Health data poverty: an assailable barrier to equitable digital health care

The Lancet Digital Health - Tập 3 - Trang e260-e265 - 2021
Hussein Ibrahim1,2,3, Xiaoxuan Liu1,2,3,4, Nevine Zariffa5, Andrew D Morris4, Alastair K Denniston1,2,3,4,6
1Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, UK
2University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
3Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
4Health Data Research UK, London, UK
5NMD Group, Bala Cynwyd, PA, USA
6NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK

Tài liệu tham khảo

Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 44, 10.1038/s41591-018-0300-7 Chen, 2020, Ethical machine learning in health care, ArXiv Veinot, 2018, Good intentions are not enough: how informatics interventions can worsen inequality, J Am Med Inform Assoc, 25, 1080, 10.1093/jamia/ocy052 Lee, 2020, Big data in context: addressing the twin perils of data absenteeism and chauvinism in the context of health disparities research, J Med Internet Res, 22, 10.2196/16377 Ferryman Wiens, 2019, Do no harm: a roadmap for responsible machine learning for health care, Nat Med, 25, 1337, 10.1038/s41591-019-0548-6 Ghassemi, 2019, Practical guidance on artificial intelligence for health-care data, Lancet Digit Health, 1, e157, 10.1016/S2589-7500(19)30084-6 Safran, 2007, Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper, J Am Med Inform Assoc, 14, 1, 10.1197/jamia.M2273 Sirugo, 2019, The missing diversity in human genetic studies, Cell, 177, 26, 10.1016/j.cell.2019.02.048 Fry, 2017, Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population, Am J Epidemiol, 186, 1026, 10.1093/aje/kwx246 Kaushal, 2020, Geographic distribution of US cohorts used to train deep learning algorithms, JAMA, 324, 1212, 10.1001/jama.2020.12067 Khan, 2020, A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability, Lancet Digit Health, 3, E51, 10.1016/S2589-7500(20)30240-5 Sheng Fernandez, 2008, Ethical issues in health research in children, Peadiatr Child Health, 13, 707, 10.1093/pch/13.8.707 Holdcroft, 2007, Gender bias in research: how does it affect evidence based medicine?, J R Soc Med, 100, 2, 10.1177/014107680710000102 Blehar, 2013, Enrolling pregnant women: issues in clinical research, Womens Health Issues, 23, e39, 10.1016/j.whi.2012.10.003 Redwood, 2013, Under-representation of minority ethnic groups in research—call for action, Br J Gen Pract, 63, 342, 10.3399/bjgp13X668456 Witham, 2007, How to get older people included in clinical studies, Drugs Aging, 24, 187, 10.2165/00002512-200724030-00002 Whyte, 2018, The normal range: it is not normal and it is not a range, Postgrad Med J, 94, 613, 10.1136/postgradmedj-2018-135983 Tomašev, 2019, A clinically applicable approach to continuous prediction of future acute kidney injury, Nature, 572, 116, 10.1038/s41586-019-1390-1 Adamson, 2018, Machine learning and health care disparities in dermatology, JAMA Dermatol, 154, 1247, 10.1001/jamadermatol.2018.2348 Buster, 2012, Dermatologic health disparities, Dermatol Clin, 30, 53, 10.1016/j.det.2011.08.002 Dawes, 2016, Racial disparities in melanoma survival, J Am Acad Dermatol, 75, 983, 10.1016/j.jaad.2016.06.006 Veinot, 2019, Health informatics and health equity: improving our reach and impact, J Am Med Inform Assoc, 26, 689, 10.1093/jamia/ocz132 Holland, 2018, The dataset nutrition label: a framework to drive higher data quality standards, ArXiv Gebru, 2020, Datasheets for datasets, ArXiv Sendak, 2020, Presenting machine learning model information to clinical end users with model facts labels, NPJ Digit Med, 3, 41, 10.1038/s41746-020-0253-3 Mitchell, 2019, Model cards for model reporting, ArXiv 2009 Kalkman, 2019, Patients' and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence, J Med Ethics, 10.1136/medethics-2019-105651 Sheridan, 2020, Why do patients take part in research? An overview of systematic reviews of psychosocial barriers and facilitators, Trials, 21, 259, 10.1186/s13063-020-4197-3 Mello, 2018, Clinical trial participants' view of the risks and benefits of data sharing, N Engl J Med, 378, 2201, 10.1056/NEJMsa1713258 Majeed, 2020 Menni, 2020, Quantifying additional COVID-19 symptoms will save lives, Lancet, 395, e107, 10.1016/S0140-6736(20)31281-2 Menni, 2020, Widespread smell testing for COVID-19 has limited application – authors' reply, Lancet, 396, 1630, 10.1016/S0140-6736(20)32316-3 Crawford, 2020, Digital health equity and COVID-19: the innovation curve cannot reinforce the social gradient of health, J Med Internet Res, 22, 10.2196/19361