Skip to main content
SpringerLink
Log in

AIM and Patient Safety

  • Living reference work entry
  • First Online:
Artificial Intelligence in Medicine

Abstract

Patient safety has constituted a huge public health concern for a long period of time. The focus of safety in the healthcare context is around reducing preventable harms, such as medical errors and treatment-related injuries. COVID-19 pandemic, if anything, has act as a wake-up call for health experts to address latent safety problems. Advancements in the field of artificial intelligence have highlighted the use of intelligent systems as a proven means of improving patient safety and enhancing quality of care.

This chapter explores trends in quality and safety research, the use of machine learning and natural language processing in the context of improving patient safety and outcomes, the use of patient safety databases as a source of data for machine learning, and the future of artificial intelligence in quality and safety.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. World Health Organization. Regional strategy for patient safety in the WHO South-East Asia Region (2016–2025). WHO Regional Office for South-East Asia; 2015.

    Google Scholar 

  2. Grober ED, Bohnen JMA. Defining medical error. Can J Surg. 2005;48(1):39–44.

    PubMed  PubMed Central  Google Scholar 

  3. Wallis KA. Learning from no-fault treatment injury claims to improve the safety of older patients. Ann Fam Med. 2015;13(5):472–4.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shojania KG, Duncan BW, McDonald KM, Wachter RM, Markowitz AJ. Making Healthcare Safer: a critical analysis of patient safety practices. Rockville: Agency for Healthcare Research and Quality; 2001. Evidence Report/Technology Assessment No. 43; AHRQ publication 01-E058.

    Google Scholar 

  5. Newman-Toker DE, Pronovost PJ. Diagnostic errors-the next frontier for patient safety. JAMA. 2009;301(10):1060–2.

    Article  CAS  PubMed  Google Scholar 

  6. Macrae C. Governing the safety of artificial intelligence in healthcare. BMJ Qual Saf. 2019;28(6):495–8.

    Article  PubMed  Google Scholar 

  7. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24:1716–20.

    Article  CAS  PubMed  Google Scholar 

  8. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94.

    Article  CAS  PubMed  Google Scholar 

  9. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.

    Article  CAS  PubMed  Google Scholar 

  10. Tanner C, Gans D, White J, Nath R, Pohl J. Electronic health records and patient safety: co-occurrence of early EHR implementation with patient safety practices in primary care settings. Appl Clin Inform. 2015;6(1):136–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. ACS NSQIP [Internet]. American College of Surgeons. 2020 [cited 16 November 2020]. https://www.facs.org/quality-programs/acs-nsqip/about

  12. Neves A, Freise L, Laranjo L, Carter A, Darzi A, Mayer E. Impact of providing patients access to electronic health records on quality and safety of care: a systematic review and meta-analysis. BMJ Qual Saf. 2020;29:1019–32.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bell SK, Delbanco T, Elmore J, Fitzgerald P, Fossa A, Harcourt K et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw Open; 2020; 3(6).

    Google Scholar 

  14. Parkinson B, Meacock R, Checkland K, Sutton M. How sensitive are avoidable emergency department attendances to primary care quality? Retrospective observational study. BMJ Qual Saf. 2020;0:1–9.

    CAS  Google Scholar 

  15. Leape LL. Reporting of adverse events. N Engl J Med. 2002;347(20):1633–8.

    Article  PubMed  Google Scholar 

  16. ANZACS-QI (Registry) | Enigma Solutions Ltd [Internet]. Enigma.co.nz. 2020 [cited 16 November 2020]. https://www.enigma.co.nz/predict-medical/anzacs-qi/

  17. Kerr AJ, Lee M, Jiang Y, Grey C, Wells S, Williams M, Jackson R, Poppe K. High level of capture of coronary intervention and associated acute coronary syndromes in the all New Zealand acute coronary syndrome quality improvement cardiac registry and excellent agreement with national administrative datasets (ANZACS-QI 25). N Z Med J. 2019;132(1492):19–29.

    PubMed  Google Scholar 

  18. Stewart R. Comparison of high and low oxygen protocol in patients with suspected ACS. Presentation presented at; 2019; ESC Congress.

    Google Scholar 

  19. My audits [Internet]. 2020 [cited 16 November 2020]. https://www.surgeons.org/en/research-audit/my-audits

  20. Finlayson SR. Assessing and improving the quality of surgical care in rural America. Surg Clin North Am. 2009;89(6):1373–81.

    Article  PubMed  Google Scholar 

  21. Anderson KT, Appelbaum R, Bartz-Kurycki MA, Tsao K, Browne M. Advances in perioperative quality and safety. Semin Pediatr Surg. 2018;27(2):92–101.

