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

Clinical Informatics (1022) Focus Groups and Qualitative Research for Human Factors Research (851) mHealth in a Clinical Setting (615)

Published on in Vol 21, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13143, first published .
Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Authors of this article:

Alistair Connell1, 2 Author Orcid Image ;   Georgia Black3 Author Orcid Image ;   Hugh Montgomery1 Author Orcid Image ;   Peter Martin3 Author Orcid Image ;   Claire Nightingale3, 4 Author Orcid Image ;   Dominic King2 Author Orcid Image ;   Alan Karthikesalingam2 Author Orcid Image ;   Cían Hughes2 Author Orcid Image ;   Trevor Back2 Author Orcid Image ;   Kareem Ayoub2 Author Orcid Image ;   Mustafa Suleyman2 Author Orcid Image ;   Gareth Jones5 Author Orcid Image ;   Jennifer Cross5 Author Orcid Image ;   Sarah Stanley5 Author Orcid Image ;   Mary Emerson5 Author Orcid Image ;   Charles Merrick5 Author Orcid Image ;   Geraint Rees6 Author Orcid Image ;   Christopher Laing5 Author Orcid Image ;   Rosalind Raine3 Author Orcid Image
Article Authors Cited by (19) Tweetations (52) Metrics

Journals

  1. Sbaffi L, Walton J, Blenkinsopp J, Walton G. Information Overload in Emergency Medicine Physicians: A Multisite Case Study Exploring the Causes, Impact, and Solutions in Four North England National Health Service Trusts. Journal of Medical Internet Research 2020;22(7):e19126 View
  2. Connell A, Raine R, Martin P, Barbosa E, Morris S, Nightingale C, Sadeghi-Alavijeh O, King D, Karthikesalingam A, Hughes C, Back T, Ayoub K, Suleyman M, Jones G, Cross J, Stanley S, Emerson M, Merrick C, Rees G, Montgomery H, Laing C. Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs. Journal of Medical Internet Research 2019;21(7):e13147 View
  3. Kallenberger S, Schmidt C. Forecasting the development of acute kidney injury using a recurrent neural network. Cardiovascular Research 2019 View
  4. Li R, Asch S, Shah N. Developing a delivery science for artificial intelligence in healthcare. npj Digital Medicine 2020;3(1) View
  5. Bhatt S, Cohon A, Rose J, Majerczyk N, Cozzi B, Crenshaw D, Myers G. Interpretable Machine Learning Models for Clinical Decision-Making in a High-Need, Value-Based Primary Care Setting. NEJM Catalyst 2021;2(4) View
  6. Flores A, Demsas F, Leeper N, Ross E. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circulation Research 2021;128(12):1833 View
  7. Chew C, Hogan H, Jani Y. Scoping review exploring the impact of digital systems on processes and outcomes in the care management of acute kidney injury and progress towards establishing learning healthcare systems. BMJ Health & Care Informatics 2021;28(1):e100345 View
  8. Heijsters F, Santema J, Mullender M, Bouman M, Bruijne M, van Nassau F. Stakeholders barriers and facilitators for the implementation of a personalised digital care pathway: a qualitative study. BMJ Open 2022;12(11):e065778 View
  9. Wornow M, Gyang Ross E, Callahan A, Shah N. APLUS: A Python library for usefulness simulations of machine learning models in healthcare. Journal of Biomedical Informatics 2023;139:104319 View
  10. Shapiro M, Renly S, Maiorano A, Young J, Medina E, Neinstein A, Odisho A. Digital Health at Enterprise Scale: Evaluation Framework for Selecting Patient-Facing Software in a Digital-First Health System. JMIR Formative Research 2023;7:e43009 View
  11. Hogg H, Al-Zubaidy M, Talks J, Denniston A, Kelly C, Malawana J, Papoutsi C, Teare M, Keane P, Beyer F, Maniatopoulos G. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. Journal of Medical Internet Research 2023;25:e39742 View
  12. Atia J, Evison F, Gallier S, Hewins P, Ball S, Gavin J, Coleman J, Garrick M, Pankhurst T. Does acute kidney injury alerting improve patient outcomes?. BMC Nephrology 2023;24(1) View
  13. Harris S, Bonnici T, Keen T, Lilaonitkul W, White M, Swanepoel N. Clinical deployment environments: Five pillars of translational machine learning for health. Frontiers in Digital Health 2022;4 View
  14. Wilson M, Asselbergs F, Miguel R, Brealey D, Harris S. Embedded point of care randomisation for evaluating comparative effectiveness questions: PROSPECTOR-critical care feasibility study protocol. BMJ Open 2022;12(9):e059995 View
  15. Yi T, Laing C, Kretzler M, Nkulikiyinka R, Legrand M, Jardine M, Rossignol P, Smyth B. Digital health and artificial intelligence in kidney research: a report from the 2020 Kidney Disease Clinical Trialists (KDCT) meeting. Nephrology Dialysis Transplantation 2022;37(4):620 View
  16. Sanmarchi F, Fanconi C, Golinelli D, Gori D, Hernandez-Boussard T, Capodici A. Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. Journal of Nephrology 2023;36(4):1101 View
  17. Heijsters F, van Loon G, Santema J, Mullender M, Bouman M, de Bruijne M, van Nassau F. A usability evaluation of the perceived user friendliness, accessibility, and inclusiveness of a personalized digital care pathway tool. International Journal of Medical Informatics 2023;175:105070 View
  18. Arnold M, Goldschmitt M, Rigotti T. Dealing with information overload: a comprehensive review. Frontiers in Psychology 2023;14 View
  19. Wenk J, Voigt I, Inojosa H, Schlieter H, Ziemssen T. Building digital patient pathways for the management and treatment of multiple sclerosis. Frontiers in Immunology 2024;15 View