Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio

Author:

Keats Kelli1,Sikora Andrea2,Heavner Mojdeh S.3,Chen Xianyan4,Smith Susan E.2,

Affiliation:

1. Department of Pharmacy, Augusta University Medical Center, Augusta, GA.

2. Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA.

3. Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD.

4. Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA.

Abstract

BACKGROUND: The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients. METHODS: Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis. RESULTS: Our protocol describes the processes and methods for an observational study in the ICU. CONCLUSIONS: This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Critical Care and Intensive Care Medicine

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