ClinicalTrials.gov as a Data Source for Semi-Automated Point-Of-Care Trial Eligibility Screening
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CitationPfiffner, Pascal B., JiWon Oh, Timothy A. Miller, and Kenneth D. Mandl. 2014. “ClinicalTrials.gov as a Data Source for Semi-Automated Point-Of-Care Trial Eligibility Screening.” PLoS ONE 9 (10): e111055. doi:10.1371/journal.pone.0111055. http://dx.doi.org/10.1371/journal.pone.0111055.
AbstractBackground: Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies. Objective: To evaluate the utility of the ClinicalTrials.gov registry as a data source for semi-automated trial eligibility screening. Methods: Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in ClinicalTrials.gov. Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status. Results: 24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients. Conclusion: Because ClinicalTrials.gov has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting ClinicalTrials.gov data entry forms to capture key eligibility criteria in a simple, structured format.
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