Accepted for/Published in: JMIR Research Protocols
Date Submitted: Apr 6, 2023
Date Accepted: Jul 17, 2023
Implementation of Machine Learning Applications in Healthcare Organizations: Protocol for a Systematic Review of Empirical Studies
ABSTRACT
Background:
An increasing interest in machine learning (ML) has been observed amongst scholars and healthcare professionals. However, while ML algorithms have been shown to be effective and have the potential to change the delivery of patient care, their implementation in healthcare organizations is complex, and several are the challenges that currently hamper their uptake in the daily practice.
Objective:
The aim of this systematic review is twofold: (1) to map the ML-based technologies implemented in healthcare organizations, with a focus on investigating the organizational dimensions that are relevant in the implementation process; (2) to analyze the determinants of successful uptake of ML, identifying implementation barriers and/or facilitators to these technologies.
Methods:
This protocol has been developed following the PRISMA-P guidelines. The search is conducted on three databases (PubMed, Scopus, and Web of Science), considering a 10-year timeframe (2013-2023). The search strategy is built around four blocks of keywords (AI, implementation, health care, study type). Detailed inclusion criteria have been defined, and only empirical studies documenting the implementation of learning-based algorithms used by healthcare professionals in clinical settings will be considered. The study protocol was registered in PROSPERO).
Results:
The review is ongoing and is expected to be completed by June 2023. Preliminary findings highlight the need to further elaborate on the determinants of successful implementation of ML technologies, to provide the management of healthcare organizations with viable indications on how to arrive to a daily use of proven health interventions.
Conclusions:
ML algorithms involving clinical decision support and automation of clinical tasks present unique traits that add several layers of complexity compared to earlier health technologies. Our review aims at contributing to the existing literature by investigating the implementation of ML-technologies from an organizational perspective, and by systematizing a conspicuous amount of information on factors influencing implementation.
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