A Path for Translation of Machine Learning Products into Healthcare Delivery

TLDR

Despite enthusiasm, machine learning models are rarely translated into clinical care and evidence of impact is minimal, and although new venues promote research, the translational path remains unclear. The review investigates how EHR‑based machine learning models can be applied to clinical decision support and translated into practice. The authors analyze 21 EHR‑based machine learning products and identify four translation phases—design/development, evaluation/validation, diffusion/scale, and ongoing monitoring/maintenance. The review identifies diverse approaches across the four phases, discusses challenges and opportunities, and offers guidance to researchers, regulators, and health system leaders.

Abstract

Despite enormous enthusiasm, machine learning models are rarely translated into clinical care and there is minimal evidence of clinical or economic impact. New conference venues and academic journals have emerged to promote the proliferating research; however, the translational path remains unclear. This review undertakes the first in-depth study to identify how machine learning models that ingest structured electronic health record data can be applied to clinical decision support tasks and translated into clinical practice. The authors complement their own work with the experience of 21 machine learning products that address problems across clinical domains and across geographic populations. Four phases of translation emerge: design and develop, evaluate and validate, diffuse and scale, and continuing monitoring and maintenance. The review highlights the varying approaches taken across each phase by teams building machine learning products and presents a discussion of challenges and opportunities. The translational path and associated findings are instructive to researchers and developers building machine learning products, policy makers regulating machine learning products, and health system leaders who are considering adopting a machine learning product.