Design and Implementation of a Community Driven Clinical Trial Information Management System for Precision Oncology Settings.

ABSTRACT

INTRODUCTION: Trial recruitment is a crucial factor for precision oncology, potentially improving patient outcomes and generating new scientific evidence. To identify suitable, biomarker-based trials for patients' clinicians need to screen multiple clinical trial registries which lack support for modern trial designs and offer only limited options to filter for in- and exclusion criteria. Several registries provide trial information but are limited regarding factors like timeliness, quality of information and capability for semantic, terminology enhanced searching for aspects like specific inclusion criteria. METHODS: We specified a Fast Healthcare Interoperable Resources (FHIR) Implementation Guide (IG) to represent clinical trials and their meta data. We embedded it into a community driven approach to maintain clinical trial data, which is fed by openly available data sources and later annotated by platform users. A governance model was developed to manage community contributions and responsibilities. RESULTS: We implemented Community Annotated Trial Search (CATS), an interactive platform for clinical trials for the scientific community with an open and interoperable information model. It provides a base to collaboratively annotate clinical trials and serves as a comprehensive information source for community members. Its terminology driven annotations are coined towards precision oncology, but its principles can be transferred to other contexts. CONCLUSION: It is possible to use the FHIR standard and an open-source information model represented in our IG to build an open, interoperable clinical trial register. Advanced features like user suggestions and audit trails of individual resource fields could be represented by extending the FHIR standard. CATS is the first implementation of an open-for-collaboration clinical trial registry with modern oncological trial designs and machine-to-machine communication in mind and its methodology could be extended to other medical fields besides precision oncology. Due to its well-defined interfaces, it has the potential to provide automated patient recruitment decision support for precision oncology trials in digital applications.