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% increase in countries since 2015 — more investigator sites and 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.
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.
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 lack of standardization in terminology also prevents selection data from being reused. Sponsors and CROs often do not digitize and store the data 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.
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.
One way of unlocking automation potential and digitizing ClinOps data is using ontologies. This data science technique creates human-generated, machine-readable descriptions of a domain, broadly consisting of types of things and their relationships. 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.
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