Debunking the myth of industry 4.0 in health care: insights from a systematic literature review

Abstract

Purpose

Industry 4.0, artificial intelligence and digitalization have got a momentum in health care. However, scholars and practitioners do not agree on their implications on health services' quality and effectiveness. The article aims at shedding light on the applications, aftermaths and drawbacks of industry 4.0 in health care, summarizing the state of the art.

Design/methodology/approach

A systematic literature review was undertaken. We arranged an ad hoc research design, which was tailored to the study purposes. Three citation databases were queried. We collected 1,194 scientific papers which were carefully considered for inclusion in this systematic literature review. After three rounds of analysis, 40 papers were taken into consideration.

Findings

Industry 4.0, artificial intelligence and digitalization are revolutionizing the design and the delivery of care. They are expected to enhance health services' quality and effectiveness, paving the way for more direct patient–provider relationships. In addition, they have been argued to allow a more appropriate use of available resources. There is a dark side of health care 4.0 involving both management and ethical issues.

Research limitations/implications

Industry 4.0 in health care should not be conceived as a self-nourishing innovation; rather, it needs to be carefully steered at both the policy and management levels. On the one hand, comprehensive governance models are required to realize the full potential of health 4.0. On the other hand, the drawbacks of industry 4.0 should be timely recognized and thoroughly addressed.

Originality/value

The article contextualizes the state of the art of industry 4.0 in the health care context, providing some insights for further conceptual and empirical developments.

Citation

Cavallone, M. and Palumbo, R. (2020), "Debunking the myth of industry 4.0 in health care: insights from a systematic literature review", The TQM Journal, Vol. 32 No. 4, pp. 849-868. https://doi.org/10.1108/TQM-10-2019-0245