    Article  PubMed  Google Scholar 

  22. Reason J. Human error: models and management. BMJ. 2000;320(7237):768–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Braithwaite J, Donaldson L. Patient safety and quality. In: Ferlie E, Montgomery K, Pederson AR, editors. The Oxford handbook of health care management. Oxford, UK: Oxford University Press; 2016. p. 325–51.

    Google Scholar 

  24. Braithwaite J, Wears RL, Hollnagel E. Resilient health care: turning patient safety on its head. Int J Qual Health Care. 2015;27(5):418–20.

    Article  PubMed  Google Scholar 

  25. McCarthy J, Hayes P. Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer B, Michie D, editors. Machine Intelligence 4. Edinburgh: Edinburgh University Press; 1969. p. 463–502.

    Google Scholar 

  26. World Health Organization. World alliance for patient safety: WHO draft guidelines for adverse event reporting and learning systems: from information to action. World Health Organization; 2005. https://apps.who.int/iris/handle/10665/69797

  27. Report by the Director General – A72/26. Patient safety – global action on patient safety. Geneva: World Health Organization; 2019. https://apps.who.int/gb/ebwha/pdf_files/WHA72/A72_26-en.pdf. Accessed 20 Nov 2020.

  28. Schwendimann R, Blatter C, Dhaini S, Simon M, Ausserhofer D. The occurrence, types, consequences and preventability of in-hospital adverse events – a scoping review. BMC Health Serv Res. 2018;18:521.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Unbeck M, Schildmeijer K, Henriksson P, Jürgensen U, Muren O, Nilsson L, Pukk-Härenstam K. Is detection of adverse events affected by record review methodology? An evaluation of the “Harvard Medical Practice Study” method and the “Global Trigger Tool”. Patient Saf Surg. 2013;7(1):10.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Schmider J, Kumar K, LaForest C, Swankoski B, Naim K, Caubel PM. Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing. Clin Pharmacol Ther. 2019;105(4):954–61.

    Article  PubMed  Google Scholar 

  31. Singh H, Schiff GD, Graber ML, Onakpoya I, Thompson MJ. The global burden of diagnostic errors in primary care. BMJ Qual Saf. 2016;0:1–11.

    Google Scholar 

  32. Bejnordi BE, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–210.

    Article  Google Scholar 

  33. Banda JM, Sarraju A, Abbasi F, et al. Finding missed cases of familial hypercholesterolemia in health systems using machine learning. NPJ Digit Med. 2019;2(23):1–8.

    Google Scholar 

  34. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008;17(1):128–44.

    Google Scholar 

  35. Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848–55.

    Article  CAS  PubMed  Google Scholar 

  36. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc. 2005;12(4):448–57.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Mei X, Lee HC, Diao KY, et al. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat Med. 2020;26:1224–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Institute of Medicine (US) Committee on Quality of Health Care in America; Kohn LT, Corrigan JM, Donaldson MS, editors. To Err is Human: Building a Safer Health System. Washington, DC: National Academies Press (US); 2000; 2, Errors in health care: a leading cause of death and injury. https://www.ncbi.nlm.nih.gov/books/NBK225187/

  39. Shankar PR. Medicines use in primary care in developing and transitional countries: fact book summarizing results from studies reported between 1990 and 2006. Bull World Health Organ. 2009;87(10):804.

    Article  PubMed Central  Google Scholar 

  40. Wilson RM, Michel P, Olsen S, et al. Patient safety in developing countries: retrospective estimation of scale and nature of harm to patients in hospital. BMJ. 2012;344:1–14.

    Google Scholar 

  41. Barnsteiner JH. Medication reconciliation. In: Hughes RG, editor. Patient safety and quality: an evidence-based handbook for nurses. Rockville: Agency for Healthcare Research and Quality (US); 2008. Chapter 38. https://www.ncbi.nlm.nih.gov/books/NBK2648/.

    Google Scholar 

  42. Choudhury A, Asan O. Human factors: bridging artificial intelligence and patient safety. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care. 2020;9(1):211–15.

    Google Scholar 

  43. Thomson W, Farrell B. Deprescribing: what is it and what does the evidence tell us? Can J Hosp Pharm. 2013;66(3):201–2.

    Google Scholar 

  44. Aitken M, Gorokhovich L. Advancing the responsible use of medicines: applying levers for change. IMS Institute for Healthcare Informatics: Parsippany; 2012.

