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Ethics in Biology, Engineering and Medicine: An International Journal

ISSN Imprimer: 2151-805X

ISSN En ligne: 2151-8068

SJR: 0.123

Ethical Challenges of Artificial Intelligence in Health Care: A Narrative Review

Volume 12, Numéro 1, 2021, pp. 55-71
DOI: 10.1615/EthicsBiologyEngMed.2022041580
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RÉSUMÉ

With the advent of advanced computing power, artificial intelligence (AI) has gained traction in all areas of human life. The field of medicine is no exception to the influence of AI because technology is intricately linked to the advancement of modern-day clinical practice. However, many challenges must still be addressed to ensure that broad adoption of AI in health care is practically feasible, safe, and accepted by health care professionals. This review focuses on the ethical challenges of implementing and developing AI algorithms to augment patient care in health care settings. More specifically, we discuss the issues of bias, privacy, security, lack of transparency and explainability, and the potential impacts on physician−patient relationships when large-scale AI models are incorporated into modern-day medicine.

RÉFÉRENCES
  1. Schwartz WB. Medicine and the computer-The promise and problems of change. N Engl J Med. 1970;283(23):1257-64. doi: 10.1056/NEJM197012032832305. DOI: 10.1056/NEJM197012032832305

  2. Loh E. Medicine and the rise of the robots: A qualitative review of recent advances of artificial intelligence in health. BMJ Lead. 2018;2(2):59-63. doi: 10.1136/leader-2018-000071. DOI: 10.1136/leader-2018-000071

  3. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi: 10.1038/s41591-018-0300-7. DOI: 10.1038/s41591-018-0300-7

  4. Chen JH, Asch SM. Machine learning and prediction in medicine-Beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-9. doi: 10.1056/NEJMp1702071. DOI: 10.1056/NEJMp1702071

  5. American Medical Association. Augmented intelligence in health care. Am Med Assoc. Published online 2018:4.

  6. Chen JH, Altman RB. Automated physician order recommendations and outcome predictions by data-mining electronic medical records. AMIA Summits Transl Sci Proc. 2014;2014:206-10.

  7. Shilo S, Rossman H, Segal E. Axes of a revolution: Challenges and promises of big data in healthcare. Nat Med. 2020;26(1):29-38. doi: 10.1038/s41591-019-0727-5. DOI: 10.1038/s41591-019-0727-5

  8. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff. 2014;33(7):1123-31. doi: 10.1377/ hlthaff.2014.0041.

  9. Smith M, Saunders R, Stuckhardt L, McGinnis JM, editors. Best care at lower cost: The path to continuously learning health care in America. Washington DC: The national academies press; 2013.

  10. Mennemeyer ST, Menachemi N, Rahurkar S, Ford EW. Impact of the HITECH act on physicians' adoption of electronic health records. J Am Med Inform Assoc. 2016;23(2):375-9. doi: 10.1093/ jamia/ocv103.

  11. Matheny M, Israni S, Ahmed M, Whicher D, editors. Artificial intelligence in health care. Washington DC: National academy of medicine; 2019. Accessed 8 Feb 2021. Available from: https://nam.edu/wp- content/uploads/2019/12/AI-in-Health-Care-PREPUB-FINAL.pdf.

  12. Atasoy H, Greenwood BN, McCullough JS. The digitization of patient care: A review of the effects of electronic health records on health care quality and utilization. Annu Rev Public Health. 2019;40(1):487-500. doi: 10.1146/annurev-publhealth-040218-044206. DOI: 10.1146/annurev-publhealth-040218-044206

  13. United States Department of Health and Human Services. HITECH act enforcement interim final rule. HHS.gov. Health Information Policy; 2009 [cited 2021 Feb 9]. Available from: https://www.hhs.gov/ hipaa/for-professionals/special-topics/HITECH-act-enforcement-interim-final-rule/index.html.

