From Data Chaos To Clarity: How To Modernize Clinical Trial Site Selection
By Thierry Escudier and Aditya Tyagi, Pistoia Alliance
In recent years, the complexity of clinical trials has surged dramatically, introducing new challenges for clinical operations (ClinOps) teams. Trials now involve more regions — with Phase 3 trials seeing a 39% increase1 in countries since 2015 — more investigator sites and more protocol amendments. Additionally, the push for greater patient diversity necessitates that ClinOps teams gain a deeper understanding of patient populations before designing a protocol. Such complexities result in unplanned delays and unforeseen costs. For instance, a single Phase 3 protocol amendment can incur an additional $535,0002 in direct unbudgeted costs and extend the trial timeline by three months.
One area where many of these complexities collide is investigator site selection. Identifying and vetting the right investigator sites is crucial for the success of any clinical trial, yet it remains time-consuming and expensive. Largely this is because ClinOps teams are still contending with the overarching burden of manual processes that persist during investigator site selection. Embracing automation and streamlining site selection workflows could significantly reduce costs and accelerate study timelines, ultimately improving the efficiency and effectiveness of clinical trials.
Why A Single-Use Approach To Site Selection Data Is Inefficient And Costly
Currently, the investigator site selection process begins with questionnaires that evaluate whether a site has the necessary resources, personnel, and experience to conduct a proposed study. Feasibility questionnaires sent to all proposed sites cover information including the geographical location, patient demographics, staff qualifications, and the availability of equipment and laboratory facilities.
The challenge is that each CRO or sponsor has its own unique format for these questionnaires, leading to a lack of standardization across the industry. Questions and answers also often use different vocabularies since there are no agreed industrywide terminologies. For instance, a sponsor might enquire about access to an electrocardiogram (ECG), while the site refers to it as a “heart monitor.” Similarly, a sponsor might seek sites with a Good Clinical Practice (GCP) certification, but a site might only disclose it has “clinical trial training.”
The lack of standardization in terminology also prevents selection data from being reused. Sponsors and CROs often do not digitize and store the data they have collected from site feasibility questionnaires. Even when information is digitized and saved, it remains unsearchable because key terminologies aren’t linked or related in a machine-readable way. For example, if a sponsor searches for “GCP certified” sites, then any questionnaires that mention synonyms like “GCP trained” or “ethical conduct certification” will be missed. This makes it almost impossible for ClinOps teams to search past questionnaires to accelerate the site selection of new protocols.
The inconsistencies in site feasibility questionnaires create a single-use approach to data that is both inefficient and costly. Sponsors must repeatedly gather the same information from sites, and sites must fill out multiple questionnaires for different sponsors. This redundancy is time-consuming and frustrating for all parties, underlining the need for a more streamlined and automated approach to site selection.
Applying A Data Science Technique To Make Data Reusable
One way of unlocking automation potential and digitizing ClinOps data is using ontologies. This data science technique creates human-generated, machine-readable descriptions3 of a domain, which broadly consists of types of things and the relationships between them. A basic example could be that an “egg” is a type of “food.” There could also be subtypes according to how the egg is prepared, with “fried egg” linked as a synonym to “sunny side up.” In the life sciences, “clinical trial” could be a branch of “drug development,” with "Phase 1 trial" linked as a synonym to "first-in-human study."
Applying ontologies to standardize and structure site selection data would create a community consensus view of the domain that is updated as the field evolves. This method would make site selection information searchable and reusable, accelerating clinical trial setup and reducing the manual burden for all.
ClinOps Ontology Project: An Industry-Led Initiative
The utility of ontologies for accelerating site selection is being demonstrated by the ClinOps Ontology Project, developed by the global nonprofit Pistoia Alliance. In contrast to existing commercially driven databases on investigator sites, the Alliance’s project explores a unified, machine-readable data backbone for automating site feasibility questionnaires that will ultimately be made open source to benefit the entire industry.
The initiative is supported by a steering committee of experts from four major pharma companies — Novartis, Roche, Merck, and Boehringer Ingelheim. The initial proof of concept (PoC) focused on personnel and materials site selection factors — encompassing the expertise and infrastructure a site has. These domains were chosen due to the availability of data sets that could be used to map the relationship between technical terms, including merging synonyms. These include, for instance, the Unified Study Definitions Model, Digital Data Flow, Shared Investigator Platform (SIP), and SNOWMED CT.
The project marks a significant advance in modeling technical clinical trial design terminologies in a machine-readable format. The Alliance plans to expand the ontology to other site selection factors, such as patient recruitment, sample management, vendor management, and budgets. The benefits from the successful completion of this project will be significant; by automating questionnaire design, data capture, and analysis, the current 10-week site selection process could be reduced by up to 80% once the ontology is complete.
Automating The Future Of ClinOps
The ClinOps Ontology Project holds immense promise for transforming clinical operations. By creating a common language agreed upon by stakeholders across the industry, the data model will facilitate better communication between CROs, sponsors, technology companies, and sites. Long term, the approach will both reduce manual burden and unlock efficiencies, reduce associated costs, and bring new treatments to patients faster.
As clinical trials grow more complex, such projects are essential. The Alliance’s ontology paves the way for automating other areas of clinical operations, such as IP management, regulatory submissions, and vendor management. But transformation cannot be achieved in isolation. Many companies are running their own initiatives, and the industry must ensure efforts (and errors) aren’t being duplicated. We must collaborate to innovate. The future of clinical operations depends on industrywide efforts like the ClinOps Ontology Project, so sponsors, CROs, and sites can overcome common hurdles and unlock efficiencies together.
For further information visit www.pistoiaalliance.org.
References:
- Tufts University, Protocol design scope, and execution burden continue to rise, most notably in Phase III, Tufts CSDD Impact Report, Volume 25, Number 3, 2023. https://9468915.fs1.hubspotusercontent-na1.net/hubfs/9468915/May-June%202023%20-%20Protocol%20Scope%20and%20Execution_Page_1.jpg
- James Miessler, No End in Sight for Trial Complexity, CSDD Report Reveals, CenterWatch, 2022. https://www.centerwatch.com/articles/25921-no-end-in-sight-for-trial-complexity-csdd-report-reveals
- Jane Lomax, What Are Ontologies and How Are They Creating a FAIRer Future for the Life Sciences?, Technology Networks, 2022. https://www.technologynetworks.com/informatics/articles/what-are-ontologies-and-how-are-they-creating-a-fairer-future-for-the-life-sciences-363371
About The Authors:
Since October 2023, Thierry Escudier has served as a portfolio lead for the Pistoia Alliance. Prior to this, he was a strategic leader at the Pistoia Alliance for over 2 1/2 years. Thierry brings an extensive background in clinical operations, having worked for Pierre Fabre Medicament for 25 years, with a strong focus on digital innovation and patient centricity.
Aditya Tyagi is a clinical operations expert and project manager at the Pistoia Alliance, where he leads the ClinOps project. Aditya has over 16 years of experience in managing clinical trials in both the U.S. and India. He has overseen pre-clinical and phase I-IV clinical trials in areas such as oncology, cardiology, stroke, and rare diseases.