Effectiveness of a hospital intelligent financial system based on AI and RPA
- 1Department of Financial Management, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan
Abstract
Background: Hospital financial management is facing ongoing challenges. Although artificial intelligence (AI) and robotic process automation (RPA) have shown their potential, there is still a lack of empirical evidence of the effectiveness of the integration of the two in the financial operations of hospitals. This study aims to evaluate the application effect of a set of an AI-RPA integrated intelligent financial system in a large hospital in China.
Methods: This single-center, non-randomized, uncontrolled before-and-after study was carried out in a large tertiary A general hospital in China from January to December 2025. As an intervention, the intelligent AI-RPA financial system launched in June 2025 consists of four levels: data governance, business processing, technology application and risk control. The study compared the 6 months before the system was launched and the 6 months after the system was launched, in terms of operational efficiency (daily data collection time, report generation time, and automated processing time per transaction), data quality and risk control (data docking success rate, data entry error rate, logical conflict correction rate, and risk prediction accuracy for medical insurance violations) and health-care staff satisfaction.
Results: After the system went online, the daily data collection time was shortened from 132.5 to 18.6 min ( P < 0.001); the report generation time was shortened from 58.4 to 5.8 min ( P < 0.001); the automated processing time of each transaction was reduced from 3.5 to 1.9 s ( P < 0.001). Data interfacing success rate increased from 94.2 to 99.7%, data entry error rate dropped from 3.5 to 0.6%, logic conflict correction rate rose from 78.1 to 98.5%, and risk prediction accuracy for insurance violations improved from 72.1 to 89.3%. Total staff satisfaction score improved from 30.1 to 43.1 ( P < 0.001).
Conclusion: This study provides preliminary empirical evidence that the AI-RPA integrated intelligent financial system was associated with improvements in operational efficiency, data quality, risk control, and user satisfaction in a large Chinese hospital.
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BibTeX
@article{zhao2026effectiveness,
title = {{Effectiveness of a hospital intelligent financial system based on AI and RPA}},
author = {Jindong Zhao and Jie Zhang and Licai Pan},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1945091},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1945091/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Effectiveness of a hospital intelligent financial system based on AI and RPA
AU - Jindong Zhao
AU - Jie Zhang
AU - Licai Pan
PY - 2026
DA - 2026/10/15
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1945091
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1945091/full
AB - Background: Hospital financial management is facing ongoing challenges. Although artificial intelligence (AI) and robotic process automation (RPA) have shown their potential, there is still a lack of empirical evidence of the effectiveness of the integration of the two in the financial operations of hospitals. This study aims to evaluate the application effect of a set of an AI-RPA integrated intelligent financial system in a large hospital in China. Methods: This single-center, non-randomized, uncontrolled before-and-after study was carried out in a large tertiary A general hospital in China from January to December 2025. As an intervention, the intelligent AI-RPA financial system launched in June 2025 consists of four levels: data governance, business processing, technology application and risk control. The study compared the 6 months before the system was launched and the 6 months after the system was launched, in terms of operational efficiency (daily data collection time, report generation time, and automated processing time per transaction), data quality and risk control (data docking success rate, data entry error rate, logical conflict correction rate, and risk prediction accuracy for medical insurance violations) and health-care staff satisfaction. Results: After the system went online, the daily data collection time was shortened from 132.5 to 18.6 min ( P < 0.001); the report generation time was shortened from 58.4 to 5.8 min ( P < 0.001); the automated processing time of each transaction was reduced from 3.5 to 1.9 s ( P < 0.001). Data interfacing success rate increased from 94.2 to 99.7%, data entry error rate dropped from 3.5 to 0.6%, logic conflict correction rate rose from 78.1 to 98.5%, and risk prediction accuracy for insurance violations improved from 72.1 to 89.3%. Total staff satisfaction score improved from 30.1 to 43.1 ( P < 0.001). Conclusion: This study provides preliminary empirical evidence that the AI-RPA integrated intelligent financial system was associated with improvements in operational efficiency, data quality, risk control, and user satisfaction in a large Chinese hospital.
SN - 2296-2565
ER -