From data interoperability to ‘moral interoperability’ in the global health data architecture: integrated use case of AI-driven computational ethical analysis with Bayesian-propensity score and cost-benefit analyses optimizing efficiency and equity in colorectal cancer
Article Sidebar
Main Article Content
Abstract
The surging health and financial costs of diseases, disabilities, and disparities support the global acceleration of interests and investments in health AI for better, cheaper, faster, and fairer health solutions globally and locally. Yet there is no consensus on practically operationalizing responsible AI principles across diverse global sectors, states, and belief systems. This proof-of-concept study utilizes the global pluralistic ethical framework (the Personalist Social Contract) to therefore
provide the first known Bayesian augmented AI-driven Computational Ethical (AiCE) and policy analysis integrating clinical, cost effectiveness, and healthcare disparity analyses with nationally representative data to estimate the global cost of healthcare disparities in colonoscopy (CS) and the savings from AI-enabled CS to reduce them. It suggests
that reversing racial disparities particularly for Hispanics and Asians may save American healthcare systems $17.61 billion annually, with AI-augmented CS potentially contributing savings of $625.40 million for Hispanics and $289.00 million for Asians in particular (with similar cost savings for vulnerable communities in middle and low-income countries also). The above findings support the cost savings imperative for such strategic and capacity-building investment in these AI-driven measures to improve healthcare’s strategic aims of Sustainability, Effectiveness, Efficiency, and Equity (SEEE). Such empirical results inform the larger global bioethical argument from the twin dimensions of human dignity and human security (rooted in the personalist, multicultural, and metaphysical account of the person as a member of the global human family) to highlight the AI ethical imperative to optimize the performance of the global digital health ecosystem. Such
an instrumental end is a critical means of advancing toward the ultimate end of the common good, in which the individual good of each person is safeguarded and in which it finds her/his fulfillment working toward.
Downloads
PLUMX Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Medicina y Ética is distributed under a Creative Commons License Atribución-NoComercial-CompartirIgual 4.0 Internacional.
The author keeps the property rights with no restriction whatsoever and guarantees the magazine the right to be the first publication of the work. The author is free to deposit the published version in any other medium, such as an institutional archive or on his own website.
References
Monlezun DJ. The Thinking Healthcare System: Artificial Intelligence and Human Equity. Oxford, UK: Elsevier. 2023
McGlynn EA, Asch SM, Adams J. The quality of health care delivered to adults in the United Nations. New England Journal of Medicine. 2003; 348:2635-2645. https://pubmed.ncbi.nlm.nih.gov/12826639/
WHO, OECD, World Bank, 2018. Delivering quality health services: A global imper- ative for universal health coverage. Geneva, Switzerland: who Press.
Makary MA. Daniel M. Medical error-the third leading cause of death in the US. BMJ. 2016; 353:i2139. https://doi.org/10.1136/bmj.i2139
Barber SL, Lorenzoni L, Ong P. Price setting and price regulation in health care. Geneva, Switzerland: who Press. 2019.
Shamasunder S, Holmes SM, Goronga T, Carrasco H, Katz E, Frankfurter R. COVID-19 reveals weak health systems by design: Why we must re-make global health in this historic moment. Global Public Health. 2020; 15(7):1083-9. https://doi.org/10.1080/17441692.2020.1760915
Appleby C, Hendricks J, Wurz J, Shudes C, Shukla M, Chang C. Digital transformation: from a buzzword to an imperative for health systems. Deloitte. 2020. https://www2.deloitte.com/uk/en/insights/industry/health-care/digital-transformation-in-healthcare.html
Chén OY, Roberts B. Personalized health care and public health in the Digital Age. Frontiers in Digital Health. 3:595704. https://doi.org/10.3389/fdgth.2021.595704
Zimlichman E, Nicklin W, Aggarwal R, Bates DW. Health care 2030: The coming transformation. New England Journal of Medicine Catalyst. 2020. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0569
Philips. Future Health Index 2021: A resilient future. 2021. https://www.philips.com/c-dam/corporate/newscenter/global/future-health-index/report-pages/experience-transformation/2021/philips-future-health-index-2021-report-healthcare-leaders-look-beyond-the-crisis-global.pdf
Takemi K, Jimba M, Ishii S, Katsuma Y, Nakamura Y. Working Group on Challenges in Global Health and Japan’s Contribution. Human security approach for global health. London, England: Lancet. 2008; 372(9632):13-14. https://doi.org/10.1016/S0140-6736(08)60971-X
Monlezun DJ, Sinyavskiy O, Peters N,Steigner L, Aksamit T ,Girault MI, Garcia A, Gallagher C, Iliescu C. Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score. Medicina Kaunas, Lithuania. 2022; 58(8):1039. https://doi.org/10.3390/medicina58081039
Gomez J, Feldberg B, Krois J, Schwendicke F. Evaluation of the Clinical, Techni- cal, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis. JMIR Med Inform. 2022; 10(8):e33703. https://doi.org/10.2196/33703
Voets MM, Veltman J, Slump CH, Siesling S, Koffijberg H. Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2022; 25(3):340–349. https://doi.org/10.1016/j.jval.2021.11.1362
who, 2021.who issues first global report on artificial intelligence (AI) in health and six guiding principles for its design and use. World Health Organization. https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use
Monlezun DJ. The Personalist Social Contract: Saving Multiculturalism, Artificial Intelligence & Civilization; Cambridge Scholars Press: Cambridge, UK, 2022.
Morgan E, Arnold M, Gini A, Lorenzoni V, Cabasag CJ, Laversanne M, Vignat J, Ferlay J, Murphy N, Bray F. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. BMJ gut. 2022. https://doi.org/10.1136/gutjnl-2022-327736
HCUP Databases. Healthcare Cost and Utilization Project (HCUP): Agency for Healthcare Research and Quality. 2021. www.hcup-us.ahrq.gov/nisoverview.jsp
DUA Training. Healthcare Cost and Utilization Project (HCUP): Agency for Healthcare Research and Quality. 2021. www.hcup-us.ahrq.gov/DUA/dua_508/DU-A508version.jsp
Data Sets Not Requiring IRB Review. National Bureau of Economic Research. 2022. https://www.nber.org/programs-projects/projects-and-centers/human-subjects-protection-and-institutional-review-board-irb/guidance-data-sets-not-requiring-irb-review
Monlezun DJ, Lawless S, Palaskas N, Peerbhai S, Charitakis K, Marmagkiolis K, Lopez-Mattei J, Mamas M, Iliescu C. Machine learning-augmented propensity score analysis of percutaneous coronary intervention in over 30 million cancer and non-cancer patients. Front. Cardiovasc. Med. 2021; 8:620857.
Monlezun DJ, Carr C, Niu T, Nordio F, De Valle N, Sarris L, Harlan T. Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5847 medical trainees, providers and patients. Public Health Nutr. 2022; 25:281-289.