  14. Glaze J. Epic systems draws on literature greats for its next expansion | local government | madison. com; 2015 [cited 2021 Jan 17]. Available from: https://madison.com/news/local/govt-and-politics/ epic-systems-draws-on-literature-greats-for-its-next-expansion/article_4d1cf67c-2abf-5cfd-8ce1- 2da60ed84194.html.

  15. Choi E, Biswal S, Malin B, Duke J, Stewart WF, Sun J. Generating multi-label discrete patient records using generative adversarial networks. J Mach Learn Res. 2017;68:20.

  16. Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6(1):26094. doi: 10.1038/srep26094. DOI: 10.1038/srep26094

  17. Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA. 2016;316(22):2353-4. doi: 10.1001/jama.2016.17438. DOI: 10.1001/jama.2016.17438

  18. Keane PA, Topol EJ. With an eye to AI and autonomous diagnosis. NPJ Digit Med. 2018;1(40). doi: 10.1038/s41746-018-0048-y. DOI: 10.1038/s41746-018-0048-y

  19. Obermeyer Z, Emanuel EJ. Predicting the future-Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9. doi: 10.1056/NEJMp1606181. DOI: 10.1056/NEJMp1606181

  20. DeCamp M, Lindvall C. Latent bias and the implementation of artificial intelligence in medicine. J Am Med Inform Assoc. 2020;27(12):2020-3. doi: 10.1093/jamia/ocaa094. DOI: 10.1093/jamia/ocaa094

  21. Zhang B, Dafoe A. Artificial intelligence: American attitudes and trends. SSRN Electron J. 2019. Available from: https://ssrn.com/abstract=3312874 or http://dx.doi.org/10.2139/ssrn.3312874. DOI: 10.2139/ssrn.3312874

  22. AI and empathy: Combining artificial intelligence with human ethics for better engagement. Pegasystems; 2019 [cited 2021 Jan 17]. Available from: https://www.pega.com/system/files/re- sources/2019-11/pega-ai-empathy-study.pdf.

  23. Goode L. Life, but not as we know it: A.I. and the popular imagination. Cult Unbound. 2018;10(2):185-207. doi: 10.3384/cu.2000.1525.2018102185. DOI: 10.3384/cu.2000.1525.2018102185

  24. Esmaeilzadeh P. Use of AI-based tools for healthcare purposes: A survey study from consumers' perspectives. BMC Med Inform Decis Mak. 2020;20(1):170. doi: 10.1186/s12911-020-01191-1. DOI: 10.1186/s12911-020-01191-1

  25. Ouchchy L, Coin A, Dubljevic V. AI in the headlines: The portrayal of the ethical issues of artificial intelligence in the media. AI Soc. 2020;35(4):927-36. doi: 10.1007/s00146-020-00965-5. DOI: 10.1007/s00146-020-00965-5

  26. Turner Lee N. Detecting racial bias in algorithms and machine learning. J Inf Commun Ethics Soc. 2018;16(3):252-60. doi: 10.1108/JICES-06-2018-0056. DOI: 10.1108/JICES-06-2018-0056

  27. Angwin J, Larson J, Mattu S, Kirchner L. Machine bias. ProPublica; 2016 [cited 2021 Jan 17]. Available from: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

  28. Murray SG, Wachter RM, Cucina RJ. Discrimination by artificial intelligence in a commercial electronic health record - A case study. Health affairs; 2020 [cited 2021 Jan 17]. Available from: https:// www.healthaffairs.org/do/10.1377/hblog20200128.626576/full/?utm_source=Newsletter&utm_ medium=email&utm_content=US+Response+To+Coronavirus;+Discrimination+By+Artificial+Intel ligence;+Hot+Articles+From+January;+Book+Reviews&utm_campaign=HAT. DOI: 10.1377/forefront.20200128.626576

  29. Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866-72. doi: 10.7326/M18-1990. DOI: 10.7326/M18-1990

  30. Char DS, Shah NH, Magnus D. Implementing machine learning in health care-Addressing ethical challenges. N Engl J Med. 2018;378(11):981-3. doi: 10.1056/NEJMp1714229. DOI: 10.1056/NEJMp1714229

  31. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. doi: 10.1371/journal.pmed.1002689. DOI: 10.1371/journal.pmed.1002689

  32. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20(1):117-21. doi: 10.1136/amiajnl-2012-001145. DOI: 10.1136/amiajnl-2012-001145

  33. Agniel D, Kohane IS, Weber GM. Biases in electronic health record data due to processes within the healthcare system: Retrospective observational study. BMJ. 2018:361. doi: 10.1136/bmj.k1479. DOI: 10.1136/bmj.k1479

  34. Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol. 2018;129(3):421-6. doi: 10.1016/j. radonc.2018.05.030.

