Literature DB >> 34888459

Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption.

Kirsty Morrison1.   

Abstract

BACKGROUND: AI has the potential to improve healthcare. However, there is limited research investigating the factors which influence the adoption of AI within a healthcare system. RESEARCH AIMS: I aimed to use innovation theory to understand the barriers and facilitators that influence AI adoption in the NHS; and to explore solutions to overcome these barriers, and examine these factors, particularly within radiology, pathology and general practice.
METHODOLOGY: Twelve semi-structured, one-to-one interviews were conducted with key informants. Interview data were analysed using thematic analysis.
FINDINGS: A range of barriers and facilitators to the adoption of AI within the NHS were identified, including IT infrastructure and language clarity. Several solutions to overcome the barriers were proposed by participants, including education strategies and innovation champions.
CONCLUSION: Future research should explore the importance of IT infrastructure in supporting AI adoption, examine the terminology around AI and explore specialty-specific barriers to AI adoption in greater depth. © Royal College of Physicians 2021. All rights reserved.

Entities:  

Keywords:  NHS; adoption; artificial intelligence; machine learning

Year:  2021        PMID: 34888459      PMCID: PMC8651325          DOI: 10.7861/fhj.2020-0258

Source DB:  PubMed          Journal:  Future Healthc J        ISSN: 2514-6645


  25 in total

1.  Assessing or predicting adoption of telehealth using the diffusion of innovations theory: a practical example from a rural program in New Mexico.

Authors:  Deborah Helitzer; Debra Heath; Kristine Maltrud; Eileen Sullivan; Dale Alverson
Journal:  Telemed J E Health       Date:  2003       Impact factor: 3.536

2.  Doctors on-line: using diffusion of innovations theory to understand internet use.

Authors:  Fiona Chew; William Grant; Rohit Tote
Journal:  Fam Med       Date:  2004-10       Impact factor: 1.756

3.  Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

Authors:  Stefano A Bini
Journal:  J Arthroplasty       Date:  2018-02-27       Impact factor: 4.757

4.  Artificial intelligence and diagnosis in general practice.

Authors:  Nick Summerton; Martin Cansdale
Journal:  Br J Gen Pract       Date:  2019-07       Impact factor: 5.386

Review 5.  High-performance medicine: the convergence of human and artificial intelligence.

Authors:  Eric J Topol
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

6.  Health Care Robotics: Qualitative Exploration of Key Challenges and Future Directions.

Authors:  Kathrin Cresswell; Sarah Cunningham-Burley; Aziz Sheikh
Journal:  J Med Internet Res       Date:  2018-07-04       Impact factor: 5.428

7.  Analysis of Gender Perceptions in Health Technology: A Call to Action.

Authors:  Lyn Denend; Stacey McCutcheon; Mike Regan; Maria Sainz; Paul Yock; Dan Azagury
Journal:  Ann Biomed Eng       Date:  2020-02-20       Impact factor: 3.934

8.  The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Authors:  Stacy M Carter; Wendy Rogers; Khin Than Win; Helen Frazer; Bernadette Richards; Nehmat Houssami
Journal:  Breast       Date:  2019-10-11       Impact factor: 4.380

9.  Deep learning in chest radiography: Detection of findings and presence of change.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Chayanin Nitiwarangkul; John A Patti; Fatemeh Homayounieh; Atul Padole; Pooja Rao; Preetham Putha; Victorine V Muse; Amita Sharma; Subba R Digumarthy
Journal:  PLoS One       Date:  2018-10-04       Impact factor: 3.240

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  1 in total

Review 1.  The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus.

Authors:  Daniele Giansanti; Francesco Di Basilio
Journal:  Healthcare (Basel)       Date:  2022-03-10
  1 in total

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