Machine learning for enhanced healthcare: an overview for operational and clinical leads. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Roberts, Luke; Dhanoa, Harpreet; Lanes, Sadie; Holdship, Jonathan
• Is Part Of: British journal of healthcare management. Volume 29:Issue 1(2023)
• Page Start: 12
• Page End: 19
• Publication Date: 2023-01-02
• Subjects: Artificial intelligence -- Machine learning -- Quality improvement -- Technological innovations; Health services administration -- Great Britain -- Periodicals; 362.1068
• DOI: 10.12968/bjhc.2022.0096
• Languages: English
• ISSNs: 1358-0574

Abstract

Abstract : Machine learning has the potential to transform how healthcare is delivered. It can support clinical decision making, determine the risk, presence and prognosis of disease and help optimise patient pathways. Widespread use and access to digital health records mean implementing machine learning models is quicker and easier than ever before. It is imperative for clinical and operational leads to understand the principles behind machine learning, so they can evaluate how it may be helpful to them and their teams. This article provides an overview of machine learning and how it can be used to help solve common healthcare-related problems.