Parshv praful Gala1 & Dr. Shruti Saxena2
1Carnegie Mellon University
Pittsburgh, USA
2Savitribai Phule pune university
Pune, India
Abstract — The convergence of data harvesting and machine learning (ML) into clinical trials has transformed the pharmaceutical research and development landscape. This paper analyzes the progress of these technologies from 2015 to 2024, highlighting their application to optimize site selection, investigator performance, protocol design, and competitive intelligence. Data harvesting entails gathering disparate datasets such as electronic health records (EHRs), clinical trial databases, and even social media, to deliver real-time, actionable information. Machine learning, when applied to these datasets, makes it possible to predict patient eligibility, determine high-performing trial sites, and tailor trial protocols according to patient profiles. Multiple studies have illustrated how machine learning models can optimize recruitment efficiency, with models having up to 92% accuracy in predicting patient eligibility. Also, predictive analytics and data integration methodologies have accelerated site selection by scrutinizing historical performance data, cutting down on site identification time by 40%. AI-powered protocol design and real-time monitoring have yielded significant cuts in protocol amendments and resource wastage. Lastly, machine learning tools applied to competitive intelligence have allowed sponsors to forecast market trends and competitor plans, bestowing a strategic edge on them. In all, the confluence of data harvesting and machine learning has resulted in more efficient, cost-saving, and customized clinical trials, and is set to continue leading drug development innovation. These technologies are set to not only improve the trial process but also speed up and increase the success rate of clinical research.
Keywords— Data harvesting, machine learning, clinical trials, site selection, investigator performance, protocol design, competitive intelligence, predictive analytics, patient recruitment, personalized medicine, trial optimization, real-time monitoring, AI-driven insights, data integration, pharmaceutical research.
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