  35. AMIA Supports, encourages further refinement of FDA AI/machine learning regulatory framework; 2019 [cited 2021 Jan 17]. Available from: https://www.amia.org/news-and-publications/press-release/ amia-supports-encourages-further-refinement-fda-aimachine-learning.

  36. Booker C, Wyden R. Algorithmic accountability act of 2019. Available from: https://www.wyden.senate. gov/imo/media/doc/Algorithmic%20Accountability%20Act%20of%202019%20Bill%20Text.pdf.

  37. Epstein AM, Stern RS, Tognetti J, Begg CB, Hartley RM, Cumella E, Ayanian JZ. The association of patients' socioeconomic characteristics with the length of hospital stay and hospital charges within diagnosis-related groups. N Engl J Med. 1988 Jun 16;318(24):1579-85. doi: 10.1056/ NEJM198806163182405.

  38. Morris M, Cooper RL, Ramesh A, Tabatabai M, Arcury TA, Shinn M, Im W, Juarez P, Matthews-Juarez P. Training to reduce LGBTQ-related bias among medical, nursing, and dental students and providers: A systematic review. BMC Med Educ. 2019;19(1):325. doi: 10.1186/s12909-019-1727-3. DOI: 10.1186/s12909-019-1727-3

  39. Michelson MR. The power of visibility: Advances in LGBT rights in the United States and Europe. J Polit. 2018;81(1):e1-5. doi: 10.1086/700591. DOI: 10.1086/700591

  40. Smith DM, Mathews WC. Physicians' attitudes toward homosexuality and HIV: Survey of a California medical society-revisited (PATHH-II). J Homosex. 2007;52(3-4):1-9. doi: 10.1300/ J082v52n03_01.

  41. The Henry J. Kaiser family foundation. national survey of physicians part 1: Doctors on disparities in medical care; 2002. Available from: https://www.kff.org/wp-content/uploads/2002/03/national-sur-vey-of-physicians-part-1.pdf.

  42. Safer JD, Coleman E, Feldman J, Garofalo R, Hembree W, Radix A, Sevelius J. Barriers to health care for transgender individuals. Curr Opin Endocrinol Diabet Obes. 2016;23(2):168-71. doi: 10.1097/ MED.0000000000000227.

  43. Grant JM, Mottet LA, Tanis J. Injustice at every turn: A report of the national transgender discrimination survey. National center for transgender equality [cited 2021 Mar 29]. Available from: https:// www.transequality.org/sites/default/files/docs/resources/NTDS_Report.pdf.

  44. Walker RV, Powers SM, Witten TM. Impact of anticipated bias from healthcare professionals on perceived successful aging among transgender and gender nonconforming older adults. LGBT Health. 2017;4(6):427-33. doi: 10.1089/lgbt.2016.0165. DOI: 10.1089/lgbt.2016.0165

  45. Aleshire ME, Ashford K, Fallin-Bennett A, Hatcher J. Primary care providers' attitudes related to LGBTQ people: A narrative literature review. Health Promot Pract. 2019;20(2):173-87. doi: 10.1177/1524839918778835. DOI: 10.1177/1524839918778835

  46. Mayfield JJ, Ball EM, Tillery KA, Crandall C, Dexter J, Winer JM, Bosshardt ZM, Welch JH, Dolan E, Fancovic ER, Nanez AI, DeMay H, Finlay E, Lee SM, Streed CG, Ashraf K. Beyond men, women, or both: A comprehensive, LGBTQ-inclusive, implicit-bias-aware, standardized-patient-based sexual history taking curriculum. MedEdPORTAL. doi: 10.15766/mep_2374-8265.10634. DOI: 10.15766/mep_2374-8265.10634

