Machine learning has the potential to transform health care, although its current application to routine clinical practice has been limited.
Multidisciplinary partnership between technical experts and end-users, including clinicians, administrators, and patients and their families, is essential to developing and implementing machine-learned solutions in health care.
A 3-phase framework can be used to describe the development and adoption of machine-learned solutions: an exploration phase to understand the problem being addressed and the deployment environment, a solution design phase for the development of machine-learned models and user-friendly tools, and an implementation and evaluation phase to deploy and assess the impact of the machine-learned solution.
Machine learning — the process of developing systems that learn from data to recognize patterns and make accurate predictions of future events1 — has considerable potential to transform health care. Machine-learned tools could support complex clinical decision-making and could automate many of the mundane tasks that may waste clinician time and lead to work dissatisfaction. 2 Despite growing interest in and regulatory approval of such technologies, for example smartwatch algorithms to detect atrial fibrillation,3 to date machine-learned tools have had only limited use in routine clinical practice.4 Developing and implementing machine-learned tools in medicine requires infrastructure and resources that can be difficult to access, such as large, real-time clinical data sets, technical skills in data science, computing power and clinical informatics infrastructure. Other barriers to adoption include challenges in ensuring data security and privacy, poorly performing mathematical models, difficulty integrating tools into existing workflows, low acceptance of machine-learned solutions by clinician users, and uncertainty about how to evaluate them.4 In this article we outline an approach to developing and adopting machine-learned solutions in health care. Related articles discuss some of the caveats of using this technology5 and the evaluation of machine-learned tools.6
Developing machine-learned solutions for clinical use requires a strong understanding of clinical care, data science and implementation science. A number of excellent frameworks support data analytics and quality-improvement initiatives, including the Cross-Industry Standard Process for Data Mining (CRISP-DM),7 the Model for…
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