Workforce Optimization Models in Multi-Center Clinical Trial Projects

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DOI: https://doi.org/10.63345/ijrmp.v14.i8.1

Prof. (Dr) Avneesh Kumar

Galgotias University

Greater Noida, Uttar Pradesh 203201 India

avneesh.avn119@gmail.com

Abstract

Multi-center clinical trials (MCCTs) are increasingly complex, geographically dispersed, and resource intensive. Suboptimal workforce planning leads to protocol deviations, delays in patient recruitment, cost overruns, and staff burnout. This manuscript synthesizes operations research, project management, and health services literature to propose an integrated workforce optimization framework tailored for MCCTs. Using a mixed-method design—systematic review, process mapping, discrete-event simulation (DES), and multi-objective optimization (MOO)—we model personnel allocation across study phases (start-up, recruitment, intervention/follow-up, closeout) and across functional silos (site management, data management, regulatory, pharmacovigilance, biostatistics). The results section presents a hypothetical but realistic case of a 25-site oncology trial to demonstrate how task-based effort estimation, skill-matrix mapping, and robust scheduling (with stochastic demand for monitoring and query resolution) can reduce cycle times by 18%, staffing costs by 12%, and overtime by 35% without compromising quality indicators such as query turnaround, monitoring visit frequency, and adverse event reporting timeliness. We conclude that optimization-driven workforce planning should be embedded as a continuous, data-informed process supported by digital twins and adaptive dashboards. Future work can extend the framework with machine-learning forecasts of recruitment velocity, federated learning-based productivity benchmarks, and equity-centered staffing policies.

Keywords

Workforce optimization; multi-center clinical trials; discrete-event simulation; multi-objective optimization; staffing models; resource allocation; project management; operations research; digital twin; recruitment forecasting

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