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Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study.

Natasha Alexander¹, Catherine Aftandilian², Lin Lawrence Guo³

¹Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada.

JMIR Medical Informatics

|November 17, 2022

Summary

This summary is machine-generated.

Healthcare leaders prioritize machine learning models that improve patient outcomes by addressing common, high-morbidity issues and enabling risk stratification for better clinical actions.

Keywords:

clinical utilization machine learning preferences qualitative interviews

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Area of Science:

Healthcare Informatics
Clinical Decision Support
Machine Learning in Medicine

Background:

Implementing machine learning (ML) in clinical practice involves significant costs.
A systematic approach is needed to prioritize ML model implementation effectively.
Prioritization frameworks can guide resource allocation for ML tools.

Purpose of the Study:

To identify key healthcare attributes for prioritizing ML model implementation in pediatric settings.
To explore perspectives on ML implementation through qualitative interviews.
To establish criteria for selecting high-impact ML applications.

Main Methods:

A mixed-methods study involving surveys and qualitative interviews at two pediatric institutions.
Survey respondents (leaders, physicians, data scientists) ranked implementation attributes.
Attributes included clinical problem prevalence, morbidity/mortality impact, risk stratification utility, workload reduction, and cost savings.

Main Results:

275 of 613 (44.9%) responded to the survey; 17 participated in interviews.
Top-ranked attributes: risk stratification leading to different actions (74.5%) and substantial morbidity/mortality (64.4%).
Reducing physician workload and saving money were least prioritized; patient outcome improvement was consistently favored.

Conclusions:

Prioritization of ML models should focus on those enabling risk stratification for improved actions and addressing severe clinical problems.
The study provides a framework for selecting ML implementations that demonstrably enhance patient outcomes.
Findings can guide healthcare institutions in optimizing ML investments for maximum clinical impact.