Evidence map›Paper›PMID 34888459›Full record

ArticleFuture healthcare journal2021

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

Kirsty Morrison

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Future healthcare journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 42 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Kirsty MorrisonUniversity of Birmingham College of Medical and Dental Sciences, Birmingham, UK.
Birmingham Dental Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAI 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

aimsI 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.

findingsA 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.

conclusionFuture 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.

Indexed as

adoptionartificial intelligencemachine learningNHS

Identifiers

PMID34888459
PMCPMC8651325
OpenAlexW3196560242

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.