PURPOSE Tumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching. METHODS Clinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated. RESULTS Manual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches. CONCLUSION Reflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.
In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.
e18006 Background: Clinical trial enrollment is an assiduous process and requires active initiation and maintenance efforts by providers, patients, trial investigators, or other clinical staff. Setting up automated process triggers to perform a reflex clinical trial matching can kick-start the process without requiring human intervention. Methods: Using a clinical trial matching service developed in collaboration with GenomOncology, we used the receipt of sequencing test results as a process trigger to perform reflex clinical trial matching on oncology patients. A research nurse performed additional refinements to these results using multi-faceted filtering and an initial manual prescreening. In this pragmatic study, providers were randomized to receive results of prescreening events. EMR messages were sent to the intervention cohort of providers with recommendations for clinical trials for their patients and suggested next steps. Provider responses and prescreening outcomes were recorded in a REDCap project. To iteratively refine the trial results, matching algorithm updates were deployed throughout the study. Results: In the pilot deployment of this trial, we performed prescreening on 60 patients. At the time of prescreening vital status of 17% of the patients was outdated. We observed that trial cohort related recruiting statuses were the highest contributors to false matches (44%) and provider response rate was 94%. Conclusions: It is not possible to make substantial improvements to the outcome of clinical trial enrollment events without investing in reliable publicly available resources that host updated recruiting status for trials at the arm/cohort level. Uptake of such efforts by NCI or NLM has the potential to radically change the accuracy of clinical trial matching services and thereby improve enrollment efficiencies.[Table: see text]
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