (n = 2,594) were randomly assigned to derivation and validation cohorts. INTERVENTIONS: Multivariable logistic regression modeling was performed to identify factors predictive of patients who required an intervention (i.e., medication dose adjustment, initiation, or discontinuation). Patient-specific factors (e.g., demographics, medication dispensings, diagnoses) were collected from administrative databases. A parsimonious model based on clinical judgment and statistical assessment was developed in the derivation cohort and assessed for fit in the validation cohort. MAIN OUTCOME MEASURES: Model to predict patients requiring clinical pharmacist intervention. Secondary outcome was a comparison of factors between patients who did and did not receive a clinical pharmacist intervention. RESULTS: Ninety-five factors were assessed. The derivation (n = 1,299) model comprised 22 factors (area under the curve [AUC] = 0.79, 95% confidence interval [CI] 0.74-0.84). A clopidogrel dispensing (odds ratio [OR] = 2.42, 95% CI 1.19-4.91), fall (OR = 2.47, 95% CI 1.59-3.83), or diagnosis for vertebral fracture (OR = 2.33, 95% CI 1.34-4.05) in the 180 days prior to clinical pharmacist medication review were predictive of requiring an intervention. The model fit the validation cohort (n = 1,295) well, AUC = 0.79 (95% CI 0.74-0.84). CONCLUSION: Administrative data predicted patients in a SNF who required clinical pharmacist intervention. Application of this model in real-time could result in clinical pharmacist time-savings and improved pharmacy services through more directed patient care.

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