Characteristics of Patient Complaints in a Large Tertiary Hospital in China: A Longitudinal Analysis from 2022 to 2024

Background: Patient complaints provide critical insights into healthcare quality and patient experience, serving as a direct feedback mechanism for service improvement. Objective: This study aimed to characterize major complaint categories, departmental distribution, personnel attribution, and resolution effectiveness of patient complaints at a large Chinese tertiary hospital using empirical data from 2022 to 2024. Methods: A retrospective observational design was conducted on complaint records (2022: n = 187; 2023: n = 192; 2024: n = 201) at Xiangya Hospital. All data presented are empirically observed records from the hospital’s internal complaint management system; no hypothetical projections or modelled data are included. Complaints were independently categorized into six themes by two researchers (Cohen’s κ = 0.84). Resolution effectiveness was coded by the hospital’s complaints office. Descriptive statistics and chi-square tests were performed using R software. A complementary SWOT analysis was conducted to assess institutional complaint management capacity. Results: Service attitude (25.1% in 2022, 23.4% in 2023, 21.9% in 2024) and communication (20.3%, 18.8%, 17.2%) constituted the largest complaint categories, both showing significant declining trends (χ2_trend = 6.54, df = 2, p = 0.038 for communication; χ2_trend = 5.98, df = 2, p = 0.045 for service attitude). The top three departments with the highest complaint volumes were General Surgery (16.6% in 2022), Gastroenterology (12.8%), and Otolaryngology (9.6%). Seasonal analysis revealed that the third quarter (July–September) was the peak complaint period (29.4% in 2022). The overall effective resolution rate improved from 62.3% in 2022 to 67.7% in 2023 and 71.5% in 2024 (χ2_trend = 8.12, df = 2, p = 0.017). Treatment outcome complaints showed a relative increase from 18.2% in 2022 to 20.1% in 2024, while billing/administrative complaints increased from 15.0% to 17.4%. Conclusion: Systematic analysis of patient complaints effectively identifies recurrent issues and informs quality improvement strategies. A balanced focus on both “soft skills” (e.g., communication, empathy) and “hard system” factors (e.g., processes, resources) is essential. Continuous monitoring and responsive feedback mechanisms are crucial for sustaining improvements in patient safety and satisfaction.

Authors

Institutions

Publication Details

Journal
Healthcare
Published
2026-08-28
DOI
https://doi.org/10.3390/healthcare14172745
Primary Topic
Medical Malpractice and Liability Issues
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characteristics of Patient Complaints in a Large Tertiary Hospital in China: A Longitudinal Analysis from 2022 to 2024

Xuyuan Kuang, Baoxiang Wang
Healthcare
Medical Malpractice and Liability Issues
article

Characteristics of Patient Complaints in a Large Tertiary Hospital in China: A Longitudinal Analysis from 2022 to 2024

Xuyuan Kuang, Baoxiang Wang
article en

Abstract

Background: Patient complaints provide critical insights into healthcare quality and patient experience, serving as a direct feedback mechanism for service improvement. Objective: This study aimed to characterize major complaint categories, departmental distribution, personnel attribution, and resolution effectiveness of patient complaints at a large Chinese tertiary hospital using empirical data from 2022 to 2024. Methods: A retrospective observational design was conducted on complaint records (2022: n = 187; 2023: n = 192; 2024: n = 201) at Xiangya Hospital. All data presented are empirically observed records from the hospital’s internal complaint management system; no hypothetical projections or modelled data are included. Complaints were independently categorized into six themes by two researchers (Cohen’s κ = 0.84). Resolution effectiveness was coded by the hospital’s complaints office. Descriptive statistics and chi-square tests were performed using R software. A complementary SWOT analysis was conducted to assess institutional complaint management capacity. Results: Service attitude (25.1% in 2022, 23.4% in 2023, 21.9% in 2024) and communication (20.3%, 18.8%, 17.2%) constituted the largest complaint categories, both showing significant declining trends (χ2_trend = 6.54, df = 2, p = 0.038 for communication; χ2_trend = 5.98, df = 2, p = 0.045 for service attitude). The top three departments with the highest complaint volumes were General Surgery (16.6% in 2022), Gastroenterology (12.8%), and Otolaryngology (9.6%). Seasonal analysis revealed that the third quarter (July–September) was the peak complaint period (29.4% in 2022). The overall effective resolution rate improved from 62.3% in 2022 to 67.7% in 2023 and 71.5% in 2024 (χ2_trend = 8.12, df = 2, p = 0.017). Treatment outcome complaints showed a relative increase from 18.2% in 2022 to 20.1% in 2024, while billing/administrative complaints increased from 15.0% to 17.4%. Conclusion: Systematic analysis of patient complaints effectively identifies recurrent issues and informs quality improvement strategies. A balanced focus on both “soft skills” (e.g., communication, empathy) and “hard system” factors (e.g., processes, resources) is essential. Continuous monitoring and responsive feedback mechanisms are crucial for sustaining improvements in patient safety and satisfaction.

HealthcareVol. 14(17)
Central South University (CN), Xiangya Hospital Central South University (CN)
Openalex Percentile: Top 5%
Medical Malpractice and Liability Issues
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.