Dai, Siqi;
Xie, Zhiyi;
Yang, Zheshuai;
Miao, Wei;
(2025)
The paradox of AI assistance: enhancing quality while hindering efficiency in local hospitals.
Journal of Digital Management
, 1
, Article 8. 10.1007/s44362-025-00009-2.
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Abstract
Artificial intelligence (AI) is transforming the medical industry, with AI applications in healthcare expanding across clinical domains. By 2025, medical AI is expected to be adopted in 90% of hospitals to support doctors’ work. Although AI has demonstrated proven capabilities in enhancing medical diagnosis and treatment efficacy, there remains a lack of in-depth research on its impact on doctors’ work, particularly for doctors with average qualifications in small-scale hospitals. Through an analysis of chest CT diagnostic data from a local hospital in China, our analysis reveals that after the introduction of AI assistance, doctors’ work quality improved, as evidenced by a 2.8% increase in the length of report conclusions and a 1.0% increase in the description length. However, work efficiency declined, with the average number of chest CT reports processed daily reduced by 4.3% for the overall department and 2.8% per doctor. Notably, over a six-month period following the adoption of AI, this trade-off became increasingly significant. Understanding the impact of AI assistance on doctors’ work performance is crucial for optimizing healthcare resource allocation and management decisions, ultimately enhancing patient satisfaction and well-being. This study redirects attention from patient perceptions to clinician behaviors, offering actionable insights for AI implementation in small-scale hospitals.
| Type: | Article |
|---|---|
| Title: | The paradox of AI assistance: enhancing quality while hindering efficiency in local hospitals |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s44362-025-00009-2 |
| Publisher version: | https://doi.org/10.1007/s44362-025-00009-2 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Artificial intelligence (AI) assistance, Doctors’ work performance, Work efficiency, Work quality, Efficiency-quality trade-off |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215402 |
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