  47. Stewart K, O'Reilly P. Exploring the attitudes, knowledge and beliefs of nurses and midwives of the healthcare needs of the LGBTQ population: An integrative review. Nurse Educ Today. 2017;53:67- 77. doi: 10.1016/j.nedt.2017.04.008. DOI: 10.1016/j.nedt.2017.04.008

  48. Dean MA, Victor E, Guidry-Grimes L. Inhospitable healthcare spaces: Why diversity training on LGBTQIA issues is not enough. J Bioethical Inq. 2016;13(4):557-70. doi: 10.1007/s11673-016-9738-9. DOI: 10.1007/s11673-016-9738-9

  49. What women need to know. National osteoporosis foundation [cited 2021 Mar 29]. Available from: https://www.nof.org/preventing-fractures/general-facts/what-women-need-to-know/.

  50. Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA. 2019;322(24):2377-8. doi: 10.1001/jama.2019.18058. DOI: 10.1001/jama.2019.18058

  51. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-8. doi: 10.7326/M18-1376. DOI: 10.7326/M18-1376

  52. Bellamy RKE, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, Martino J, Mehta S, Mojsilovic A, Nagar S, Ramamurthy KN, Richards J, Saha D, Sattigeri P, Singh M, Varshney KR, Zhang Y. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J Res Dev. 2019;63(4/5):4:1-15. doi: 10.1147/JRD.2019.2942287. DOI: 10.1147/JRD.2019.2942287

  53. Gaonkar B, Kim K, Macyszyn L. Ethical issues arising due to bias in training A.I. algorithms in healthcare and data sharing as a potential solution. AI Ethics J. 2020;1(1):1-9. doi: 10.47289/AIEJ20200916. DOI: 10.47289/AIEJ20200916

  54. Lewis GA, Bellomo S, Galyardt A. Component mismatches are a critical bottleneck to fielding AI-enabled systems in the public sector. ArXiv191006136 Cs; 2019 [cited 2021 Feb 12]. Available from: http://arxiv.org/abs/1910.06136.

  55. Longo DL, Drazen JM. Data sharing. N Engl J Med. 2016;374(3):276-7. doi: 10.1056/NEJMe1516564. DOI: 10.1056/NEJMe1516564

  56. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: Implications for health systems. J Glob Health. 2019;9(2). doi: 10.7189/jogh.09.020318. DOI: 10.7189/jogh.09.020318

  57. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. doi: 10.1126/science.aax2342. DOI: 10.1126/science.aax2342

  58. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: Concept and applications. ACM Trans Intel Syst Technol. 2019;10(2):12:1-19. doi: 10.1145/3298981. DOI: 10.1145/3298981

  59. Glenn T, Monteith S. Privacy in the digital world: Medical and health data outside of HIPAA protections. Curr Psychiatry Rep. 2014;16(11):494. doi: 10.1007/s11920-014-0494-4. DOI: 10.1007/s11920-014-0494-4

  60. Park SH, Do K-H, Kim S, Park JH, Lim Y-S. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16. doi: 10.3352/jeehp.2019.16.18. DOI: 10.3352/jeehp.2019.16.18

  61. Gillon R. Medical ethics: Four principles plus attention to scope. BMJ. 1994;309(6948):184. doi: 10.1136/bmj.309.6948.184. DOI: 10.1136/bmj.309.6948.184

  62. Yu R, Ali GS. What's inside the black box? AI challenges for lawyers and researchers. Leg Inf Manag. 2019;19(1):2-13. doi: 10.1017/S1472669619000021. DOI: 10.1017/S1472669619000021

  63. Dosilovic FK, Brcic M, Hlupic N. Explainable artificial intelligence: A survey. In: Proceedings of the 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO). May 21-25, 2018. Opatija, Croatia.

