Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms

BACKGROUND: Patient satisfaction is one of the primary Key Performance Indicator (KPI) goal of health care service, and it creates many reasons for implementing research, plans, and innovations to achieve it for a better quality of life. Cutting Patient waiting time would increase patient satisfaction. OBJECTIVE: A healthcare framework has been constructed utilizing a machine learning approach to construct an early predicting preparation model of pharmacy prescriptions and the worthiness of changing the outpatient pharmacy workflow. METHODS: Data sets were retrieved between Januarys and June 2019 from Prince Sultan Military Medical City, Riyadh, KSA, for all patients who visited the clinics or discharged with pharmacy prescriptions. Included (1048575) instances and composed of (11) attributes. The evaluation criteria to compare the four algorithms were based on precision, Recall, True Positive Rate, False Negative Rate, F-measure, and Area under the curve. RESULTS: Overall, 94.88% of patient’s shows at the pharmacy, female represents 58.89% of the data set while male represents 41.1%. RT gives the highest accuracy, with 97.22% in comparison to the other algorithms. CONCLUSION: The suggestion to change the pharmacy workflow is worth increasing patient satisfaction and overall the quality of the care.