Work

Data-Driven Leadership in Internal Medicine Clinics: A DES–DOE Framework for Optimizing Patient Flow and Turnaround Time

Year: 2025

Type: article

Abstract: Purpose: Efficient leadership in healthcare requires actionable insights to improve both service quality and patient outcomes. This study aims to enhance outpatient service efficiency by applying a data-driven decision-support framework that integrates Discrete Event Simulation (DES) with Design of Experiments (DOE). The focus is on optimizing patient flow and reducing turnaround time in an internal medicine clinic of a Thai public hospital. Methods: A hybrid DES-DOE model was developed to replicate the clinic's real-world processes and assess the impact of key operational factors. The DES model, built in Arena, captured detailed workflows and resource constraints. A full factorial DOE design evaluated five critical variables: patient arrival patterns, physician availability, consultation start-time delays, pre-appointment blood testing, and proportions of patient categories (eg, cardiovascular, neurological, endocrine). A total of 32 scenarios were tested and analyzed using ANOVA. Results: All five factors significantly influenced turnaround time, with patient category proportions showing the strongest effect. The optimized scenario resulted in a 10.46% reduction in average turnaround time. These findings suggest that targeted, evidence-based adjustments can substantially improve patient throughput and clinic performance. Conclusion: This research provides healthcare leaders with a validated, replicable framework for improving operational efficiency through simulation-based experimentation. It demonstrates how integrating DES and DOE can support strategic planning, workforce management, and service design, ultimately contributing to better patient experiences and more resilient healthcare systems.

Source: Journal of Healthcare Leadership

DOI: 10.2147/jhl.s543508

Authors: Chawis Boonmee, Wipada Thamtrakul

Institution: Chiang Mai University

Language: English

Topic: Healthcare Operations and Scheduling Optimization

Subfield: Emergency Medical Services

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,320