AI: automating indirect clinical tasks and administration: living evidence
Living evidence tables provide high level summaries of key studies and evidence on a particular topic, and links to sources. They are reviewed regularly and updated as new evidence and information is published.
This page is part of the Artificial Intelligence series.
This table describes areas where artificial intelligence is being used for indirect clinical care and administration in healthcare systems. Evidence includes examples of established tools and pilot studies, as well as expert commentary in cases where robust evidence is not yet available.
Regular checks are conducted for new content and any updates are highlighted.
To develop this living table:
- Systematic reviews on artificial intelligence published from 2021 to May 2023 were screened in PubMed.
- Since May 2023, a weekly PubMed search on artificial intelligence has been conducted to continually update the table.
- Articles are screened for those that describe established uses of AI in indirect clinical care and administration, pilot studies of implementation, or expert commentary.
- Grey literature sources are identified via Google or through publications and websites of well-known organisations with relevance to the Australian clinical context (e.g. World Health Organization, OECD, NICE, The King’s Fund, US Center for Disease Control and Prevention, etc).
Best bet – how can AI help | Evidence and expert commentary | Examples of adoption in healthcare systems |
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Automating routine indirect clinical tasks | Limited but emerging positive evidence that generative AI can automate tasks and free up clinician time:
Expert commentary and analysis notes:
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Reducing errors via double checking mechanisms and risk alerts. | Limited but emerging positive evidence that AI can support drug decisions and dispensing processes16and reduce near miss events including:17-19
Limited but emerging positive evidence that AI can support proactive identification of patients at risk of:
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Quality and process improvement | Limited but emerging positive evidence that AI can speed up and improve workflow:
Limited but emerging positive evidence that AI can evaluate clinician skill:
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Patient reported outcome data | Limited but emerging positive evidence that AI can evaluate patient reported data:
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Analysis and automation of patient triage and flow | Limited but emerging positive evidence that AI can automate patient prioritisation:
Limited but emerging positive evidence that AI can predict and improve patient flow:34
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AI analysis of system resource allocation | Limited but emerging positive evidence that AI can suggest improvements in system organisation and resourcing:
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Automation of administrative tasks (aka “back office” functions)44
AI adoption is expected to be rapid - administrative AI doesn’t require clinical or regulatory approval (like clinical AI does).45 | Limited but emerging positive evidence that AI can improve scheduling:
Expert commentary and analysis:
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References
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- Willis M, Duckworth P, Coulter A, et al. Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice. BMJ Open. 2020;10(6):e032412. DOI: 10.1136/bmjopen-2019-032412
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- Buijs E, Maggioni E, Mazziotta F, et al. Clinical impact of AI in radiology department management: a systematic review. La radiologia medica. 2024;129(11):1656-66. DOI: 10.1007/s11547-024-01880-1
- Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform. 2020;8(7):e18599. DOI: 10.2196/18599
- Pais C, Liu J, Voigt R, et al. Large language models for preventing medication direction errors in online pharmacies. Nature Medicine. 2024. DOI: 10.1038/s41591-024-02933-8
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- Johns E, Alkanj A, Beck M, et al. Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review. European Journal of Hospital Pharmacy. 2024;31(4):289-94. DOI: 10.1136/ejhpharm-2023-003857
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- Scardoni A, Balzarini F, Signorelli C, et al. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature. Journal of Infection and Public Health. 2020;13(8):1061-77. DOI: https://doi.org/10.1016/j.jiph.2020.06.006
- Salehinejad H, Meehan AM, Rahman PA, et al. Novel machine learning model to improve performance of an early warning system in hospitalized patients: a retrospective multisite cross-validation study. eClinicalMedicine. DOI: 10.1016/j.eclinm.2023.102312
- Landro L. How 4 hospitals are using technology to reduce medical errors. Online: Advisory Board; 2023 [cited 14 Nov 2023]. Available from: https://www.advisory.com/daily-briefing/2023/03/15/medical-errors
- Wan P, Huang Z, Tang W, et al. Outpatient reception via collaboration between nurses and a large language model: a randomized controlled trial. Nature Medicine. 2024. DOI: 10.1038/s41591-024-03148-7
- Titov O, Bykanov A, Pitskhelauri D. Neurosurgical skills analysis by machine learning models: systematic review. Neurosurgical Review. 2023;46(1):121. DOI: 10.1007/s10143-023-02028-x
- Lavanchy JL, Zindel J, Kirtac K, et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Scientific Reports. 2021;11(1):5197. DOI: 10.1038/s41598-021-84295-6
- Yilmaz R, Bakhaidar M, Alsayegh A, et al. Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial. Scientific Reports. 2024;14(1):15130. DOI: 10.1038/s41598-024-65716-8
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- Henderson AP, Van Schuyver PR, Economopoulos KJ, et al. The Use of Artificial Intelligence for Orthopedic Surgical Backlogs Such as the One Following the COVID-19 Pandemic: A Narrative Review. JBJS Open Access. 2024;9(3):e24.00100. DOI: 10.2106/jbjs.Oa.24.00100
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Living evidence tables include some links to low quality sources and an assessment of the original source has not been undertaken. Sources are monitored regularly but due to rapidly emerging information, tables may not always reflect the most current evidence. The tables are not peer reviewed, and inclusion does not imply official recommendation nor endorsement of NSW Health.
Last updated on 3 Dec 2024