AI fairness; Socio-ethical considerations; datafication, privacy and trust; digital determinants of health; digital health equity; diversity, equity and digital inclusion; Health Policy; Health Informatics
Abstract :
[en] Digitalization in medicine offers a significant opportunity to transform healthcare systems by providing novel digital tools and services to guide personalized prevention, prediction, diagnosis, treatment and disease management. This transformation raises a number of novel socio-ethical considerations for individuals and society as a whole, which need to be appropriately addressed to ensure that digital medical devices (DMDs) are widely adopted and benefit all patients as well as healthcare service providers. In this narrative review, based on a broad literature search in PubMed, Web of Science, Google Scholar, we outline five core socio-ethical considerations in digital medicine that intersect with the notions of equity and digital inclusion: (i) access, use and engagement with DMDs, (ii) inclusiveness in DMD clinical trials, (iii) algorithm fairness, (iv) surveillance and datafication, and (v) data privacy and trust. By integrating literature from multidisciplinary fields, including social, medical, and computer sciences, we shed light on challenges and opportunities related to the development and adoption of DMDs. We begin with an overview of the different types of DMDs, followed by in-depth discussions of five socio-ethical implications associated with their deployment. Concluding our review, we provide evidence-based multilevel recommendations aimed at fostering a more inclusive digital landscape to ensure that the development and integration of DMDs in healthcare mitigate rather than cause, maintain or exacerbate health inequities.
Disciplines :
Public health, health care sciences & services
Author, co-author :
PACCOUD, Ivana ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine ; Digital Medicine Group, Department of Population Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
LEIST, Anja ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) > Socio-Economic Inequality
SCHWANINGER, Isabel ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine ; Digital Medicine Group, Department of Population Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
van Kessel, Robin; LSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK ; Mental Health Policy and Economics Group, Department of Psychiatry, University of Cambridge, Cambridge, UK ; Department of International Health, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands ; Digital Public Health Task Force, Association of Schools of Public Health in the European Region (ASPHER), Brussels, Belgium
KLUCKEN, Jochen ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Digital Medicine ; Digital Medicine Group, Department of Population Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg ; Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
External co-authors :
yes
Language :
English
Title :
Socio-ethical challenges and opportunities for advancing diversity, equity, and inclusion in digital medicine.
van Kessel R Roman-Urrestarazu A Anderson M, et al. Mapping factors that affect the uptake of digital therapeutics within health systems: scoping review. J Med Internet Res 2023; 25: e48000.
Strong K Mathers C Leeder S, et al. Preventing chronic diseases: how many lives can we save? Lancet 2005; 366: 1578–1582.
Elenko E Underwood L Zohar D. Defining digital medicine. Nat Biotechnol 2015; 33: 456–461.
The NICE Evidence Standards Framework for digital health and care technologies—Developing and maintaining an innovative evidence framework with global impact—Harriet Unsworth, Bernice Dillon, Lucie Collinson, Helen Powell, Mark Salmon, Tosin Oladapo, Lynda Ayiku, Gary Shield, Joanne Holden, Neelam Patel, Mark Campbell, Felix Greaves, Indra Joshi, John Powell, Alexia Tonnel, https://journals.sagepub.com/doi/full/10.1177/20552076211018617 (2021)
Lyles CR Wachter RM Sarkar U. Focusing on digital health equity. JAMA 2021; 326: 1795.
Grundy Q. A review of the quality and impact of mobile health apps. Annu Rev Public Health 2022; 43: 117–134.
Fahy N Williams GA Habicht T, et al. Use of digital health tools in Europe: before, during and after COVID-19. (European Observatory on Health Systems and Policies, Copenhagen (Denmark), 2021).
Rudschies C Schneider I. Ethical, legal, and social implications (ELSI) of virtual agents and virtual reality in healthcare. Soc Sci Med 2024; 340: 116483.
van den Hoven J. Value sensitive design and responsible innovation. In: Responsible innovation. John Wiley & Sons, Ltd, Hoboken, New Jersey, U.S, 2013, pp.75–83. doi: https://doi.org/10.1002/9781118551424.ch4
Braveman P Egerter S Williams DR. The social determinants of health: coming of age. Annu Rev Public Health 2011; 32: 381–398.
