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The Role of Artificial Intelligence in Healthcare Quality Improvement: A Scoping Review and Critical Appraisal of Operational Efficiency, Patient Outcomes, and Implementation Challenges
Hospitals, Volume: 2, Issue: 4, Start page: 27
Swansea University Authors:
Meshach Aiwerioghene, Vivian Osuchukwu
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© 2025 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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DOI (Published version): 10.3390/hospitals2040027
Abstract
Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barrie...
| Published in: | Hospitals |
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| ISSN: | 2813-4524 |
| Published: |
MDPI AG
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70865 |
| Abstract: |
Background: Artificial Intelligence (AI) holds significant potential to enhance operational efficiency and quality in healthcare. However, despite substantial investment, its widespread, sustained implementation is limited, necessitating a thorough risk assessment to overcome current adoption barriers. Methods: This scoping review, guided by the Arksey and Malley framework, systematically mapped 13 articles published between 2019 and 2024, sourced from five major databases (including CINAHL, Medline, and PubMed). A rigorous, systematic process involving independent data charting and critical appraisal, using the Critical Appraisal Skills Programme (CASP) tool, was implemented, followed by thematic synthesis to address the research questions. Results: AI demonstrates a significant positive impact on both operational efficiency (e.g., optimised resource allocation, reduced waiting times) and patient outcomes (e.g., improved patient-centred, proactive care, and identification of readmission risks). Major implementation hurdles identified include high costs, critical data security and privacy concerns, the risk of algorithmic bias, and significant staff resistance stemming from limited understanding. Conclusions: Healthcare managers must address key challenges related to cost, bias, and staff acceptance to leverage the potential of AI fully. Strategic investments, the implementation of robust data governance frameworks, and comprehensive staff training are crucial steps for mitigating risks and creating a more efficient, patient-centred, and effective healthcare system. |
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| Keywords: |
artificial intelligence; healthcare delivery; operational efficiency; patient outcomes; predictive analytics; implementation barriers; scoping review; healthcare management; big data analytics; clinical decision support |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
The authors declare that they used no funding sources for this study. |
| Issue: |
4 |
| Start Page: |
27 |