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2015年至2022年上海患者投诉的时空分析

Spatial-temporal analysis of patient complaints in Shanghai from 2015 to 2022.

作者信息

Wang Gan, Wu Chaowei, Yao Ye, Zhang Tiantian

机构信息

Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200032, China.

School of Public Health, Fudan University, Shanghai, 200032, China.

出版信息

BMC Health Serv Res. 2025 Feb 25;25(1):311. doi: 10.1186/s12913-025-12350-3.

DOI:10.1186/s12913-025-12350-3
PMID:40001037
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11863805/
Abstract

OBJECTIVE

Patient complaints are crucial for improving the service quality of hospitals and healthcare professionals as well as enhancing patient satisfaction. The 12320 and 12345 hotlines in Shanghai are key platforms for receiving public health complaints. Understanding the temporal and spatial changes in the growth rate of patient complaints in Shanghai is essential for resource allocation and service quality improvement.

METHODS

This retrospective study investigated the spatial distribution characteristics of the growth rate of patient complaints in Shanghai's sub-districts from 2015 to 2022, involving a total of 326,147 patient complaint cases. We used ArcGIS 10.2 to analyze the annual trends in complaint volume, perform spatial autocorrelation analysis, hot-spot analysis (Getis-Ord Gi*), and content analysis based on the Healthcare Complaint Analysis Tool (HCTA).

RESULTS

The volume of patient complaints showed a continuous upward trend from 2015 to 2022. The Moran's Index for the two periods of 2015-2019 and 2015-2022 was 0.020 (P = 0.036) and 0.045 (P = 0.001), respectively. Patient complaints exhibited strong spatial clustering effects in certain street areas, particularly in the western suburbs, where the growth rate of complaints was more severe than in central urban areas. The top three types of complaints in focus areas in 2022 were environment, safety, and institutional processes, accounting for 45.93%, 13.73%, and 18.24%, respectively. Hotspot areas were concentrated in the western and southwestern parts of Shanghai, with the main complaint stages occurring during admission, examination and diagnosis, and surgery and procedures.

CONCLUSION

This study explored the methodological approach to studying the temporal and spatial distribution of patient complaints. Shanghai should focus on healthcare complaints from the western and southwestern suburbs. Future resource allocation and service quality improvement should target these high-complaint regions and frequently complained issues to enhance patient satisfaction and overall healthcare quality.

摘要

目的

患者投诉对于提高医院和医护人员的服务质量以及提升患者满意度至关重要。上海的12320和12345热线是接收公共卫生投诉的关键平台。了解上海患者投诉增长率的时空变化对于资源分配和服务质量提升至关重要。

方法

本回顾性研究调查了2015年至2022年上海各街道患者投诉增长率的空间分布特征,共涉及326,147例患者投诉案例。我们使用ArcGIS 10.2分析投诉量的年度趋势,进行空间自相关分析、热点分析(Getis-Ord Gi*),并基于医疗投诉分析工具(HCTA)进行内容分析。

结果

2015年至2022年患者投诉量呈持续上升趋势。2015 - 2019年和2015 - 2022年两个时期的莫兰指数分别为0.020(P = 0.036)和0.045(P = 0.001)。患者投诉在某些街道区域呈现出强烈的空间聚集效应,特别是在西郊,那里的投诉增长率比中心城区更为严重。2022年重点区域投诉量排名前三的类型是环境、安全和机构流程,分别占45.93%、13.73%和18.24%。热点区域集中在上海的西部和西南部,主要投诉阶段发生在入院、检查诊断以及手术和操作期间。

结论

本研究探索了研究患者投诉时空分布的方法。上海应关注西郊和西南郊的医疗投诉。未来的资源分配和服务质量提升应针对这些高投诉地区和频繁投诉的问题,以提高患者满意度和整体医疗质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/cd9e6e99e05b/12913_2025_12350_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/dc2e7f1fe992/12913_2025_12350_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/b3323128d340/12913_2025_12350_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/83a24cd016e1/12913_2025_12350_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/4e881d61b094/12913_2025_12350_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/cd9e6e99e05b/12913_2025_12350_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/dc2e7f1fe992/12913_2025_12350_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/b3323128d340/12913_2025_12350_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/83a24cd016e1/12913_2025_12350_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/4e881d61b094/12913_2025_12350_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f27a/11863805/cd9e6e99e05b/12913_2025_12350_Fig5_HTML.jpg

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