Objective
Wide-scale adoption of electronic medical records (EMRs) has created an
unprecedented opportunity for the implementation of Rapid Learning Systems (RLS) 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 Methods
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.
Results
The 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).
Discussion
The 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.
Conclusion
The 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.