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

Kirsty Morrison
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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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  • For correspondence: kirsty_morrison@aol.co.uk
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    Table 1.

    Further information on the diffusion of innovations framework22

    AttributeDescriptionImpact on rate of adoption
    Relative advantageDegree to which an innovation is perceived as being better than the idea it supersedesIncreases rate
    CompatibilityDegree to which an innovation is perceived as consistent with the existing values, past experiences and needs of potential adoptersIncreases rate
    ComplexityDegree to which an innovation is perceived as relatively difficult to understand and useDecreases rate
    TrialabilityDegree to which an innovation may be experimented with on a limited basisIncreases rate
    ObservabilityDegree to which the results of an innovation are visible to othersIncreases rate
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    Table 2.

    Key informants sought and rationale

    Group of key informantsRationale
    Individuals of influenceInfluential power and a breadth of experience in the field
    Individuals from royal collegesKnowledge on specialty-specific issues
    Individuals from governmental and regulatory bodiesKnowledge on the regulation and policy aspects of AI
    ResearchersIn-depth understanding of the subject area
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    Box 1.

    Description of data analysis

    • 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.

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    Table 3.

    Themes and sub-themes

    ThemeSub-themes
    SystemSocio-political context of the NHS in 2020
    Regulatory landscape
    Fit within the puzzle
    PeopleWhat actually is AI?
    People powered transformation
    It's not going to be a robo-doc
    TechnologyData driven nature
    Challenges ahead
    Evaluation
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    Table 4.

    Key findings mapped to the diffusion of innovations framework

    Element of diffusion of innovations theoryImpact on rate of adoptionFindings
    Relative advantageIncreases rateAI 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 standard
    CompatibilityIncreases rateNHS 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 site
    ComplexityDecreases rateImproved language clarity around AI could reduce its perceived complexity
    Education about AI could reduce its perceived complexity
    TrialabilityIncreases rateHigh up-front costs of AI, combined with the existing financial pressures facing the NHS, limit its trialability
    ObservabilityIncreases rateBlack box AI reduces the observability of the decision-making process
    Time: adopter categoriesn/aSome healthcare professionals will be more or less resistant to adopting AI: this reflects the five adopter categories
    Social system: opinion leadersIncreases rateChampions 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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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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