Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data

Abstract

ObjectiveWide-scale adoption of electronic medical records (EMRs) has created an unprecedented opportunity for the implementation of Rapid Learning Systems (RLSs) that leverage primary clinical data for real-time decision support. In cancer, where large variations among patient features leave gaps in traditional forms of medical evidence, the potential impact of a RLS is particularly promising. We developed the Melanoma Rapid Learning Utility (MRLU), a component of the RLS, providing an analytical engine and user interface that enables physicians to gain clinical insights by rapidly identifying and analyzing cohorts of patients similar to their own. Materials and methodsA new approach for clinical decision support in Melanoma was developed and implemented, in which patient-centered cohorts are generated from practice-based evidence and used to power on-the-fly stratified survival analyses. A database to underlie the system was generated from clinical, pharmaceutical, and molecular data from 237 patients with metastatic melanoma from two academic medical centers. The system was assessed in two ways: (1) ability to rediscover known knowledge and (2) potential clinical utility and usability through a user study of 13 practicing oncologists. ResultsThe MRLU enables physician-driven cohort selection and stratified survival analysis. The system successfully identified several known clinical trends in melanoma, including frequency of BRAF mutations, survival rate of patients with BRAF mutant tumors in response to BRAF inhibitor therapy, and sex-based trends in prevalence and survival. Surveyed physician users expressed great interest in using such on-the-fly evidence systems in practice (mean response from relevant survey questions 4.54/5.0), and generally found the MRLU in particular to be both useful (mean score 4.2/5.0) and useable (4.42/5.0). DiscussionThe MRLU is an RLS analytical engine and user interface for Melanoma treatment planning that presents design principles useful in building RLSs. Further research is necessary to evaluate when and how to best use this functionality within the EMR clinical workflow for guiding clinical decision making. ConclusionThe MRLU is an important component in building a RLS for data driven precision medicine in Melanoma treatment that could be generalized to other clinical disorders.