Implementing machine learning in healthcare

Abstract:

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 Improvement developed by the Institute for Healthcare Improvement 8 and the Knowledge to Action9 framework. However, there is no clear, comprehensive framework specifically focused on adoption of machine-learned tools in health care. We propose a 3-phase framework to develop and implement machine-learned solutions in clinical care, illustrated by a case example (Box 1). The framework comprises an exploration phase, a solution design phase, and an implementation and evaluation phase (Figure 1). It can be used for a range of solutions, including computer vision–based projects, automation and optimization projects, and predictive analytics. The framework can also be applied when organizations are implementing machine-learned solutions that were developed elsewhere because the steps, other than model development, remain the same.