The use of data science by healthcare leaders

Reza Salehnejad and Nathan Proudlove

Chapter 38 in Research Handbook on Leadership in Healthcare, 2023, pp 729-751 from Edward Elgar Publishing

Abstract: Both the individual clinical management of a patient and the collective management of patient flows through a healthcare organisation involve decision-making based on judgements and predictions. They generate large and complex datasets. This is a rich area for application of data science, in particular machine learning and artificial intelligence - prediction technologies built on automated learning algorithms. Early applications include automating routine tasks and advising on optimal clinical decisions and patient and resource assignments. However, enabling their huge potential requires complementary developments: in managerial learning about how and where these technologies can be used, in staff training, in data infrastructure and in innovation in regulatory environments to enable faster progress without compromising legitimate concerns about privacy and ethics. The technology is also developing rapidly, including the explicability of machine recommendations, an issue of particular concern in healthcare. However, further developments are also needed in the types of knowledge encoded in models (human knowledge of causation as well as patterns in observational data) and appreciation of the uncertainty in predictions.

Keywords: Business and Management; Economics and Finance; Sociology and Social Policy (search for similar items in EconPapers)
Date: 2023
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