Critical Intelligence Unit

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

Automating routine indirect clinical tasks

Limited but emerging positive evidence that generative AI can automate tasks and free up clinician time:

  • up to 10% of health   providers’ time is spent on administrative tasks that could be automated. The   potential for AI rationalise processes and automate data entry is substantial1-3
  • in a hospital which moved to completely digital   systems and used AI to automate as much as possible, the amount of time nurses   spent with patients increased by a factor of two4
  • large   language models can outperform medical experts in analysing large amounts of   patient clinical documentation and summarising it5
  • large language models can draft replies to patient emails, resulting   in staff adoption of these systems and significant reductions in staff burden   and burnout scores6
  • generative   AI can now transcribe patient consults then summarise these into clinical   notes
    • this   technology is commercially available7, 8
    • pilot   studies have found clinicians spend up to 66% less time on report writing and   working within the electronic health record(EHR).9, 10

Expert commentary and analysis notes:

  • roughly 44% of   administrative tasks carried out by staff in general practice are ‘mostly’ or   ‘completely’ automatable using currently available technology.11
  • Some hospitals have started trialling generative AI to write clinical documentation, although clinician checks of the documentation are still used.12

Reducing errors via double checking mechanisms and risk alerts

Limited but emerging positive evidence that AI can support drug decisions and dispensing processes13and reduce near miss events including14:

  • reduce prescription errors15
  • identify harmful drug interactions15, 16
  • identify harmful drug-food interactions16
  • support drug dosing decisions for high-risk drugs15

Limited but emerging positive evidence that AI can support proactive identification of patients at risk of:

  • falls17 (and can lead to associated cost   savings18)
  • healthcare associated infections19
  • a range of adverse events (all-cause mortality,   cardiac arrest, transfer to intensive care, and evaluation by the rapid   response team in practice)20
  • A US paediatric hospital uses safety risk software to track when patients are on three or more medications that could be toxic to their kidneys, in order to alert staff.21
  • Several hospitals in the US use an AI-driven falls risk calculator which is linked to a patient’s EMR and prompts a personalised falls prevention plan.17, 21

Quality and process improvement

Limited but emerging positive evidence that AI can evaluate clinician skill:

  • machine learning models can outperform humans at   evaluating neurosurgical methods and skill. This has implications for   surgical training and improvement methods and may reduce the senior personnel   hours required for supervision and training.22, 23
  • Mount Sinai has installed an AI platform that live records videos of surgery, adds automatic annotations and syncs these files to the patient EHR and clinical outcomes, for later evaluation and in order to promote surgical quality improvement.24
Patient reported outcome data

Limited but emerging positive evidence that AI can evaluate patient reported data:

  • natural language   processing can analyse unstructured patient-reported outcome data to support risk   stratification and adverse clinical outcomes in oncology25
 

Analysis and automation of patient flow

Limited but emerging positive evidence that AI can predict and improve patient flow:

  • in inpatient settings   including improving bed capacity, and achievement of discharge targets264, 27
  • in outpatient   settings28
  • in surgical   management to predict surgery length, recovery ward length of stay or to   predict surgery cancellation risks29
  • Three hospitals under Alfred Health in Western Australia have deployed an AI system that acts as a centralised ‘command centre’ to manage patient flow across their wards.30

AI analysis of system resource allocation

Limited but emerging positive evidence that AI can suggest improvements in system organisation and resourcing:

  • during major emergencies:
    • allocating emergency services31
    • determining the optimum plan for resource allocation across multiple hospitals32
  • An NHS trust is piloting AI software to reduce missed hospital appointments. The software is projected to allow an additional 80-100,000 patients to be seen each year.33
  • AI-driven resource allocation has been used by NGOs to assess diagnostic testing needs and simplify transport logistics.34

Automation of administrative tasks (aka “back office” functions)35

  • coding
  • scheduling and patient reminders
  • billing
  • identifying fraud

AI adoption is expected to be rapid - administrative AI don’t require clinical or regulatory approval (like clinical AI does).36

Limited but emerging positive evidence that AI can improve scheduling:

  • including much faster creation of staff rosters which are fairer, include multiple combinations for redundancy and reduce staff burnout.37

Expert commentary and analysis:

  • The McKinsey Global Institute suggests that routine administrative tasks can take up to 70% of a healthcare practitioner’s time.26
  • A hospital in Kansas City now uses AI software to automate scheduling and workflow in order to maximise operating theatre time and resources. The hospital  has been able to do 7% more surgical cases despite having to close 20% of its ORs at times.38

