A Comprehensive Study on Exploring Operational Research Techniques to Enhance Efficiency in CRO Delivery Models for Patient-Centric Trials

Authors

Prasanna Kumar C S
Saisree Mangu

DOI:

https://doi.org/10.63682/jns.v14i19S.5598

Keywords:

Operational research, Patient-centric, Graph theory, Game theory, Network optimization, Linear programming, CRO delivery model

Abstract

This study presents a comprehensive exploration of Operational Research (OR) techniques aimed at enhancing the efficiency of Contract Research Organization (CRO) delivery models in the context of patient-centric clinical trials. As the pharmaceutical and biotechnology industries increasingly prioritize patient engagement and outcomes, the need for optimized trial designs and execution strategies becomes paramount. This research investigates various OR methodologies, including simulation modeling, linear programming, and decision analysis, to identify bottlenecks and streamline processes within CRO operations. By analyzing case studies and real-world applications, we demonstrate how these techniques can improve resource allocation, reduce trial timelines, and enhance patient recruitment and retention. The findings highlight the potential for OR to transform CRO delivery models, fostering a more agile and responsive framework that aligns with the evolving demands of patient-centric trials. Ultimately, this study contributes to the growing body of knowledge on integrating operational research into clinical trial management, offering actionable insights for CROs seeking to improve their service delivery and patient outcomes.

References

A Malikova, M. (2016). Optimization of protocol design: a path to efficient, lower cost clinical trial execution. ncbi.nlm.nih.gov

Barnes, J. & Harary, F. (1983). Graph theory in network analysis. [PDF]

Guillemin, E. (1955). Network Synthesis. [PDF]

Anisimov, V. & Austin, M. (2022). Modeling restricted enrollment and optimal cost-efficient design in multicenter clinical trials. [PDF]

Bahadori, M., Mohsen Mohammadnejhad, S., Ravangard, R., & Teymourzadeh, E. (2014). Using Queuing Theory and Simulation Model to Optimize Hospital Pharmacy Performance. ncbi.nlm.nih.gov

Bush, N. (2019). Impact of Queueing Theory on Capacity Management in the Emergency Department. [PDF]

C. Mak, B., T. Addeman, B., Chen, J., A. Papp, K., J. Gooderham, M., C. Guenther, L., Liu, Y., C. Broedl, U., & E. Logger, M. (2021). Leveraging Blockchain Technology for Informed Consent Process and Patient Engagement in a Clinical Trial Pilot. ncbi.nlm.nih.gov

Coffey, T., R. Williamson, P., & Gillies, K. (2024). Understanding implementation of findings from trial method research: a mixed methods study applying implementation frameworks and behaviour change models. ncbi.nlm.nih.gov

Crown, W., Buyukkaramikli, N., Sir, M. Y., Thokala, P., Morton, A., Marshall, D. A., Tosh, J. C., Ijzerman, M. J., Padula, W. V., & Pasupathy, K. S. (2018). Application of constrained optimization methods in health services research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force. [PDF]

Di Pumpo, M., Ianni, A., Azzurra Miccoli, G., Di Mattia, A., Gualandi, R., Pascucci, D., Ricciardi, W., Damiani, G., Sommella, L., & Laurenti, P. (2022). Queueing Theory and COVID-19 Prevention: Model Proposal to Maximize Safety and Performance of Vaccination Sites. ncbi.nlm.nih.gov

Doga, H., Emre Sahin, M., Bettencourt-Silva, J., Pham, A., Kim, E., Andress, A., Saxena, S., Bose, A., Parida, L., Lukas Robertus, J., Kawaguchi, H., Soliman, R., & Blankenberg, D. (2024). Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges. [PDF]

Farley, T., Kiefer, J., Lee, P., Von Hoff, D., M Trent, J., Colbourn, C., & Mousses, S. (2013). The BioIntelligence Framework: a new computational platform for biomedical knowledge computing. ncbi.nlm.nih.gov

Ghim, J. L. & Ahn, S. (2023). Transforming clinical trials: the emerging roles of large language models. ncbi.nlm.nih.gov

Ghosh, S., Mallick, A., Chowdhury, A., & De Sarkar, K. (2023). Graph Theory Applications in Advanced Geospatial Research. [PDF]

Heath, A., G. Myriam Hunink, M., Krijkamp, E., & Pechlivanoglou, P. (2021). Prioritisation and design of clinical trials. ncbi.nlm.nih.gov

