This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (671)

Published on in Vol 8, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11232, first published .
The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System

Authors of this article:

Matthew Willis1 Author Orcid Image ;   Paul Duckworth2 Author Orcid Image ;   Angela Coulter3 Author Orcid Image ;   Eric T Meyer1, 4 Author Orcid Image ;   Michael Osborne2 Author Orcid Image
Article Authors Cited by (16) Tweetations (5) Metrics

Journals

  1. Willis M, Duckworth P, Coulter A, Meyer E, Osborne M. Qualitative and quantitative approach to assess the potential for automating administrative tasks in general practice. BMJ Open 2020;10(6):e032412 View
  2. Iqbal U, Celi L, Li Y. How Can Artificial Intelligence Make Medicine More Preemptive?. Journal of Medical Internet Research 2020;22(8):e17211 View
  3. Fruehwirt W, Duckworth P. Towards better healthcare: What could and should be automated?. Technological Forecasting and Social Change 2021;172:120967 View
  4. Aquino Y, Rogers W, Braunack-Mayer A, Frazer H, Win K, Houssami N, Degeling C, Semsarian C, Carter S. Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills. International Journal of Medical Informatics 2023;169:104903 View
  5. Bartlett J, Fisher E, Liikkanen S, Turunen J, Skog M, Eccleston C. The Design and Development of an Embodied Semi-Autonomous Mentoring Intelligence (SAMI) for Use in Virtual Reality Interventions, Operationalized for the Self-Management of Chronic Pain. Frontiers in Virtual Reality 2022;3 View
  6. Hung C, Shi H, Lee P, Chang C, Rau K, Lee H, Tseng C, Pei S, Tsai K, Chiu C. Potential and role of artificial intelligence in current medical healthcare. WArtificial Intelligence in Cancer 2022;3(1):1 View
  7. Hardhantyo M, Djasri H, Nursetyo A, Yulianti A, Adipradipta B, Hawley W, Mika J, Praptiningsih C, Mangiri A, Prasetyowati E, Brye L. Quality of National Disease Surveillance Reporting before and during COVID-19: A Mixed-Method Study in Indonesia. International Journal of Environmental Research and Public Health 2022;19(5):2728 View
  8. Pepito J, Acaso N, Merioles R, Ismael J. The Integration of Automation in Nursing Practice: Opportunities, Challenges, and Future Directions: Discursive Paper (Preprint). JMIR Nursing 2025 View
  9. Webster C. Service automation and practical implementation in hospitality outlets: insights from observations and interviews of employees in Bulgaria and the USA. Journal of Hospitality and Tourism Technology 2025;16(5):1143 View

Books/Policy Documents

  1. Moumtzoglou A. Quality Assurance in the Era of Individualized Medicine. View
  2. Lidströmer N, Davids J, Sood H, Ashrafian H. Artificial Intelligence in Medicine. View
  3. Mistry P. Artificial Intelligence in Medicine. View
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  5. Mistry P. Artificial Intelligence in Medicine. View
  6. Mistry P. Artificial Intelligence in Medicine. View
  7. Nithiyanandam P, Arun A, Sreemathy J, Lakshminarayanan R. Technological Tools for Predicting Pregnancy Complications. View