Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study.

ABSTRACT:

Background

Optimizing prescription practices for surgical patients is crucial due to the complexity and sensitivity of their medication regimens. To enhance medication safety and improve patient outcomes by introducing a machine learning (ML)-based warning model integrated into a pharmacist-led Surgical Medicines Prescription Optimization and Prediction (SMPOP) service.

Method

A retrospective cohort design with a prospective implementation phase was used in a tertiary hospital. The study was divided into three phases: (1) Data analysis and ML model development (1 April 2019 to 31 March 2022), (2) Establishment of a pharmacist-led management model (1 April 2022 to 31 March 2023), and (3) Outcome evaluation (1 April 2023 to 31 March 2024). Key variables, including gender, age,

SUBMITTER: Li X 

PROVIDER: S-EPMC11949800 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature