De la interoperabilidad de datos a la “interoperabilidad moral” en la arquitectura mundial de datos sanitarios: caso de uso integrado de análisis ético computacional impulsado por IA con puntuación de propensión bayesiana y análisis de costos y beneficios
Contenido principal del artículo
Resumen
El aumento de los costos sanitarios y financieros de las enfermedades, las discapacidades y las disparidades respalda la aceleración mundial de los intereses y las inversiones en IA (inteligencia artificial) sanitaria para lograr soluciones sanitarias mejores, más baratas, más rápidas y justas a escala mundial y local. Sin embargo, no existe un consenso sobre la aplicación práctica de los principios de la IA responsable en diversos sectores, estados y sistemas de creencias de todo el mundo. Este estudio de prueba de concepto utiliza el marco ético pluralista global (el Contrato Social Personalista) para proporcionar, por tanto, el primer análisis conocido de ética computacional (AiCE, por sus siglas en inglés) y política basado en IA aumentada bayesiana que integra análisis clínicos, de rentabilidad y de disparidades en la atención sanitaria con datos representativos a nivel nacional para estimar el costo global de las disparidades en la atención sanitaria en la colonoscopia (CS, por sus siglas en inglés) y el ahorro de la CS habilitada por IA para reducirlas. El estudio sugiere que revertir las disparidades raciales, en particular entre hispanos y asiáticos, puede ahorrar a los sistemas sanitarios estadounidenses 17.610 millones de dólares al año, con un ahorro potencial de 625,40 millones de dólares para los hispanos y 289 millones de dólares para los asiáticos en particular (con un ahorro similar para las comunidades vulnerables en países de ingresos medios y bajos). Los resultados anteriores respaldan el imperativo de ahorro de costos que supone la inversión estratégica y de capacitación en estas medidas impulsadas por la IA para mejorar los objetivos estratégicos de sostenibilidad, eficacia, eficiencia y equidad (SEEE) de la atención sanitaria. Estos resultados empíricos informan el argumento bioético global más amplio de las dimensiones gemelas de la dignidad y la seguridad humanas (arraigadas en el relato personalista, multicultural y metafísico de la persona como miembro de la familia humana global) para destacar el imperativo ético de la IA para optimizar el rendimiento del ecosistema sanitario digital global. Semejante fin instrumental es un medio decisivo para avanzar hacia el fin último del bien común, en el que se salvaguarda el bien individual de cada persona y en el que éste encuentra su realización trabajando hacia él.
Descargas
PLUMX Metrics
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Medicina y Ética se distribuye bajo Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
El autor conserva los derechos patrimoniales sin restricciones y garantiza a la revista el derecho de ser la primera publicación del trabajo. El autor es libre de depositar la versión publicada en cualquier otro medio, como un repositorio institucional o en su propio sitio web.
Citas
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