Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research.

Autor:	Hogg HDJ; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.; The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom.; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom., Al-Zubaidy M; The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom., Keane PA; Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom.; Institute of Ophthalmology, University College London, London, United Kingdom., Hughes G; Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, United Kingdom.; University of Leicester School of Business, University of Leicester, Leicester, United Kingdom., Beyer FR; Evidence Synthesis Group, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom., Maniatopoulos G; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.; University of Leicester School of Business, University of Leicester, Leicester, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in health services [Front Health Serv] 2023 Jul 10; Vol. 3, pp. 1161822. Date of Electronic Publication: 2023 Jul 10 (Print Publication: 2023).
DOI:	10.3389/frhs.2023.1161822
Abstrakt:	Introduction: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently ( n  = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application ( n  = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected ( n  = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis ( n  = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2023 Hogg, Al-Zubaidy, Keane, Hughes, Beyer and Maniatopoulos.)
Databáze:	MEDLINE
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