    Google Scholar 

  45. Gillespie U, Alassaad A, Henrohn D, Garmo H, Hammarlund-Udenaes M, Toss H, et al. A comprehensive pharmacist intervention to reduce morbidity in patients 80 years or older: a randomized controlled trial. Arch Intern Med. 2009;169(9):894–900.

    Article  PubMed  Google Scholar 

  46. Tomašev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572:116–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Kocbek S, Kocbek P, Stozer A, Zupanic T, Groza T, Stiglic G. Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records. PeerJ. 2018;6:e5765.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wilfling D, Hinz A, Steinhäuser J. Big data analysis techniques to address polypharmacy in patients – a scoping review. BMC Fam Pract. 2020;21:180.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Obermeyer Z, Emanuel EJ. Predicting the future – big data, machine learning, and clinical medicine. New Engl J Med. 2016;375(13):1216–9.

    Article  PubMed  Google Scholar 

  50. Segato A, Marzullo A, Calimeri F, Elena De Momi E. Artificial intelligence for brain diseases: a systematic review. APL Bioeng. 2020;4(4):041503.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Senders JT, Arnaout O, Karhade AV, Dasenbrock HH, Gormley WB, Broekman ML, Smith TR. Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery. 2018;83:181–92.

    Article  PubMed  Google Scholar 

  52. Hayashi D, Roemer FW, Eckstein F, Samuels J, Guermazi A. Imaging of OA – from disease modification to clinical utility. Best Pract Res Clin Rheumatol. 2020:101588.

    Google Scholar 

  53. Kulkarni S. Hypertension management in 2030: a kaleidoscopic view. J Hum Hypertens. 2020;1–6.

    Google Scholar 

  54. Wong TY, Liew G, Mitchell P. Clinical update: new treatments for age-related macular degeneration. Lancet. 2007;370:194–206.

    Google Scholar 

  55. Bloembergen CH, van de Graaf VA, Virgile A, et al. Infographic: can even experienced orthopaedic surgeons predict who will benefit from surgery when patients present with degenerative meniscal tears? A survey of 194 orthopaedic surgeons who made 3880 predictions. Br J Sports Med. 2020;54(9):556–7.

    Article  PubMed  Google Scholar 

  56. Oosterhoff JHF, Doornberg JN. Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner’s hype cycle. EFFORT Open Rev. 2020;5:593–603.

    Article  Google Scholar 

  57. Oosterhoff JHF, Karhade AV, Oberai T, Doornberg JN, Schwab JH. Development of machine learning algorithms for prediction of postoperative delirium in elderly hip fracture patients. Manuscript submitted for publication to Clin Orthop Relat Res; 2019.

    Google Scholar 

  58. Pitzul KB, Wodchis WP, Kreder HJ, Carter MW, Jaglal SB. Discharge destination following hip fracture: comparative effectiveness and cost analyses. Arch Osteoporos. 2017;12:87.

    Article  PubMed  Google Scholar 

  59. Molokhia M, Majeed A. Current and future perspectives on the management of polypharmacy. BMC Fam Pract. 2017;18(1):70.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Accident Compensation Act, Stat. 49, 2001 (NZ). http://www.legislation.govt.nz/act/public/2001/0049/latest/DLM99494.html?search=ts_act_accident_resel&p=1&sr=1

  61. Futoma J, Simons M, Panch T, Doshi-Velez F, Celi LA. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit Heal. 2020;2:489–92.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    M. Abdulhadi Alagha & Justin Cobb

  2. Institute of Global Health Innovation, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    M. Abdulhadi Alagha

  3. Preventive and Social Medicine, Dunedin School of Medicine, Dunedin, New Zealand

    Anastasia Young-Gough

  4. Centre for Medical and Health Science Education, University of Auckland, Auckland, New Zealand

    Mataroria Lyndon

  5. Department of Medicine, University of Otago, Dunedin, New Zealand

    Xaviour Walker & Debra L. Waters

  6. Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leo Anthony Celi

  7. Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Leo Anthony Celi

  8. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Leo Anthony Celi

Authors
  1. M. Abdulhadi Alagha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anastasia Young-Gough
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mataroria Lyndon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xaviour Walker
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Justin Cobb
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leo Anthony Celi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Debra L. Waters
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leo Anthony Celi .

Editor information

Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Alagha, M.A. et al. (2021). AIM and Patient Safety. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_272-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58080-3_272-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58080-3

  • Online ISBN: 978-3-030-58080-3

  • eBook Packages: Springer Reference MedicineReference Module Medicine

Publish with us

Policies and ethics

Search

Navigation