Ethics in the History of Medical Informatics for Decision-Making: Early Challenges to Digital Health Goals

Summary

Background: Inclusive digital health prioritizes public engagement through digital literacies and internet/web connectivity for advancing and scaling healthcare equitably by informatics technologies. This is badly needed, largely desirable and uncontroversial. However, historically, medical and healthcare practices and their informatics processes assume that individual clinical encounters between practitioners and patients are the indispensable foundation of clinical practice. This assumption has been dramatically challenged by expansion of digital technologies, their interconnectable mobility, virtuality, surveillance informatics, and the vastness of data repositories for individuals and populations that enable and support them. This article is a brief historical commentary emphasizing critical ethical issues about decisions in clinical interactions or encounters raised in the early days of the field. These questions, raised eloquently by François Grémy in 1985, have become urgently relevant to the equity/fairness, inclusivity and unbiasedness desired of today's pervasive digital health systems.

Objectives: The main goal of this article is to highlight how the personal freedoms of choice, values, and responsibilities arising in relationships between physicians and healthcare practitioners and their patients in the clinical encounter can be distorted by digital health technologies which focus more on efficiency, productivity, and scalability of healthcare processes. Understanding the promise and limitations of early and current decision-support systems and the analytics of community or population data can help place into historical context the often exaggerated claims made today about Artificial Intelligence and Machine Learning “solving” clinical problems with algorithms and data, downplaying the role of the clinical judgments and responsibilities inherent in personal clinical encounters.

Methods: A review of selected early articles in medical informatics is related to current literature on the ethical issues and technological inadequacies involved in the design and implementation of clinical systems for decision-making. Early insights and cautions about the development of decision support technologies raised questions about the ethical responsibilities in clinical encounters where freedom of personal choice can be so easily limited through the constraints from information processing and reliance on prior expertise frequently driven more by administrative rather than clinical objectives. These anticipated many of the deeper ethical problems that have arisen since then in clinical informatics.

Conclusions: Early papers on ethics in clinical decision-making provide prescient commentary on the dangers of not taking into account the complexities of individual human decision making in clinical encounters. These include the excessive reliance on data and experts, and oversimplified models of human reasoning, all of which persist and have become amplified today as urgent questions about how inclusivity, equity, and bias are handled in practical systems where ethical responsibilities of individuals patients and practitioners intertwine with those of groups within professional or other communities, and are central to how clinical encounters evolve in our digital health future.

Keywords

Ethics - clinical decision-making - informatics - delivery of health care - digital technology - artificial intelligence - machine learning

