UTILIZATION OF MACHINE LEARNING TO IMPROVE PRODUCTIVITY OF INTERVENTIONS CLASSIFICATION FOR A UNIVERSITY TEACHING HOSPITAL IN SINGAPORE

Introduction: Pharmacists have an integral role in the medication use process in performing interventions. In TTSH, accurate classification of interventions is important for supporting a learning culture within the hospital. Pharmacists spend at least 80 hours to look through 10,000 interventions monthly to ensure accurate classification. In recent years, artificial intelligence (AI) has been increasingly used in healthcare and may potentially be used to perform interventions classification. Aim: We aim to develop an intervention classification algorithm which can assist pharmacists in accurate classification of interventions. Pharmacists’ time spent can be reduced substantially and be better channeled to other higher leveled tasks to improve patient’s care. Methods: In designing the model for intervention classification algorithm, at least 80 000 pharmacist-checked intervention categories (PIC) were sent to Health Services & Outcomes Research, a division under the National Healthcare Group of Singapore (NHG). AI-predicted intervention categories (AIC) with a precision >90% matched PIC at least 95% of the time and exempted from manual pharmacists’ checking. Results: After utilization of the ML algorithm, 28% of inpatient interventions were exempted from pharmacists’ checking, corresponding to average time savings of 15.7 hours monthly and cost savings of $485.27. 33% of outpatient interventions were exempted from pharmacists’ checking, achieving an average time savings of 9.7 hours monthly and cost savings of $299.30. Conclusion: A real-world implementation of a ML model to classify interventions had led to significant savings on pharmacists’ time which could be channeled to perform clinical tasks to optimize patient outcomes.