Pharmacist-Led Surgical Medicines Prescription Optimization and Prediction Service Improves Patient Outcomes -A Machine Learning Based Study

Xianlin Li ¹ Xiunan Yue ¹ Lan Zhang ² Xiaojun Zheng ³ Nan Shang ^3*

• ¹ Shanxi Medical University, Taiyuan, Shanxi Province, China
• ² Capital Medical University, Beijing, Beijing Municipality, China
• ³ First Hospital of Shanxi Medical University, Taiyuan, China

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 (April 1, 2019 to March 31, 2022), (2) Establishment of a pharmacist-led management model (April 1, 2022 to March 31, 2023), and (3) Outcome evaluation (April 1, 2023 to March 31, 2024). Key variables, including gender, age, number of comorbidities, type of surgery, surgery complexity, days from hospitalization to surgery, type of prescription, type of medication, route of administration, and prescriber’s seniority were collected. The data set was divided into training set and test set in the form of 8:2. The effectiveness of the SMPOP service was evaluated based on prescription appropriateness, adverse drug reactions (ADRs), length of hospital stay, total hospitalization costs, and medication expenses. Results In Phase 1, 6,983 prescriptions were identified as potential prescription errors (PPEs) for ML model development, with 43.9% of them accepted by prescribers. The Random Forest (RF) model performed the best (AUC = 0.893) and retained high accuracy with 12 features (AUC = 0.886). External validation showed an AUC of 0.786. In Phase 2, SMPOP services were implemented, which effectively promoted effective communication between pharmacists and physicians and ensured the successful implementation of intervention measures. The SMPOP service was fully implemented. In Phase 3, the acceptance rate of pharmacist recommendations rose to 71.3%, while the length of stay, total hospitalization costs, and medication costs significantly decreased (p < 0.05), indicating overall improvement compared to Phase 1. Conclusion SMPOP service enhances prescription appropriateness, reduces ADRs, shortens stays, and lowers costs, underscoring the need for continuous innovation in healthcare.