A roadmap to implementing machine learning in healthcare: from concept to practice.

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2025-01-20 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1462751

Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Bren Cardiff, Lillian Sung

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Abstract

Background: The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.

Objective: To provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.

Materials and methods: We present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.

Results: We show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.

Discussion: These approaches will require refinement over time as the number of deployments and experience increase.

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