Huang, Y. L. (1970). An Alternative Outpatient Scheduling System: Improving the Outpatient Experience.. [PDF]

I Nebie, E., van Eeuwijk, P., N. Sawadogo, H., Reus, E., Utzinger, J., & Burri, C. (2024). Operational Differences between Product Development Partnership, Pharmaceutical Industry, and Investigator Initiated Clinical Trials. ncbi.nlm.nih.gov

Jand, H. & Kaur, J. (2017). Graph Theory. [PDF]

Jiang, F. C., Shih, C. M., Wang, Y. M., Yang, C. T., Chiang, Y. J., & Lee, C. H. (2019). Decision Support for the Optimization of Provider Staffing for Hospital Emergency Departments with a Queue- Based Approach. ncbi.nlm.nih.gov

Krishna Rao, R. (2024). Leveraging Decentralized Clinical Trial Management Systems (dCTMS) to Advance Science: Exploring Challenges Related to the Diffusion of Innovation and Its Execution. ncbi.nlm.nih.gov

Lane, K., Majkowski, R., Gruber, J., Amirault, D., Hillery, S., Wieber, C., D Thompson, D., Huvane, J., Bridges, J., Paul Ryu, E., M. Eyzaguirre, L., Gildea, M., E. Thompson, R., E. Ford, D., & Hanley, D. (2022). Using gamification to enhance clinical trial start-up activities. ncbi.nlm.nih.gov

Laskowski, M., D. McLeod, R., R. Friesen, M., W. Podaima, B., & S. Alfa, A. (2009). Models of Emergency Departments for Reducing Patient Waiting Times. ncbi.nlm.nih.gov

Lysenko, A., A. Roznovăţ, I., Saqi, M., Mazein, A., J Rawlings, C., & Auffray, C. (2016). Representing and querying disease networks using graph databases. ncbi.nlm.nih.gov

Marks, L. (2016). TransCelerate: a global collaboration across biopharmaceutical R&D to accelerate and simplify new medi-cines development. [PDF]

Mbuagbaw, L., Foster, G., Cheng, J., & Thabane, L. (2017). Challenges to complete and useful data sharing. ncbi.nlm.nih.gov

Mondal, R., Dung Do, M., Uddin Ahmed, N., Walke, D., Micheel, D., Broneske, D., Saake, G., & Heyer, R. (2023). Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets. ncbi.nlm.nih.gov

Nanzayi Ngayua, E., He, J., & Agyei-Boahene, K. (2021). Applying advanced technologies to improve clinical trials: a systematic mapping study. ncbi.nlm.nih.gov

Narola, J. (2018). Applying the Agile Mechanism in the Clinical Trails Domain for Drug Development. [PDF]

Nebie, E., van Eeuwijk, P., N. Sawadogo, H., Reus, E., Utzinger, J., & Burri, C. (2024). Operational Differences between Product Development Partnership, Pharmaceutical Industry, and Investigator Initiated Clinical Trials. ncbi.nlm.nih.gov

P. Saksena, C. (2018). Some contributions to the theory of mathematical programming. [PDF] Pang, J., Yan, H., & Hua, Z. (2023). Drug Supply Chain Optimization for Adaptive Clinical Trials. [PDF]

Roux, J., Bez, N., Rochet, P., Joo, R., & Mahévas, S. (2023). Graphlet correlation distance to compare small graphs. ncbi.nlm.nih.gov

Snasel, V., Drazdilova, P., & Platos, J. (2018). Closed trail distance in a biconnected graph. ncbi.nlm.nih.gov

Stamenovic, M. & Dobraca, A. (2017). Benefits of Outsourcing Strategy and IT Technology in Clinical Trials. ncbi.nlm.nih.gov

Straube, C., Herschbach, P., & E. Combs, S. (2017). Which Obstacles Prevent Us from Recruiting into Clinical Trials: A Survey about the Environment for Clinical Studies at a German University Hospital in a Comprehensive Cancer Center. ncbi.nlm.nih.gov

Success Ikechi, K., Benedict. A, O., & Ikechukwu.C., E. (2014). Application of Linear Programming Techniques to Practical Decision Making. [PDF]

V Lugg-Widger, F., Angel, L., Cannings-John, R., Hood, K., Hughes, K., Moody, G., & Robling, M. (2018). Challenges in accessing routinely collected data from multiple providers in the UK for primary studies: Managing the morass.. [PDF]