References

  1. Sutherland E. Artificial intelligence in health: big opportunities, big risks. Paris: OECD; 2023 [cited 12 Feb 2024]. Available from: https://oecd.ai/en/wonk/artificial-intelligence-in-health-big-opportunities-big-risks
  2. Vize R. More Time to Care: Automation, Digitisation and the Workforce. Beamtree; 2023 [cited 12 Feb 2024]. Available from: https://beamtree.com.au/papers-publications/more-time-to-care/
  3. Canadian Federation of Independent Business. Patients before Paperwork. Toronto: Canadian Federation of Independent Business; 2023 [cited 12 Feb 2024]. Available from: https://www.cfib-fcei.ca/en/research-economic-analysis/patients-before-paperwork
  4. Dicuonzo G, Donofrio F, Fusco A, et al. Healthcare system: Moving forward with artificial intelligence. Technovation. 2023 2023/02/01/;120:102510. DOI: https://doi.org/10.1016/j.technovation.2022.102510
  5. Van Veen D, Van Uden C, Blankemeier L, et al. Adapted large language models can outperform medical experts in clinical text summarization. Nature Medicine. 2024 2024/02/27. DOI: 10.1038/s41591-024-02855-5
  6. Garcia P, Ma SP, Shah S, et al. Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages. JAMA Network Open. 2024;7(3):e243201-e. DOI: 10.1001/jamanetworkopen.2024.3201
  7. Amazon.com Inc. AWS announces AWS HealthScribe, a new generative AI-powered service that automatically creates clinical documentation. New York: Amazon.com Inc,; 2023 [cited 25 Oct 2023]. Available from: https://press.aboutamazon.com/2023/7/aws-announces-aws-healthscribe-a-new-generative-ai-powered-service-that-automatically-creates-clinical-documentation#:~:text=With%20generative%20AI%20capabilities%20powered,to%20enter%20into%20the%20EHR.
  8. Pifer R. Amazon launches generative AI-based clinical documentation service. Washington, DC: Healthcare Dive; 2023 [cited 25 Oct 2023]. Available from: https://www.healthcaredive.com/news/amazon-generative-ai-clinical-documentation-healthscribe/688996/
  9. MIT Technology Review Insights. The AI Effect: How artificial intelligence is making health care more human. Cambridge, MA: MIT; 2019 [cited 14 Nov 2023]. Available from: https://www.gehealthcare.co.uk/-/jssmedia/61b7b6b1adc740e58d4b86eef1bb6604.pdf
  10. Tierney AA, Gayre G, Hoberman B, et al. Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation. Catalyst non-issue content. 2024;5(1):CAT.23.0404. DOI: doi:10.1056/CAT.23.0404
  11. 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
  12. Siwicki B. In pilot, generative AI expected to reduce clinical documentation time at Baptist Health. Healthcare IT News; 2023 [cited 25 Oct 2023]. Available from: https://www.healthcareitnews.com/news/generative-ai-reduces-clinical-documentation-time-baptist-health
  13. Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform. 2020 2020/7/24;8(7):e18599. DOI: 10.2196/18599
  14. Pais C, Liu J, Voigt R, et al. Large language models for preventing medication direction errors in online pharmacies. Nature Medicine. 2024 2024/04/25. DOI: 10.1038/s41591-024-02933-8
  15. Chalasani SH, Syed J, Ramesh M, et al. Artificial intelligence in the field of pharmacy practice: A literature review. Exploratory Research in Clinical and Social Pharmacy. 2023 2023/12/01/;12:100346. DOI: https://doi.org/10.1016/j.rcsop.2023.100346
  16. Davenport L. AI Tool Reveals MS Drug Interactions, Offers Safer Options. Newark, NJ: Medscape Medical News; 2023 [cited 27 Nov 2023]. Available from: https://www.medscape.com/viewarticle/997331?ecd=mkm_ret_231126_mscpmrk-OUS_InFocus_etid6087426&uac=181280EV&impID=6087426
  17. Dykes PC, Adelman JS, Alfieri L, et al. The Fall TIPS (Tailoring Interventions for Patient Safety) Program: A Collaboration to End the Persistent Problem of Patient Falls. Nurse Leader. 2019 2019/08/01/;17(4):365-70. DOI: https://doi.org/10.1016/j.mnl.2018.11.006
  18. Dykes PC, Curtin-Bowen M, Lipsitz S, et al. Cost of Inpatient Falls and Cost-Benefit Analysis of Implementation of an Evidence-Based Fall Prevention Program. JAMA Health Forum. 2023;4(1):e225125-e. DOI: 10.1001/jamahealthforum.2022.5125
  19. 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 2020/08/01/;13(8):1061-77. DOI: https://doi.org/10.1016/j.jiph.2020.06.006