  64. Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L. Explaining explanations: An overview of interpretability of machine learning. In: Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA); 2018. p. 80-9.

  65. Cohen IG, Mello MM. HIPAA and protecting health information in the 21st century. JAMA. 2018;320(3):231-2. doi: 10.1001/jama.2018.5630. DOI: 10.1001/jama.2018.5630

  66. Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517-8. doi: 10.1001/jama.2017.7797. DOI: 10.1001/jama.2017.7797

  67. Deo Rahul C. Machine learning in medicine. Circulation. 2015;132(20):1920-30. doi: 10.1161/ CIRCULATI0NAHA.115.001593.

  68. Li X, Pan D, Zhu D. Defending against adversarial attacks on medical imaging AI system, classification or detection? ArXiv200613555 Cs Ecss; 2020 [cited 2021 Jan 17]. Available from: http://arxiv. org/abs/2006.13555.

  69. Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. doi: 10.1038/s41591-018-0272-7. DOI: 10.1038/s41591-018-0272-7

  70. Ford R, Price WN. Privacy and accountability in black-box medicine. Mich Telecommun Technol Law Rev. 2016;23(1):44.

  71. Ram N, Guerrini CJ, McGuire AL. Genealogy databases and the future of criminal investigation. Science. 2018;360(6393):1078-9. doi: 10.1126/science.aau1083. DOI: 10.1126/science.aau1083

  72. Marelli L, Testa G. Scrutinizing the EU general data protection regulation. Science. 2018;360(6388):496-8. doi: 10.1126/science.aar5419. DOI: 10.1126/science.aar5419

  73. Horvitz E, Mulligan D. Data, privacy, and the greater good. Science. 2015;349(6245):253-5. doi: 10.1126/science.aac4520. DOI: 10.1126/science.aac4520

  74. Neubauer T, Heurix J. A methodology for the pseudonymization of medical data. Int J Med Inf. 2011;80(3):190-204. doi: 10.1016/j.ijmedinf.2010.10.016. DOI: 10.1016/j.ijmedinf.2010.10.016

  75. Abouelmehdi K, Beni-Hssane A, Khaloufi H, Saadi M. Big data security and privacy in healthcare: A review. Procedia Comput Sci. 2017;113:73-80. doi: 10.1016/j.procs.2017.08.292. DOI: 10.1016/j.procs.2017.08.292

  76. Narayanan A, Shmatikov V. Myths and fallacies of "personally identifiable information." Commun ACM. 2010;53(6):24-6. doi: 10.1145/1743546.1743558. DOI: 10.1145/1743546.1743558

  77. Ohmann C, Banzi R, Canham S, Battaglia S, Matei M, Ariyo C, Becnel L, Bierer B, Bowers S, Clivio L, Dias M, Druml C, Faure H, Fenner M, Galvez J, Ghersi D, Gluud C, Groves T, Houston P, Karam G, Kalra D, Knowles RL, Krleza-Jeric K, Kubiak C, Kuchinke W, Kush R, Lukkarinen A, Marques PS, Newbigging A, O'Callaghan J, Ravaud P, Schlunder I, Shanahan D, Sitter H, Spalding D, Tudur-Smith C, van Reusel P, van Veen E, Visser GR, Wilson J, Demotes-Mainard J. Sharing and reuse of individual participant data from clinical trials: Principles and recommendations. BMJ Open. 2017;7(12):e018647. doi: 10.1136/bmjopen-2017-018647. DOI: 10.1136/bmjopen-2017-018647

  78. Ohm P. Broken promises of privacy: Responding to the surprising failure of anonymization. UCLA Law Rev. 2010;77.

  79. Cohen IG, Mello MM. Big data, big tech, and protecting patient privacy. JAMA. 2019;322(12):1141- 2. doi: 10.1001/jama.2019.11365. DOI: 10.1001/jama.2019.11365

  80. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-6. doi: 10.1038/s41591-018-0307-0. DOI: 10.1038/s41591-018-0307-0