Publication History

Article published online:
02 June 2022

• References

• 1 Lusted LB. Clinical Decision-making. In: de Dombal FT, Grémy F, editors. Decision-Making and Medical Care. Amsterdam: North Holland; 1976. p. 77- 98.
• 2 Grémy F. Ethics of the person and of the community in medical decision making. In: van Bemmel JH, Grémy F, Zvarova J, editors. Medical decision making: diagnostic strategies and expert systems. Amsterdam: North Holland; 1985. p. 12 – 18.
• 3 Azzopardi-Muscat N, Sorensen K. Towards an equitable digital public health era: promoting equity through a health literacy perspective. Eur J Public Health 2019;29(Supplement_3):13-7.
• 4 Lupton D. How do data come to matter? Living and becoming with personal data. Big Data and Society 2018:1-11.
• 5 WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.
• 6 Bernard C. An introduction to the study of experimental medicine. Dover Books (English Translation); 1961.
• 7 Shaw JA, Donia J. The sociotechnical ethics of digital health: A critique and extension of approaches from bioethics. Front Digit Health Sept 2021(3):725088.
• 8 Maojo V, Kulikowski CA. Medical informatics and bioinformatics: integration or evolution through scientific crises? Methods Inf Med 2006;45(5):474-82.
• 9 Kuznetsov V, Lee HK, Maurer-Stroh S, Molnár MJ, Pongor S, Eisenhaber S, et al. How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health. Health Inf Sci Syst 2013;1:2.
• 10 Fernandez-Moure JS. Lost in translation: The gap in scientific advancements and clinical applications. Front Bioeng Biotechnol 2016 Jun 3;4:43.
• 11 Stacey D, Legare F, Lewis K., et al, Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev 2017 Apr 12;4(4):CD001431.
• 12 Degoulet P, Fieschi M, Goldberg M, Salamon R. François Grémy, a humanist and information sciences pioneer. Yearb Med Inform 2014;9(1):3-5.
• 13 Benoit S, Mauldin RF. The “anti-vax” movement: a quantitative report on vaccine beliefs and knowledge across social media. BMC Public Health 2021 Nov 17;21(1):2106.
• 14 Kulikowski CA. Pandemics: Historically Slow “Learning Curve” Leading to Biomedical Informatics and Vaccine Breakthroughs. Yearb Med Inform 2021 Aug;30(1):290-301.
• 15 Sneed T. Supreme Court ruling on Texas law was the result of decades of pressure from anti-abortion groups to shape the court; CNN December 13, 2021. Available from: https://www.cnn.com/2021/09/04/politics/abortion-legal-strategy-roe-v-wade-texas-abortion-ban/index.html
• 16 Baker RB, McCullough LB, editors. The Cambridge World History of Medical Ethics. Cambridge University Press; 2008.
• 17 Beauchamp T., Childress JF. Principles of biomedical ethics. New York, NY: Oxford University Press; 1977.
• 18 Benjamin M, Curtis J. Ethics in Nursing (3rd Ed.). New York, NY: Oxford University Press: 1992.
• 19 Murphy EA, Butzow JJ, Suarez-Murias EL. Underpinnings of Medical Ethics. Johns Hopkins University Press; 1997.
• 20 Husted GL, Husted JH. Ethical decision-making in nursing (2nd Ed). New York, NT: Mosby; 1995.
• 21 Goodman KW, editors. Ethics, Computing, and Medicine. Cambridge University Press; 1998.
• 22 Goodman KW, Cushman R, Miller RA. Ethics in biomedical health informatics: Users, standards and outcomes. In Shortliffe EH, Cimino JJ, editors. Biomedical Informatics, 4th Edition. London: Springer; 2014.
• 23 Phillips W. Ethical controversies about proper health informatics practices. Mo Med 2015 Jan-Feb 112(1):53-7.
• 24 Curtin L. Ethics in Informatics: The intersection of nursing, ethics and information technology. Nurs Admin Q 2005;29(4):349-52.
• 25 Weiss SM. A system for model-based computer-aided diagnosis and therapy. Thesis. Rutgers University; 1974.
• 26 Shortliffe EH. Computer-Based Medical Consultation: MYCIN. New York: Elsevier; 1976.
• 27 Miller RA, Pople HE, Myers JD. INTERNIST-1, An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine. N Eng J Med 1982;307:478-86.
• 28 Szolovits P, Pauker SG. Categorical and probabilistic reasoning in medical diagnosis. Artif Intell 1978;11:115-44.
• 29 Szolovits P, Pauker SG. Computers and Clinical Decision Making: Whether, How, and For Whom? Proc IEEE 1979;67(9):1224-6.
