The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Health information technology and digital innovation for national learning health and care systems
Open accessAbstract
Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.
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Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Docherty, A. B., Harrison, E. M., Green, C. A., Hardwick, H. E., Pius, R., Norman, L., Holden, K. A., Read, J. M., Dondelinger, F., Carson, G., et al. (2020) · BMJ · doi:10.1136/bmj.m1985 · Cited by 3,481
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Real-time tracking of self-reported symptoms to predict potential COVID-19
Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., Ganesh, S., Varsavsky, T., Cardoso, M. J., El-Sayed Moustafa, J. S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0916-2 · Cited by 1,517
Video consultations for covid-19
Greenhalgh, T., Wherton, J., Shaw, S., & Morrison, C. (2020) · BMJ · doi:10.1136/bmj.m998 · Cited by 866 · cited 1×
“…to COVID-19 were made available, 18 the scarcity of available and appropriate audiovisual infrastructure and software, and corresponding training, 19 required the NHS to recom mend the use of any proprietary software that health-care providers were comfortable with, 20 rather than a consistent…”
BackgroundFriedberg, M., Chen, P., Van Busum, K., Aunon, F., Pham, C., Caloyeras, J., Mattke, S., Pitchforth, E., Quigley, D., Brook, R., et al. (2014) · RAND Health Quarterly · doi:10.7249/rhq-03-04-01 · Cited by 707
The Randomized Registry Trial — The Next Disruptive Technology in Clinical Research?
Lauer, M. S., & D'Agostino, R. B. (2013) · New England Journal of Medicine · doi:10.1056/nejmp1310102 · Cited by 595
Cited by
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Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Del Prato, S., Mathieu, C., Mingrone, G., et al. (2022) · Diabetologia · doi:10.1007/s00125-022-05787-2 · Cited by 1,048
Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.008 · Cited by 716
Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10251-y · Cited by 191
Data science, artificial intelligence and the third wave of digital era governance
Dunleavy, P., & Margetts, H. (2023) · doi:10.31219/osf.io/f3rza · Cited by 186
Metadata
- Authors
- Sheikh, A., Anderson, M., Albala, S., Casadei, B., Franklin, B. D., Richards, M., Taylor, D., Tibble, H., & Mossialos, E.
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 3
- Issue
- 6
- Pages
- e383-e396
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W3158602089
- Cited by
- 389
- References
- 61
- FWCI
- 47.72
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sheikh, A., Anderson, M., Albala, S., Casadei, B., Franklin, B. D., Richards, M., Taylor, D., Tibble, H., & Mossialos, E. (2021). Health information technology and digital innovation for national learning health and care systems. The Lancet Digital Health. https://doi.org/10.1016/s2589-7500(21)00005-4
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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