  81. Miner AS, Milstein A, Hancock JT. Talking to machines about personal mental health problems. JAMA. 2017;318(13):1217-8. doi: 10.1001/jama.2017.14151. DOI: 10.1001/jama.2017.14151

  82. Verghese A. Culture shock-Patient as icon, icon as patient. N Engl J Med. 2008;359(26):2748-51. doi: 10.1056/NEJMp0807461. DOI: 10.1056/NEJMp0807461

  83. Yaghy A, Shields JA, Shields CL. Representing communication, compassion, and competence in the era ofAI. AMA J Ethics. 2019;21(11):1009-13. doi: 10.1001/amajethics.2019.1009. DOI: 10.1001/amajethics.2019.1009

  84. Lai M-C, Brian M, Mamzer M-F. Perceptions of artificial intelligence in healthcare: Findings from a qualitative survey study among actors in France. J Transl Med. 2020;18(1):14. doi: 10.1186/ s12967-019-02204-y.

  85. Turja T, Aaltonen I, Taipale S, Oksanen A. Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots. Inf Manag. 2020;57(5):103220. doi: 10.1016/j. im.2019.103220.

  86. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. Peer J. 2019;7. doi: 10.7717/peerj.7702. DOI: 10.7717/peerj.7702

  87. Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13-4. doi: 10.1016/j.ejim.2017.06.017. DOI: 10.1016/j.ejim.2017.06.017

  88. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-8. doi: 10.7861/futurehosp.6-2-94. DOI: 10.7861/futurehosp.6-2-94

  89. Lin SY, Mahoney MR, Sinsky CA. Ten ways artificial intelligence will transform primary care. J Gen Intern Med. 2019;34(8):1626-30. doi: 10.1007/s11606-019-05035-1. DOI: 10.1007/s11606-019-05035-1

  90. The complexities of physician supply and demand: Projections from 2018 to 2033. Washington DC: Association of American Medical Colleges; 2020. Available from: https://www.aamc.org/system/ files/2020-06/stratcomm-aamc-physician-workforce-projections-june-2020.pdf.

  91. Hill RG, Sears LM, Melanson SW. 4000 Clicks: A productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med. 2013;31(11):1591-4. doi: 10.1016/j.ajem.2013.06.028. DOI: 10.1016/j.ajem.2013.06.028

  92. Downing NL, Bates DW, Longhurst CA. Physician burnout in the electronic health record era: Are we ignoring the real cause? Ann Intern Med. 2018;169(1):50-1. doi: 10.7326/M18-0139. DOI: 10.7326/M18-0139

  93. Shanafelt TD, Dyrbye LN, West CP. Addressing physician burnout: The way forward. JAMA. 2017;317(9):901-2. doi: 10.1001/jama.2017.0076. DOI: 10.1001/jama.2017.0076

  94. Herasevich V, Pickering B, Gajic O. How Mayo Clinic is combating information overload in critical care units. Harv Bus Rev; 2018 [cited 2021 Feb 10]. Available from: https://hbr.org/2018/03/ how-mayo-clinic-is-combating-information-overload-in-critical-care-units.

  95. Ahmed A, Chandra S, Herasevich V, Gajic O, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med. 2011;39(7):1626-34. doi: 10.1097/CCM.0b013e31821858a0. DOI: 10.1097/CCM.0b013e31821858a0

  96. Pickering BW, Dong Y, Ahmed A, Giri J, Kilickaya O, Gupta A, Gajic O, Herasevich V. The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: A pilot step-wedge cluster randomized trial. Int J Med Inf. 2015;84(5):299-307. Doi: 10.1016/j.ijmedinf.2015.01.017. DOI: 10.1016/j.ijmedinf.2015.01.017

  97. Olchanski N, Dziadzko MA, Tiong IC, Daniels CE, Peters SG, O'Horo JC, Gong MN. Can a novel ICU data display positively affect patient outcomes and save lives? J Med Syst. 2017;41(11):171. Doi: 10.1007/s10916-017-0810-8. DOI: 10.1007/s10916-017-0810-8

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