Contents
• Study site & selection
• Introduction
• Overview of data driven paradigm in CT using ML
• Site selection Data
• Data driven site selection with ML
• Conclusion
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Study site & selection
• A clinical trial site is a controlled environment where clinical research, development
and testing can take place safely with appropriate oversight.
• Common site selection strategies use past trial data to assess how well a site would
perform in a prospective clinical trial and different standardized and objective methods
have been developed across industry and academia.
• Site Qualification Visit (SQV): A meeting with a representative from a sponsor
representative to ensure the institution is fully capable and equipped to run a specific
clinical trial.
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• Clinical trials are struggling, just look at the statistics. Despite all of the innovation
and breakthroughs in drug discovery over the last several years, it still takes 10 to 15
years and about 1 billion to develop a new therapy.
• Globally, more than 80% of trials fail to meet enrollment deadlines, resulting in
costly extensions, addition of new study sites, or outright study failure..
• There are many reasons for this, of course. First, there is the enormity and complexity
of the challenge of safely bringing a new drug to market.
• Recently, a new solution was designed to change that status by implementing a real-
world data-driven approach to the clinical trial site selection process.
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Introduction

Site selection data
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The data types that need to be gathered:
1. Demographic Data
2. Economic Indicators
3. Geographic and Environmental Factors
4. Infrastructure and Accessibility
5. Competitive Landscape

Overview of Data driven paradigm in CT using ML
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There is an increasing demand from sponsors for technology and data-driven
solutions to improve site selection, study start-up and patient recruitment.
With the explosion of data and improvements in analytical techniques, including
emerging technologies that incorporate machine learning and artificial intelligence
(AI), sponsors are also demanding greater transparency about how these processes
are carried out.
Improving the use of data analytics and technology would allow for an increased
ability to re-use site information on multiple studies and reduce the time between site
selection and initiation. In addition, it could support increased engagement and
partnering with sites with known patient populations.
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Data driven site selection with ML

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Overview of data processing in site selection using ML

Conclusion
• The ML technology have made significant progress in medicine and healthcare. Ongoing
progresses in research from ML areas are creating the recent paradigm shift in medicine and
healthcare. However, the implementation of these technologies in real-world medical fields is
still in its infancy and huge concern. These technologies are in the early stages of development.
• All recent AI–based research technologies need to scale up for use. At the same time, all the
technology needs proper validation and implementation. Therefore, the technologies must be
scalable and applicable. Similarly, during technological implementation and use, there should be
collaboration among computer scientists, physicians and data scientists, healthcare providers,
and engineers, which is essential now. At the same time, new and diversified algorithms are
needed with numerous medical applications and to improve the quality of diagnostics and
therapeutics.
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Thank You!
www.clinosol.com
(India | Canada)
9121151622/623/624
info@clinosol.com
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Data-Driven Site Selection: Leveraging Machine Learning

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