Greenhalgh T Thorne S Malterud K. Time to challenge the spurious hierarchy of systematic over narrative reviews? Eur J Clin Invest 2018; 48: e12931.
Collins JA Fauser BCJM. Balancing the strengths of systematic and narrative reviews. Hum Reprod Update 2005; 11: 103–104.
Marmot M. Social justice, epidemiology and health inequalities. Eur J Epidemiol 2017; 32: 537–546.
Honeyman M Maguire D Evans H, et al. Digital technology and health inequalities: a scoping review. 44, https://phw.nhs.wales/publications/publications1/digital-technology-and-health-inequalities-a-scoping-review/ (2020).
Palzes VA Chi FW Metz VE, et al. Overall and telehealth addiction treatment utilization by age, race, ethnicity, and socioeconomic status in California after COVID-19 policy changes. JAMA Health Forum 2023; 4: e231018.
Zhai Y Carney JV Hazler RJ. Policy effects of the expansion of telehealth under 1135 waivers on intentions to seek counseling services: difference-in-difference (DiD) analysis. J Couns Dev 2023; 101: 277–292.
van Kessel R Hrzic R O'Nuallain E, et al. Digital health paradox: international policy perspectives to address increased health inequalities for people living with disabilities. J Med Internet Res 2022; 24: e33819.
Adepoju OE Chae M Ojinnaka CO, et al. Utilization gaps during the COVID-19 pandemic: racial and ethnic disparities in telemedicine uptake in federally qualified health center clinics. J Gen Intern Med 2022; 37: 1191–1197.
Jaworski BK Webb Hooper M Aklin WM, et al. Advancing digital health equity: directions for behavioral and social science research. Transl Behav Med 2023; 13: 132–139.
Ernsting C Dombrowski SU Oedekoven M, et al. Using smartphones and health apps to change and manage health behaviors: a population-based survey. J Med Internet Res 2017; 19: e101.
Paccoud I Baumann M Le Bihan E, et al. Socioeconomic and behavioural factors associated with access to and use of patient electronic health records: a cross-sectional analysis of four European countries; 2020.
Sinha S Garriga M Naik N, et al. Disparities in electronic health record patient portal enrollment among oncology patients. JAMA Oncol 2021; 7: 935–937.
Greenhalgh T Wherton J Papoutsi C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017; 19: e8775.
Lythreatis S Singh SK El-Kassar A-N. The digital divide: a review and future research agenda. Technol Forecast Soc Change 2022; 175: 121359.
Eurostat. Digital economy and society statistics—households and individuals. https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Digital_economy_and_society_statistics_-_households_and_individuals#Internet_usage.
World Health Organization. Equity within digital health technology within the WHO European Region: a scoping review. Equity Digit. Health Technol. WHO Eur. Reg. Scoping Rev. (2022).
van Kessel R Wong BLH Forman R, et al. The European Health Data Space fails to bridge digital divides. Br Med J 2022; 378: e071913.
van Kessel R Wong BLH Rubinić I, et al. Is Europe prepared to go digital? Making the case for developing digital capacity: an exploratory analysis of Eurostat survey data. PLOS Digit Health 2022; 1: e0000013.
Fast N van Kessel R Humphreys K, et al. The evolution of telepsychiatry for substance use disorders during COVID-19: a narrative review. Curr Addict Rep 2023; 10: 187–197.
Ancker JS Nosal S Hauser D, et al. Access policy and the digital divide in patient access to medical records. Health Policy Technol 2017; 6: 3–11.
Veinot TC Mitchell H Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc 2018; 25: 1080–1088.
Figueroa CA Luo T Aguilera A, et al. The need for feminist intersectionality in digital health. Lancet Digit Health 2021; 3: e526–e533.
Richardson S Lawrence K Schoenthaler AM, et al. A framework for digital health equity. Npj Digit Med 2022; 5: 1–6.
Lorenc T Petticrew M Welch V, et al. What types of interventions generate inequalities? Evidence from systematic reviews. J Epidemiol Community Health 2013; 67: 190–193.
Wong BLH Maaß L Vodden A, et al. The dawn of digital public health in Europe: implications for public health policy and practice. Lancet Reg Health Eur 2022; 14: 100316.
Schwaninger I Güldenpfennig F Weiss A, et al. What do you mean by trust? Establishing shared meaning in interdisciplinary design for assistive technology. Int J Soc Robot 2021; 13: 1879–1897.
Alkire S. Why the capability approach? J Hum Dev 2005; 6: 115–135.
Simon MA de la Riva EE Bergan R, et al. Improving diversity in cancer research trials: the story of the cancer disparities research network. J Cancer Educ 2014; 29: 366–374.
Chen IY Szolovits P Ghassemi M. Can AI help reduce disparities in general medical and mental health care? AMA J Ethics 2019; 21: 167–179.
Ibrahim H Liu X Zariffa N, et al. Health data poverty: an assailable barrier to equitable digital health care. Lancet Digit Health 2021; 3: e260–e265.
Advancing equity in medical device performance—The Lancet Global Health, https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(24)00141-4/fulltext?dgcid=raven_jbs_etoc_email.
Bangash MN Hodson J Evison F, et al. Impact of ethnicity on the accuracy of measurements of oxygen saturations: a retrospective observational cohort study. eClinicalMedicine 2022; 48: 101428.
Fawzy A Wu TD Wang K, et al. Racial and ethnic discrepancy in pulse oximetry and delayed identification of treatment eligibility among patients with COVID-19. JAMA Intern Med 2022; 182: 730–738.
Ford JG Howerton MW Lai GY, et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer 2008; 112: 228–242.
Pratap A Neto EC Snyder P, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med 2020; 3: 21.
Rudrapatna VA Butte AJ. Opportunities and challenges in using real-world data for health care; 2020. doi:10.1172/JCI129197. https://www.jci.org/articles/view/129197/pdf
Stern AD Brönneke J Debatin JF, et al. Advancing digital health applications: priorities for innovation in real-world evidence generation. Lancet Digit Health 2022; 4: e200–e206.
Chen IY Pierson E Rose S, et al. Ethical machine learning in healthcare. Annu Rev Biomed Data Sci 2021; 4: 123–144.
Klucken J Krüger R Schmidt P, et al. Management of Parkinson’s disease 20 years from now: towards digital health pathways. J Park Dis 2018; 8: S85–S94.
Leist AK Klee M Kim JH, et al. Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. Sci Adv 2022; 8: eabk1942.
Obermeyer Z Powers B Vogeli C, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366: 447–453.
Chen RJ Chen TY Lipkova J, et al. Algorithm fairness in AI for medicine and healthcare. ArXiv Prepr. ArXiv211000603 (2021).
Manly JJ Jones RN Langa KM, et al. Estimating the prevalence of dementia and mild cognitive impairment in the US: the 2016 health and retirement study harmonized cognitive assessment protocol project. JAMA Neurol 2022; 79: 1242–1249.
Flanagin A Frey T Christiansen SL and AMA Manual of Style Committee. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 2021; 326: 621–627.
Cirillo D Catuara-Solarz S Morey C, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. Npj Digit Med 2020; 3: 1–11.
La M Hw N Dm G. The experience of symptoms of depression in men vs women: analysis of the National Comorbidity Survey Replication. JAMA Psychiatry 2013; 70(10): 1100–1106
Paccoud I Nazroo J Leist AK. Region of birth differences in healthcare navigation and optimisation: the interplay of racial discrimination and socioeconomic position. Int J Equity Health 2022; 21: 106.
Norheim OF Asada Y. The ideal of equal health revisited: definitions and measures of inequity in health should be better integrated with theories of distributive justice. Int J Equity Health 2009; 8: 40.
Starke G De Clercq E Elger BS. Towards a pragmatist dealing with algorithmic bias in medical machine learning. Med Health Care Philos 2021; 24: 341–349.
Vyas DA Eisenstein LG Jones DS. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. N Engl J Med 2020; 383: 874–882.
Owens K Walker A. Those designing healthcare algorithms must become actively anti-racist. Nat Med 2020; 26: 1327–1328.
