Clinical Workflow Optimization Using Data-Driven Process Modeling

Abstract

Clinical motivation, objectives, data-driven approach, methods, findings, and implications are synthesized. Clinical workflows govern patient management in specialty practices, creating a need for technique deployment to improve operational outcomes and resource utilization. Data-Driven Process Modeling uses systematic data analysis to provide rigorous evidence and formal structure to clinical decision-making. ‡Process mining identifies the real recurrence of patient journeys; machine learning models future event occurrences to replicate desirable flows; simulation of mapped processes quantifies throughput, waits, and cycle times; and hybrid methods combine these techniques. Together, they identify bottlenecks and variations affecting performance.

An Emergency Department throughput case shows how analyzed paths reveal long wait times at triage and radiology, together with extended time between physician disposition and patient departure. Measurable improvements result from targeted interventions. In a second application, development of Machine Learning algorithms for segments of an inpatient discharge planning pathway highlights excessive attorney handoffs, leading to simplified processes and faster discharge times. Analysis of quantitative results indicates throughput times, waits, and cycle times are shortened, variance reduced, and changes statistically significant. ‡Qualitative evidence complements numbers by revealing practitioner satisfaction and user acceptance.