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CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 115-126
DOI: 10.1055/s-0043-1768733
Section 3: Clinical Information Systems
Survey

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

Farah Magrabi
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
,
David Lyell
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
,
Enrico Coiera
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
› Author Affiliations Funding This research is supported by the Australian National Health and Medical Research Council Centre for Research Excellence in Digital Health (APP1134919) and Macquarie University. The funding sources did not play any role in study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the article for publication.
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Summary

Aims and objectives: To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings.

Method: PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised.

Results: AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making.

Conclusion: AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.

Keywords

Clinical information systems - artificial intelligence - automation - implementation - evaluation

Competing Interests

The authors have no competing interests to declare.


Contributions

FM conceptualised the study, undertook the literature search, performed data analysis and drafted the article. All authors participated in writing and revising the article. All aspects of the study (including design; collection, analysis, and interpretation of data; writing of the report; and decision to publish) were led by the authors.


Supplementary Material



Publication History

Article published online:
26 December 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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