Back

Digital healthcare reform and reduction of duplicate examinations and tests: a study of physicians' behavior on mutual recognition in response to clinical specialized demands and information intervention form a national pilot province in China

Song, C.; Huang, X.; Qian, S.; Yuan, C.; Liu, S.; Zhou, J.

2025-01-28 health systems and quality improvement
10.1101/2025.01.26.25321151 medRxiv
Show abstract

BackgroundTo reduce duplicate medical exams and tests for patients across different hospitals and alleviate their financial burden, Zhejiang, with the capital Hangzhou, has launched a digital healthcare reform named "Zhejiang Medical Mutual Recognition" from 2021 and become a pilot province in China. This digital healthcare reform policy has begun to be gradually implemented nationwide. This study aims to evaluate and analyze the differences in physicians behavior during the mutual recognition process across different types of hospitals, clinical specialties, and information intervention strategies, in order to provide suggestions and assistance for the optimization and improvement of nationwide mutual recognition policies. MethodsThis is a one-year multicenter study involving eight top-tier hospitals in Hangzhou, China, covering various types of hospitals, including general hospitals, traditional chinese medicine hospitals, integrated chinese and western medicine hospitals, and specialized hospitals. A set of recognition indicators, such as recognition proportion, cross-hospital recognition rate, were designed to evaluate physicians behaviors, providing a multi-dimensional perspective. Hospitals were grouped and compared based on their characteristics of their clinical specialties. The key recognition indicators among different hospitals, different specialties, and the same specialties in different hospitals were compared. The information intervention strategies were implemented in 3 hospitals to reduce the overlooked access rate and improve recognition rates through the method of information system restrictions. The remaining five hospitals, which did not implement these specific information interventions, served as the control group. ResultsThe traditional chinese medicine demonstrated a low cross-hospital precision delivery rate but a high rate of recognizing reports from other hospitals, contrasting with pediatrics. There were significant differences in recognition indicators among different clinical specialties. The total recognition proportion for the traditional chinese medicine group and the pediatrics group were 51.18% and 9.95%, respectively. The differences were not significant among the same clinical specialties in different hospitals, however, some recognition indicators were also noticeably affected by the mutual recognition management strategies of the affiliated hospitals. Information intervention in certain hospitals significantly reduced the overlooked access rate; information intervention had increased the workload for physicians in accessing duplicate reports, mainly stemming from the repetitive process of accessing reports from their own hospital both locally and on the platform, without significant affect on the main recognition indicators across hospitals. ConclusionThe recognition indicators designed in this study can effectively assess and provide decision support for mutual recognitio management. Although the treatment characteristics of clinical specialties are factors affecting mutual recognition, the management strategies implemented by hospitals can also significantly change mutual recognition. Hospital management strategies, such as information interventions, need to strike a balance between mutual recognition work and the workload of physicians accessing duplicate reports.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.