Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Kirsty Morrison
DOI: https://doi.org/10.7861/fhj.2020-0258
Future Healthc J November 2021 Kirsty Morrison
AUniversity of Birmingham College of Medical and Dental Sciences, Birmingham, UK
Roles: medical student

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Tables
Attribute Description Impact on rate of adoption Relative advantage Degree to which an innovation is perceived as being better than the idea it supersedes Increases rate Compatibility Degree to which an innovation is perceived as consistent with the existing values, past experiences and needs of potential adopters Increases rate Complexity Degree to which an innovation is perceived as relatively difficult to understand and use Decreases rate Trialability Degree to which an innovation may be experimented with on a limited basis Increases rate Observability Degree to which the results of an innovation are visible to others Increases rate Group of key informants Rationale Individuals of influence Influential power and a breadth of experience in the field Individuals from royal colleges Knowledge on specialty-specific issues Individuals from governmental and regulatory bodies Knowledge on the regulation and policy aspects of AI Researchers In-depth understanding of the subject area Familiarisation with the data was achieved initially through transcription. To be immersed in the data, the researcher listened to the audio-recording of each interview, re-read the transcripts and made notes of first impressions.
Initial codes were generated. The researcher coded all transcripts, and 69 codes were identified.
Themes were searched for. Codes were collated in three prospective themes, with 11 candidate sub-themes.
Themes were reviewed. Several sub-themes merged to produce nine sub-themes within the three main themes.
Themes and sub-themes were defined and named: system, people and technology.
Finally, this paper was produced. Themes were interpreted using the diffusion of innovations theoretical lens.
Theme Sub-themes System Socio-political context of the NHS in 2020
Regulatory landscape
Fit within the puzzlePeople What actually is AI?
People powered transformation
It's not going to be a robo-docTechnology Data driven nature
Challenges ahead
EvaluationElement of diffusion of innovations theory Impact on rate of adoption Findings Relative advantage Increases rate AI offers a relative advantage by improving the working lives of clinicians
Risk of bias in AI tools reduces this relative advantage
The degree of relative advantage needed for adoption of AI in the NHS has not been agreed: absence of a gold standardCompatibility Increases rate NHS IT infrastructure may not be compatible with AI
Regulatory landscape is not compatible with AI
Certain specialties are more compatible with AI
Transferability of AI tools may be poor: they may only be compatible with a single NHS siteComplexity Decreases rate Improved language clarity around AI could reduce its perceived complexity
Education about AI could reduce its perceived complexityTrialability Increases rate High up-front costs of AI, combined with the existing financial pressures facing the NHS, limit its trialability Observability Increases rate Black box AI reduces the observability of the decision-making process Time: adopter categories n/a Some healthcare professionals will be more or less resistant to adopting AI: this reflects the five adopter categories Social system: opinion leaders Increases rate Champions could be used as facilitators of AI adoption; these reflect the opinion leaders described by Rogers
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Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Kirsty Morrison
Future Healthc J Nov 2021, 8 (3) e648-e654; DOI: 10.7861/fhj.2020-0258
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