Using machine learning in diagnostic services: a report with recommendations from CQC’s regulatory sandbox

Abstract

This report presents the findings from the Care Quality Commission’s (CQC’s) regulatory sandbox pilot. Regulatory sandboxing is a way of working proactively and collaboratively to understand new types of health and social care service, agree what good quality looks like, and develop our approach to regulation. We think this is particularly important for innovative and technology-enabled services, which are developing quickly, and where a response requires collaboration with other national bodies.

This sandbox round focused on the use of machine learning applications for diagnostic purposes in healthcare services. Part of this work involved building a consensus on what is needed to deliver high-quality care in services that use these applications. To do this, we worked with healthcare providers, technology suppliers, people who use services, clinicians, and other stakeholders. We have used the findings of this sandbox to identify and consider where we need to update our current regulatory methods, and what work we need to do to get this right, which will help us to regulate these services better.

This content is available under the Open Government Licence v3.0

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