  20. 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
  21. 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
  22. Titov O, Bykanov A, Pitskhelauri D. Neurosurgical skills analysis by machine learning models: systematic review. Neurosurgical Review. 2023 2023/05/16;46(1):121. DOI: 10.1007/s10143-023-02028-x
  23. Lavanchy JL, Zindel J, Kirtac K, et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Scientific Reports. 2021 2021/03/04;11(1):5197. DOI: 10.1038/s41598-021-84295-6
  24. Siwicki B. AI helps Mount Sinai organize and unlock largely untapped surgical data. Chicago, IL: Healthcare IT News; 2023 [cited 14 Nov 2023]. Available from: https://www.healthcareitnews.com/news/ai-helps-mount-sinai-organize-and-unlock-largely-untapped-surgical-data
  25. Sim J-A, Huang X, Horan MR, et al. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Review of Pharmacoeconomics & Outcomes Research. 2024 2024/04/20;24(4):467-75. DOI: 10.1080/14737167.2024.2322664
  26. Spatharou A, Hieronimus S, Jenkins J. Transforming healthcare with AI: The impact on the workforce and organizations. New York: McKinsey & Company,; 2020 [cited 17 Jul 2023]. Available from: https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai
  27. Jiandong Z, Andrew JB, David AC, et al. Improving patient flow through hospitals with machine learning based discharge prediction. medRxiv. 2023:2023.05.02.23289403. DOI: 10.1101/2023.05.02.23289403
  28. Li L, Diouf F, Gorkhali A. Managing outpatient flow via an artificial intelligence enabled solution. Systems Research and Behavioral Science. 2022 2022/05/01;39(3):415-27. DOI: https://doi.org/10.1002/sres.2870
  29. Bellini V, Russo M, Domenichetti T, et al. Artificial Intelligence in Operating Room Management. Journal of Medical Systems. 2024 2024/02/14;48(1):19. DOI: 10.1007/s10916-024-02038-2
  30. Hospital and Healthcare. Alfred Health rolls out new solutions to support patient flow. Online: Hospital and Healthcare; 2023 [cited 14 Nov 2023]. Available from: https://www.hospitalhealth.com.au/content/technology/news/alfred-health-rolls-out-new-solutions-to-support-patient-flow-826496502
  31. Du L. Medical Emergency Resource Allocation Model in Large-Scale Emergencies Based on Artificial Intelligence: Algorithm Development. JMIR Med Inform. 2020 2020/6/25;8(6):e19202. DOI: 10.2196/19202
  32. Huang C-H, Batarseh FA, Boueiz A, et al. Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management. Data & Policy. 2021;3:e30. DOI: 10.1017/dap.2021.29
  33. NHS England. NHS pilots artificial intelligence software to cut missed hospital appointments. London: NHS; 2023 [cited 15 Nov 2023]. Available from: https://www.england.nhs.uk/2023/02/nhs-pilots-artificial-untelligence-software-to-cut-missed-hospital-appointments/
  34. Purcell R. Coupa and FIND Recognized by Fast Company for a World Changing Idea. San Mateo, CA: Coupa; 2022 [cited 14 Nov 2023]. Available from: https://www.coupa.com/blog/coupa-news-culture/coupa-and-find-recognized-fast-company-for-world-changing-idea
  35. Organisation for Economic Co-operation and Development (OECD). Trustworthy AI in health. Paris: OECD; 2020 [cited 17 Jul 2023]. Available from: https://www.oecd.org/health/trustworthy-artificial-intelligence-in-health.pdf
  36. Davenport T, Bean R. Clinical AI Gets the Headlines, but Administrative AI May Be a Better Bet. Cambridge, MA: MIT Sloan Management Review; 2022 [cited 20 Nov 2023]. Available from: https://sloanreview.mit.edu/article/clinical-ai-gets-the-headlines-but-administrative-ai-may-be-a-better-bet/
  37. American Society of Anesthesiologists (ASA). Using AI to create work schedules significantly reduces physician burnout, study shows. Schaumburg, IL ASA; 2022 [cited 15 Nov 2023]. Available from: https://www.asahq.org/about-asa/newsroom/news-releases/2022/01/using-ai-to-create-work-schedules-significantly-reduces-physician-burnout
  38. Siwicki B. AI-powered OR scheduling tech brings big efficiencies for St. Luke's. Online: Healthcare IT News; 2022 [cited 20 Nov 2023]. Available from: https://www.healthcareitnews.com/news/ai-powered-or-scheduling-tech-brings-big-efficiencies-st-lukes

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 6 May 2024

Back to top