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

Abstract

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.

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

“…Using the infrastructure of electronic health records for clinical DSS could support personalized medicine practices by providing the tailored information at the right time. Various researchers have discussed the advantages of incorporating DSS in electronic health records in combination with smart algorithms, such that continuous updating and rapid adaptation of DSS are ensured [11,[24][25][26][27]. This precision oncology should be a multidisciplinary effort incorporating information from various experts and stakeholders [24,25].…”

The three numbers you need to know about healthcare: the 60-30-10 Challenge

“…How then, can we use this knowledge and evidence from other examples [35][36][37] to break the impasse to achieve better, more cost-effective, and safer care? The 60-30-10 Challenge is ample warning against trudging along the same well-worn tracks to disappointment.…”

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider

“…The differences between the latter classes of interventions and those used in precision medicine should not be ignored, as they have potentially far-reaching implications for the ethical justifiability and societal acceptability of learning healthcare systems in precision medicine. A large proportion of precision medicine is aimed either at developing novel therapeutics that are biologically designed to target specific biomarkers or at identifying biomarkers to more accurately select which subsets of patients may benefit from newly designed therapeutics [33][34][35]. Drugs used in precision medicine, and the biomarkers on the basis of which these drugs are prescribed, are oftentimes relatively novel.…”