Murray TA, Yuan Y, Thall PF .A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes. Journal of the American Statistical Association. 2018; 113(523):1255-1267. https://doi.org/10.1080/01621459.2017.1340887
D’Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 1998; 17: 2265-2281. https://pubmed.ncbi.nlm.nih.gov/9802183/
Elze MC, Gregson J, Baber U, Williamson E, Sartori S, Mehran R, Nichols M, Stone GW, Pocock SJ. Comparison of propensity score methods and covariate adjustment: Evaluation in 4 cardiovascular studies. J. Am. Coll. Cardiol. 2017; 69:345357. https://doi.org./10.1016/j.jacc.2016.10.060
Wager E, Ortaliza J, Cox C. How does health spending in the U.S.compare to other countries? Perseon-KFF. 2022. https://www.healthsystemtracker.org/chart-collection/health-spending-u-s-compare-countries/#GDP%20per%20capita%20and%20health%20consumption%20spending%20per%20capita,%202020%20(U.S.%20dollars,%20PPP%20adjusted)
U.S. Population Estimated at 332,403,650 on Jan. 1, 2022. U.S. Department of Commerce. 2022. https://www.commerce.gov/news/blog/2022/01/us-population-estimated-332403650-jan-1-2022#:~:text=As%20our%20nation%20prepares%20to,since%20New%20Year%E2%80%99s%20Day%202021
Tikkanen R, Abrams MK. U.S. Health Care from a Global Perspective, 2019: Higher Spending, Worse Outcomes? The Commonwealth Fund. 2020. https://www.commonwealthfund.org/publications/issue-briefs/2020/jan/us-healthcare-global-perspective-2019
World population to reach 8 billion on 15 November 2022. United Nations. 2022. https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022
Distribution of the global population. Statista. 2022. https://www.statista.com/statistics/237584/distribution-of-the-world-population-by-continent
Cost-benefit analysis. Centers for Disease Control and Prevention. 2022. https://www.cdc.gov/policy/polaris/economics/cost-benefit/index.html
Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, Taveira F, Spadaccini M, Antonelli G, Ebigbo A, Kudo SE, Arribas J, Barua I, Kaminski MF, Messmann H, Rex DK, Dinis-Ribeiro M, Hassan C. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. The Lancet. Digital health. 2022; 4(6):e436–e444. https://doi.org/10.1016/S2589-7500(22)00042-5
Monlezun DJ, Sotomayor C, Peters N, Steigner L, Gallagher C, Garcia A. The global AI ethics of COVID-19 recovery: Narrative review and personalist social contract ethical analysis of AI-driven optimization of public health and social equities. Med. Eth. 2022; 33: 357-376. https://doi.org/10.36105/mye.2022v33n2.02
Aristotle. Metaphysics. In The Basic Works of Aristotle; McKeon, R., Ed.; ~323 B.C., bk. XII; The Modern Library: New York. 2001; Chapter 9.
Schaeffer M. Thomistic Personalism: Clarifying and Advancing the Project; York University Press: Canada. 2016.
Clarke NW. Person and Being; Marquette University Press: Milwaukee, WI, USA, 1993.
Aquinas, T. The Summa Theologica; (1274), I.5.1, I.29.3, Ia-IIae.61.2; IIi.58.3, I.44.4; Benziger Brothers: USA. 1947.
Siwicki,B.,2021. Data interoperability, knowledge interoperability and the learning health system. Healthcare IT News. https://www.healthcareitnews.com/news/data-interoperability-knowledge-interoperability-and-learning-health-system
Brooks-LaSure C. Interoperability and the connected healthcare system. Centers for Medicare & Medicaid Services. 2021. https://www.cms.gov/blog/interoperability-and-connected-health-care-system
FAO. Rome call for artificial intelligence ethics draws global interest. United Nations Food and Agriculture Organization. 2020. https://www.fao.org/newsroom/detail/Rome-Call-for-Artificial-Intelligence-ethics-draws-global-interest/en
EU, 2020. On artificial intelligence: A European approach to excellence and trust. European Union. https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf
Do D, 2020. DO D adopts ethical principles for artificial intelligence. US Department of Defense. https://www.defense.gov/News/Releases/Release/Article/2091996/dod-adopts-ethical-principles-for-artificial-intelligence
Rainie L, Anderson J, Vogels EA. Experts doubt ethical AI design will be broadly adopted as the norm within the next decade. Pew Research Center. 2021. https://www.pewresearch.org/internet/2021/06/16/experts-doubt-ethical-ai-design-will-be-broadly-adopted-as-the-norm-within-the-next-decade
Schiff G, Shojania KG. 2022. Looking back on the history of patient safety: An opportunity to reflect and ponder future challenges. BMJ Quality & Safety. 2022; 31(2):148-52.
ChuiM,Hall B,Singla A,Sukharevsky A.The state of AIin 2021.McKinsey&Co. 2022 https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021
Chui M, Hall B, Mayhew H, Singla A. The state of AI in 2022. McKinsey & Co. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
FDA. Clinical decision support software: Guidance for industry and Food and Drug Administration Staff. US Food and Drug Administration. 2022. https://www.fda.gov/media/109618