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

Journal of Biomedical Informatics
Samuel G FinlaysonDaniel L Rubin

Abstract

Wide-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. A 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. The MRLU enables ph...Continue Reading

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Nov 20, 2016·Journal of Pediatric Oncology Nursing : Official Journal of the Association of Pediatric Oncology Nurses·Lauri A LinderAndrew Wilson
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