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Consult Pharm. 2018 Sep 1;33(9):504-520. doi: 10.4140/TCP.n.2018.504.

A Predictive Model to Identify Skilled Nursing Facility Residents for Pharmacist Intervention.

Author information

1
At the time of this study, was an outcomes research fellow in ambulatory care, Pharmacy Department, Kaiser Permanente Colorado, and Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado. Dr. Heath is now assistant professor, Clinical Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah.
2
Pharmacy Department, Kaiser Permanente Colorado, and Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences.
3
Clinical pharmacy specialist in complex care home rounding, Pharmacy Department, Kaiser Permanente Colorado, and clinical assistant professor, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, and clinical adjunct faculty, Department of Pharmacy Practice, Regis University School of Pharmacy, Denver, Colorado.
4
Clinical pharmacy specialist in continuing care, Pharmacy Department, Kaiser Permanente Colorado, and clinical instructor, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences.
5
Clinical pharmacy specialist in continuing care, Pharmacy Department, Kaiser Permanente Colorado, and clinical instructor, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, and clinical adjunct faculty, Department of Pharmacy Practice, Regis University School of Pharmacy.
6
Clinical pharmacy specialist in medication safety and clinical pharmacy specialties supervisor, Pharmacy Department, Kaiser Permanente Colorado, and clinical associate professor, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, and clinical adjunct faculty, Department of Pharmacy Practice, Regis University School of Pharmacy.

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.

PMID:
30185291
DOI:
10.4140/TCP.n.2018.504
[Indexed for MEDLINE]

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