Levey AS Tighiouart H Titan SM, et al. Estimation of glomerular filtration rate with vs without including patient race. JAMA Intern Med 2020; 180: 793–795.
Reddy S. Explainability and artificial intelligence in medicine. Lancet Digit Health 2022; 4: e214–e215.
Ethics and governance of artificial intelligence for health, https://www.who.int/publications-detail-redirect/9789240029200.
Amann J Blasimme A Vayena E, et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 2020; 20: 310.
Huerta EA Blaiszik B Brinson LC, et al. FAIR For AI: an interdisciplinary and international community building perspective. Sci Data 2023; 10: 487.
Chen IY Joshi S Ghassemi M. Treating health disparities with artificial intelligence. Nat Med 2020; 26: 16–17.
van Veen T Binz S Muminovic M, et al. Potential of mobile health technology to reduce health disparities in underserved communities. West J Emerg Med 2019; 20: 799–802.
Dijck Jv. Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveill Soc 2014; 12: 197–208.
Cukier K Mayer-Schoenberger V. The rise of big data: how it’s changing the way we think about the world. Foreign Aff 2013; 92: 28–40.
Lupton D. Health promotion in the digital era: a critical commentary. Health Promot Int 2015; 30: 174–183.
Kickbusch I Piselli D Agrawal A, et al. The lancet and Financial Times commission on governing health futures 2030: growing up in a digital world. Lancet 2021; 398: 1727–1776.
Braveman PA Egerter SA Mockenhaupt RE. Broadening the focus: the need to address the social determinants of health. Am J Prev Med 2011; 40: S4–S18.
Saeed SA Masters RM. Disparities in health care and the digital divide. Curr Psychiatry Rep 2021; 23: 61.
Ruckenstein M Schüll ND. The datafication of health. Annu Rev Anthropol 2017; 46: 261–278.
Hogle L. Data-intensive resourcing in healthcare. BioSocieties 2016; 11: 372–393.
Christophersen M Mørck P Langhoff TO, et al. Unforeseen challenges. In: Antona M Stephanidis C (eds) Universal access in human-computer interaction. Access to learning, health and well-being. Cham: Springer International Publishing, 2015, pp.288–299. doi: https://doi.org/10.1007/978-3-319-20684-4_28
Osborn CY Kripalani S Goggins KM, et al. Financial strain is associated with medication nonadherence and worse self-rated health among cardiovascular patients. J Health Care Poor Underserved 2017; 28: 499–513.
Zuboff S. Big other: surveillance capitalism and the prospects of an information civilization. J Inf Technol 2015; 30: 75–89.
Dwork C Roth A. The algorithmic foundations of differential privacy. Found Trends® Theor Comput Sci 2014; 9: 211–407.
Raab R Küderle A Zakreuskaya A, et al. Federated electronic health records for the European Health Data Space. Lancet Digit Health 2023; 5: e840–e847.
Hale TM Kvedar JC. Privacy and security concerns in telehealth. AMA J Ethics 2014; 16: 981–985.
Bächle TC Wernick A. The futures of eHealth: social, ethical and legal challenges. Berlin, Germany: Alexander von Humboldt Institute for Internet and Society, 2019.
Adjekum A Blasimme A Vayena E. Elements of trust in digital health systems: scoping review. J Med Internet Res 2018; 20: e11254.
Rodriguez JA Clark CR Bates DW. Digital health equity as a necessity in the 21st century cures act era. JAMA 2020; 323: 2381.
LaVeist TA Nickerson KJ Bowie JV. Attitudes about racism, medical mistrust, and satisfaction with care among African American and white cardiac patients. Med Care Res Rev 2000; 57: 146–161.
Wissinger C. Privacy literacy: from theory to practice. Commun Inf Lit 2017; 11: 378–389.
van Kessel R Wong BLH Clemens T, et al. Digital health literacy as a super determinant of health: more than simply the sum of its parts. Internet Interv 2022; 27: 100500.
Li X. Understanding eHealth literacy from a privacy perspective: eHealth literacy and digital privacy skills in American disadvantaged communities. Am Behav Sci 2018; 62: 1431–1449.