Digital Twin Technology for Simulating Hospital Pharmacy Operations and Workflow Improvements

Published Paper: PDF

DOI: https://doi.org/10.63345/ijrmp.v14.i7.4

Prof. (Dr.) Punit Goel

Maharaja Agrasen Himalayan Garhwal University

Uttarakhand, India

drkumarpunitgoel@gmail.com

Abstract

Digital Twin Technology (DTT) creates a dynamic, data-driven virtual replica of physical systems. In hospital pharmacies—where medication safety, inventory control, dispensing speed, and interdisciplinary coordination are mission-critical—digital twins can simulate end-to-end workflows, detect bottlenecks, test redesigns, and support real-time decision making without disrupting patient care. This manuscript examines how DTT can be harnessed to model hospital pharmacy operations, integrate clinical research data, and evaluate intervention scenarios ranging from automated dispensing cabinets (ADCs) to AI-driven demand forecasting. Drawing on systems engineering, operations research, and clinical informatics perspectives, we demonstrate a conceptual and methodological framework for constructing and validating a pharmacy digital twin. A mixed-methods clinical research design combining retrospective transactional data, time–motion observations, and stakeholder interviews feeds the computational model. Discrete-event simulation (DES), agent-based modeling (ABM), and hybrid system dynamics (SD) elements are woven together in the twin to represent patient flows, medication compounding, verification steps, and clinical pharmacist interventions at the bedside. Results from simulated experiments—such as reallocating staff, altering batch compounding schedules, or implementing RFID-based tracking—show statistically significant reductions in turnaround time (TAT), near-miss errors, and stock-outs while optimizing cost per dispensed dose. The study concludes that DTT is not merely a visualization tool but a continuous learning platform that supports quality improvement, resilience engineering, and regulatory compliance. Scope and limitations include data quality issues, organizational readiness, cybersecurity risks, and generalizability across diverse hospital sizes. We propose future research on federated twins across multi-hospital networks, predictive maintenance of pharmacy automation, and integration with digital therapeutics.

Keywords

Digital twin; hospital pharmacy; workflow simulation; discrete-event simulation; agent-based modeling; clinical research; medication safety; inventory optimization; turnaround time; operational excellence

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