Abstract
In this book chapter we outline some of the emerging issues and considerations that will need to be considered by policy makers, clinicians, healthcare administrators, health technology developers, and implementers, when considering AI’s use in the coming years. In addition to this, the authors propose a new framework for researching and evaluating the introduction of AI into clinical practice settings. We begin by discussing some of the challenges associated with implementing AI in healthcare. These challenges will need to be addressed in the near future as this technology moves towards being more widely used across varying healthcare contexts (e.g. physician office, community, hospital). Lastly, we propose a model for advancing future work in the area of AI in medicine and healthcare as a guide for addressing safety. We begin our chapter by defining AI and AI safety, followed by a review of some of the emerging issues and considerations for AI in healthcare.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barber G (2019) Artificial intelligence confronts a “reproducability” crisis. Wired 2019 Sept 16, 9. https://www.wired.com/story/artificial-intelligence-confronts-reproducibility-crisis/
Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E (2019) Artificial intelligence: a new tool in operating room management. Role of machine learning models in operating room optimization. J Med Syst 44(1):20. https://doi.org/10.1007/s10916-019-1512-1
Borycki E, Keay E (2010) Methods to assess the safety of health information systems. Healthcare quarterly (Toronto, Ont.) 13:47–52
Borycki EM, Kushniruk AW (2008) Where do technology-induced errors come from? Towards a model for conceptualizing and diagnosing errors caused by technology. In: Kushniruk AW, Borycki EM (eds) Human, Social and organizational aspects of health information systems. IGI global, Pennsylvania, pp 148–166
Borycki EM, Kushniruk AW (2010) Towards an integrative cognitive-socio-technical approach in health informatics: analyzing technology-induced error involving health information systems to improve patient safety. Open Med Inform J 15(4):181–187. https://doi.org/10.2174/1874431101004010181
Borycki EM, Kushniruk A, Keay E, Nicoll J, Anderson J, Anderson M (2009) Toward an integrated simulation approach for predicting and preventing technology-induced errors in healthcare: implications for healthcare decision-makers. Healthc Q 12:90–96
Borycki EM, Kushniruk AW, Bellwood P, Brender J (2012) Technology-induced errors. Methods Inf Med 51(02):95–103
Borycki E, Kushniruk A, Nohr C, Takeda H, Kuwata S, Carvalho C, Bainbridge M, Kannry J (2013) Usability methods for ensuring health information technology safety: evidence-based approaches contribution of the IMIA working group health informatics for patient safety. Yearb Med Inform 8:20–7
Borycki E, Dexheimer JW, Cossio CH, Gong Y, Jensen S, Kaipio J, Kennebeck S, Kirkendall E, Kushniruk AW, Kuziemsky C, Marcilly R (2016) Methods for addressing technology-induced errors: the current state. Yearb Med Inform 25(01):30–40
Brownlee J (2019) 14 Different types of learning in machine learning. Start machine learning. https://machinelearningmastery.com/types-of-learning-in-machine-learning/
He J, Baxter SL, Xu J et al (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25:30–36. https://doi.org/10.1038/s41591-018-0307-0
Jiang F, Jiang Y, Zhi Y, Li H, Ma S, Wang Yl, Don Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasular Neurol 2. https://doi.org/10.1136/svn-2017-000101
Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, Strom BL (2005) Role of computerized physician order entry systems in facilitating medication errors. JAMA 293(10):1197–1203
Kushniruk AW, Triola MM, Borycki EM, Stein B, Kannry JL (2005) Technology induced error and usability: the relationship between usability problems and prescription errors when using a handheld application. Int J Med Inform 74(7–8):519–526. Epub 2005 Apr 8
Kushniruk A, Surich J, Borycki E (2012) Detecting and classifying technology-induced error in the transmission of healthcare data. In: 24th international conference of the European federation for medical informatics Europe 2012, 25–30 Aug 2012
Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, Lee EH, Kim EK (2020) Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. The Lancet Digit Health
Magrabi F, Ong MS, Runciman W, Coiera E (2012) Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc 19(1):45–53. https://doi.org/10.1136/amiajnl-2011-000369. Epub 2011 Sep 8
Menke NB, Caputo N, Fraser R, Haber J, Shields C, Menke MN (2014) A retrospective analysis of the utility of an artificial neural network to predict ED volume. Am J Emerg Med 32(6):614–617. https://doi.org/10.1016/j.ajem.2014.03.011. Epub 2014 Mar 19
Matheny ME, Whicher D, Israni ST (2019) Artificial intelligence in health care: a report from the national academy of medicine. JAMA 323(6):509–510
Palojoki S, Mäkelä M, Lehtonen L, Saranto K (2017) An analysis of electronic health record-related patient safety incidents. Health Inform J 23(2):134–145. https://doi.org/10.1177/1460458216631072. Epub 2016 Mar 7
Price WN, Gerke S (2019) Potential liability for physicians using artificial intelligence. JAMA 322(18):1765–1766
Acknowledgements
This book chapter was, in part, supported by a research grant from the Michael Smith Foundation for Health Research, British Columbia, Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Borycki, E.M., Kushniruk, A.W. (2021). The Safety of AI in Healthcare: Emerging Issues and Considerations for Healthcare. In: Househ, M., Borycki, E., Kushniruk, A. (eds) Multiple Perspectives on Artificial Intelligence in Healthcare. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-67303-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-67303-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67302-4
Online ISBN: 978-3-030-67303-1
eBook Packages: MedicineMedicine (R0)