Clinical studies are the cornerstone of modern evidence-based healthcare and medical knowledge. ClinicalTrials.gov — the world’s largest database of interventional and observational studies — lists nearly 250,000 studies from 196 countries, and each year the number of studies added grows.
But despite this effort, data generated in clinical studies remains underused. This is because data sets exist in closed silos and on separate servers, there are no established ways to provide comprehensive meta-level information about data, and there is also a lack of widely used protocols for agreeing on the terms of using data and protecting participants’ anonymity.
Our aim was to design, prototype, and test an end-to-end Vivli experience for two types of users:
The prototypes were used for two purposes. Externally, they were used as stimulus material in expert research that we conducted with thought leaders in information sharing platforms in healthcare, and with Vivli’s potential users working all over the world in various medical domains, such as: randomised clinical trials, evidence synthesis, clinical informatics, and secondary analysis. Our findings and evidence gathered during this research were later used to improve the prototype and refine the Vivli experience.
The prototypes were also used internally, as a tool for strengthening the Vivli consortium, and communicating Vivli’s vision to stakeholders and collaborators. The following video demonstrates the functionality of the Search and Request prototype.
During our research we were able to identify a range of challenges that a data platform such as Vivli is going to face. Here are just a few of our findings that informed design decisions:
Existing platforms offer limited search capabilities, restricted to a minimal selection of high-level search terms. Vivli will provide access to rich metadata annotation, allowing requesters to create rich search queries. We designed an interactive query tool augmented by Cochrane’s PICO ontology (Population, Intervention, Comparator, Outcome) to assist researchers in formulating clinical questions and contextualised queries.
We also found that researchers often waste time requesting data that turns out to be of low quality or lacking relevant variables. To address this problem, we designed a data quality score and a list of high-level data schema that would be shown alongside each query result before a request is made.
We found that academic researchers who conduct clinical trials find limited value in making their data available for secondary analysis. This is unless data sharing is rewarded in academia’s main currency — publication citations. To incentivise better data sharing amongst academics, we designed a mechanism for tracking data usage and citations on the submitter’s account.
Study searches are only as good as the study description that is curated during study submission. We designed a curation process through which submitters use automatic extraction of metadata elements from public registries and the study protocol. This is combined with manual refinement of metadata annotation and aided with a preview of the curated study as seen by requesters in search results.
Because of the sensitive nature of clinical study data, its sharing is subject to a review process, legal arrangements, and data processing that takes time and is handled by multiple stakeholders. This is another hurdle for requesters so we designed a range of mitigating features that improve the transparency of the review process, such as live visualisations of the request status, tailored wait estimations, and a simplified online data usage agreement.