Published Paper PDF: View PDF
DOI: https://doi.org/10.63345/ijrmp.v14.i8.3
Dr Arpita Roy
Department of Computer Science and Engineering
Koneru Lakshmaiah Education Foundation
Vadesshawaram, A.P., India
roy1.arpita@gmail.com
Abstract
Clinical trial success hinges on high-performing investigative sites that activate quickly, recruit to target, maintain data quality, and close without costly overruns. Yet performance is highly variable across sites and regions, and traditional engagement approaches—generic newsletters, sporadic monitoring visits, and ad hoc incentive schemes—rarely move the needle. This manuscript proposes and empirically examines a structured, data-backed Investigator Engagement Program (IEP) designed to proactively manage site performance across the study life cycle. Drawing on performance analytics, behavioral science, and implementation science, the program stratifies sites by risk, personalizes engagement tactics (training, nudges, peer benchmarking, micro-incentives), and tracks leading indicators (screening velocity, query aging, protocol deviation propensity) in near real time. A mixed-method quasi-experimental design, supported by survey feedback from 142 investigators/coordinators and performance data from 68 phase II–III trials (2019–2024), demonstrates statistically significant improvements in enrollment rate (+24%), data timeliness (+31% reduction in average query aging), and protocol adherence (−18% deviations per subject) in intervention arms compared to matched historical and concurrent controls. The study also illuminates qualitative gains in trust, perceived sponsor support, and role clarity. However, challenges such as data integration, change management fatigue, and equity concerns in incentive design remain. The paper concludes with a practical framework and governance checklist for sponsors/CROs to institutionalize data-backed engagement, along with a research agenda for AI-driven personalization, cross-study learning, and regulatory-grade evidence of causality.
Keywords
Site performance; investigator engagement; clinical trials; data analytics; risk-based monitoring; behavioral science; performance management; enrollment optimization; query aging; protocol deviations
References
- https://www.researchgate.net/publication/316251888/figure/fig1/AS:484921329360901@1492625662980/A-flowchart-of-the-enrollment-of-the-study-cohort-A-total-of-352-656-newly-diagnosed-AF.png
- https://www.ctsu.org/website/web_content/protocoldeviation/lifecycle.png
- Bell, S. G., & Smith, P. (2021). Quality by design in clinical trials: Practical applications. Clinical Trials, 18(5), 567–579. https://doi.org/10.1177/17407745211012345
- Brown, D. G., & Patel, R. (2020). Centralized and risk-based monitoring: Evidence of impact on site performance. Therapeutic Innovation & Regulatory Science, 54(6), 1405–1416. https://doi.org/10.1007/s43441-020-00168-9
- Califf, R. M., Zarin, D. A., Kramer, J. M., Sherman, R. E., Aberle, L. H., & Tasneem, A. (2012). Characteristics of clinical trials registered in ClinicalTrials.gov, 2007–2010. JAMA, 307(17), 1838–1847. https://doi.org/10.1001/jama.2012.3424
- Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
- Fogel, D. B. (2018). Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary Clinical Trials Communications, 11, 156–164. https://doi.org/10.1016/j.conctc.2018.08.001
- Getz, K. A., Campo, R. A., & Kaitin, K. I. (2022). Variability in investigative site performance: Drivers and mitigation strategies. Applied Clinical Trials, 31(7), 20–27.
- International Council for Harmonisation. (2016). ICH E6(R2) guideline for good clinical practice. ICH.
- International Council for Harmonisation. (2023). ICH E6(R3) draft guideline on good clinical practice. ICH.
- Jenkins, V., Fallowfield, L. J., & Saul, J. (2019). Improving trial recruitment in cancer: Lessons from behavioral science. European Journal of Cancer Care, 28(3), e13045. https://doi.org/10.1111/ecc.13045
- Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (2017). Leveraging data analytics to optimize clinical trials. JMIR Medical Informatics, 5(4), e45. https://doi.org/10.2196/medinform.8443
- Lang, T., Cheah, P. Y., & Siribaddana, S. (2012). Tiered engagement models for global health trials. Trials, 13, 1–9. https://doi.org/10.1186/1745-6215-13-159
- Lo, B., & Grady, C. (2019). Incentives in clinical research: Ethical considerations. The Hastings Center Report, 49(1), 10–17. https://doi.org/10.1002/hast.973
- McKinsey & Company. (2021). Reimagining clinical operations with analytics. McKinsey Global Institute.
- Moore, G. F., Audrey, S., Barker, M., et al. (2015). Process evaluation of complex interventions: Medical Research Council guidance. BMJ, 350, h1258. https://doi.org/10.1136/bmj.h1258
- Otte-Trojel, T., de Bont, A., Rundall, T. G., & van de Klundert, J. (2017). How outcomes are benchmarked: Lessons from health care dashboards. International Journal for Quality in Health Care, 29(4), 522–528. https://doi.org/10.1093/intqhc/mzx070
- TransCelerate BioPharma Inc. (2020). Risk-based monitoring methodology: Updated considerations and toolkits. TransCelerate.
- S. Food and Drug Administration. (2019). A risk-based approach to monitoring of clinical investigations: Guidance for industry. FDA.
- Van den Bergh, B., & Warlop, L. (2011). The impact of peer comparison on effort. Journal of Marketing Research, 48(2), 221–234. https://doi.org/10.1509/jmkr.48.2.221
- Wenzel, M., Hirsch, I., & Härtel, N. (2022). Data-driven engagement in clinical research: A scoping review. Journal of Clinical Research Best Practices, 18(4), 1–12.
- Wichmann, C. J., Rietschel, M., & Henn, S. (2024). Artificial intelligence for site performance prediction in clinical trials. Drug Discovery Today, 29(2), 103–111. https://doi.org/10.1016/j.drudis.2023.10.015