• 30 Kulikowski CA. Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art – with Reflections on Present AIM Challenges. Yearb Med Inform 2019;28(01):249-56.
• 31 Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 1959 Jul 3;130(3366):9-21.
• 32 Warner HR, Toronto AF, Veasy LG, Stephenson R. A mathematical approach to medical diagnosis. Application to congenital heart disease. JAMA 1961 Jul 22;177:177-83.
• 33 Jacquez JA. The diagnostic process: the proceedings of a conference sponsored by the Biomedical Data Processing Training Program of the University of Michigan held at the University of Michigan Medical School; 1963.
• 34 Collen M. Automated multiphasic screening as a diagnostic method for preventive medicine. Methods Inf Med 1965;4:71-4.
• 35 Gorry GA, Barnett GO. Experience with a model of medical diagnosis. Comp Biomed Res 1968;1(5):490-507.
• 36 Steinberg CA, Abraha S, Caceres CA. Pattern recognition in the clinical electrocardiogram. IRE Trans Biomed Electron 1962;9(1):23-30.
• 37 Kulikowski CA. A pattern recognition approach to medical diagnosis. IEEE Trans Syst Science Cybernetics 1970;6(3):173-8.
• 38 Politakis P, Weiss S. Using Empirical analysis to refine expert system knowledge bases. Artif Intell 1984;22(1):23-48.
• 39 Weiss SM, Kulikowski CA, Galen RS. Developing microprocessor-based expert models for instrument interpretation. Proc 7th Joint Conf on Artificial Intelligence; 1981. p. 853-5.
• 40 Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ. PUFF: An expert system for interpretation of pulmonary function data. Com Biomed Res 1983;16:199-208.
• 41 Klann JG, Szolovits P, Downs S, Schadow G. Decision Support from Local Data: Creating Adaptive Order Menus from Past Clinician Behavior. J Biomed Inform 2014 Apr:48:84-93.
• 42 Austen-Smith D, Banks JS. Information aggregation, rationality, and the Condorcet jury theorem. The American Political Science Review 1996;90:34-45.
• 43 Kulikowski CA, Maojo V. COVID-19 pandemic and artificial intelligence: challenges of ethical bias and trustworthy reliable reproducibility? BMJ Health Care Inform 2021:28:e100438.
• 44 Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 2021;3(11):e745-e750.
• 45 Le Thien M-A, Redjdal A, Bouaud J, Serrousi B. Deep Learning, a not so magical problem solver: A Case Study with Predicting the complexity of breast cancer cases. In: Delgado J, Benis A, de Toledo P, Gallos P, Giacomini M, Martinez-Garcia A, et al, editors. Applying FAIR Principles to Accelerate Health Research in Europe in the Post-COVID-19 Era). EFMI and IOS Press; 2021.
• 46 Pearl J. The limitations of opaque learning machines. In: Brockman J, editors. Possible Minds: 25 Ways of Looking at AI. Penguin Press; 2019.
• 47 Lyell D, Coiera E. Automation bias and verification complexity: a systematic review. J Am Med Inform Assoc 2017 Mar 1;24(2):423-31.
• 48 Akbar S, Coiera E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. J Am Med Inform Assoc Feb 2020;27(2):330”40.
• 49 Coiera E, Baker M, Margabi F. First compute no harm. The BMJ Opinion July 19, 2017. Available from: https://blogs.bmj.com/bmj/2017/07/19/enrico-coiera-et-al-first-compute-no-harm/
• 50 Coeira E, Aarts J, Kulikowski CA. The dangerous decade. J Am Med Inform Assoc 2012;19(1):2-5.
• 51 Sharpe VA, Faden AI. Medical Harm: Historical, Conceptual, and Ethical Dimensions of Iatrogenic Illness. Cambridge University Press; 1998.
• 52 Sittig DF, Wright A, Coeira E, Magrabi F, Ratwani R, Bates DW, et al. Current challenges in health information technology-related patient safety. Health Informatics J 2020;26(1):181-89.
• 53 Kim MO, Coeira E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am Med Inform Assoc 2017;24(2):246-50.
• 54 Sieck CJ, Sheon A, Ancker JS, Castek J, Callahan B, Siefer A. NPJ Digit Med 2021;4:52.
• 55 Hippocrates. Of the Epidemics (Adams F, Translation). Available from: http://classics.mit.edu/Hippocrates/epidemics.1.i.html
• 56 Andersen M, Andersen SL. Machine Ethics. Cambridge, Cambridge University Press; 2011.
• 57 Stanford Encyclopedia of Philosophy. Ethics of Artificial Intelligence and Robotics. First Published April 30, 2020. Available from: https://plato.stanford.edu/entries/ethics-ai/