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25 October 2025, Volume 32 Issue 5
    

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  • MaChongqi, GuChengsheng, KangZhou
    Chinese Journal of Hospital Statistics. 2025, 32(5): 321-325. https://doi.org/10.3969/j.issn.1006-5253.2025.05.001
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    Objective To analyze the early warning value of glycated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hs-CRP) in the differential diagnosis of type 2 diabetic proliferative retinopathy (PDR). Methods A total of 338 patients with diabetic retinopathy (DR) discharged from hospital between 2019 and 2023 were selected as research subjects. According to the coding rules of the International Classification of Diseases, 10th Revision (ICD-10), they were divided into two groups: the type 2 diabetic non-proliferative retinopathy (NPDR) group (230 cases) and the type 2 diabetic proliferative retinopathy (PDR) group (108 cases).A PDR prediction model was established using logistic regression. The receiver operating characteristic (ROC) curve was adopted to analyze the cut-off points and early warning values of HbA1c and hs-CRP for PDR screening, respectively. Additionally, the decision curve analysis (DCA) was used to compare the fitting effect differences between the initial model and the final model after adding HbA1c and hs-CRP. Results Independent samples t-test results showed that the levels of HbA1c and hs-CRP in the PDR group were significantly higher than those in the type 2 diabetic non-proliferative retinopathy group (P<0.01). ROC curve analysis indicated that the cut-off points of HbA1c and hs-CRP for PDR diagnosis were 9.510% and 1.675 mg/L, respectively, with the areas under the ROC curve (AUC) reaching 0.826 and 0.929 (P<0.01). DCA results revealed that the net benefit of the final model established after including HbA1c and hs-CRP was higher than that of the initial model. The clinical impact curve (CIC) showed that when the threshold probability (Pt) > 0.4, the actual distribution of the final model was close to its predicted distribution, and the fitting degree was significantly improved. Logistic regression results of the final model demonstrated that both HbA1c (OR=6.052, 95% CI: 2.745–13.346) and hs-CRP (OR=1.835, 95% CI: 1.001–3.367) were risk factors for PDR (P<0.05). Conclusion HbA1c and hs-CRP have high early warning value for PDR and are expected to become auxiliary indicators for clinical early warning of DR lesions and differential diagnosis of PDR.
  • GanTing, ZhanShang, ShiXinxin, LiuYuan
    Chinese Journal of Hospital Statistics. 2025, 32(5): 326-330. https://doi.org/10.3969/j.issn.1006-5253.2025.05.002
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    Objective To explore the nomogram prediction model for lung cancer patients complicated with lower respiratory tract infection.Methods The data of lung cancer patients admitted to a cancer hospital in Jiangxi Province from July 2020 to July 2023 were analyzed retrospectively. Logistic regression was used to analyze the risk factors for lower respiratory tract infection in lung cancer patients, and R software was applied to construct a nomogram prediction model for the risk factors of lower respiratory tract infection in lung cancer patients.Results Among 200 lung cancer patients, 118 were complicated with lower respiratory tract infection. Multivariate logistic regression analysis showed that ≥3 underlying diseases, combined chemotherapeutic drugs, clinical stage (Stage III-IV), length of hospital stay ≥20 days, invasive procedures, and abuse of antibacterial drugs were independent risk factors for lower respiratory tract infection in lung cancer patients (P < 0.05). The calibration curve of the nomogram prediction model was close to the original curve and the ideal curve, with a C-index of 0.895 (95% CI: 0.851-0.938), indicating a high degree of model fit. When the threshold of the nomogram prediction model was > 0.17, it could provide clinical net benefits.Conclusion The occurrence of lower respiratory tract infection in lung cancer patients is related to factors such as the number of underlying diseases, combined use of chemotherapeutic drugs, and clinical stage (Stage III-IV). Constructing a personalized nomogram prediction model with these factors as predictors is helpful for the predictive assessment of lower respiratory tract infection in lung cancer patients.
  • ZhouMiaoying, ZhangLihua, LianYamei, GuanGuibo
    Chinese Journal of Hospital Statistics. 2025, 32(5): 331-335. https://doi.org/10.3969/j.issn.1006-5253.2025.05.003
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    Objective To explore the impact of pharmaceutical care under the physician-pharmacist collaboration model on disease control and health behaviors in elderly patients with chronic diseases in the community. Methods A total of 110 elderly patients with chronic diseases in the community were selected and randomly divided into two groups. Patients in the control group were provided with routine medication consultation and guidance, while those in the observation group received pharmaceutical care intervention under the physician-pharmacist collaboration model. The disease control status and health behaviors of patients in the two groups were compared. Results After the intervention, the compliance rates of blood glucose, blood lipid, and blood pressure reaching the standard in the observation group were higher than those in the control group (P<0.05); the rate of good medication adherence in the observation group was higher than that in the control group (P<0.05); and the completion rates of health behaviors such as abiding by the doctor's advice for medication, regular reexamination, healthy diet, and regular work and rest in the observation group were higher than those in the control group (P<0.05). Conclusions For elderly patients with chronic diseases in the community, the application of pharmaceutical care under the physician-pharmacist collaboration model for nursing intervention can effectively control the disease, improve health behaviors, and enhance medication adherence.
  • WuQiuxia
    Chinese Journal of Hospital Statistics. 2025, 32(5): 336-341. https://doi.org/10.3969/j.issn.1006-5253.2025.05.004
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    Objective: Based on the Actor-Partner Interdependence Model (APIM), this study aims to analyze the relationships between dyadic coping, self-fatigue regulation ability, and quality of life in maintenance hemodialysis (MHD) patients and their caregivers, so as to provide a theoretical basis for clinical interventions. Method: A total of 150 pairs of MHD patients and their caregivers admitted to a hospital from January 2023 to February 2024 were selected as research subjects. General information questionnaires, Dyadic Coping Scale, Self-Regulation Fatigue Scale, and Quality of Life Scale were used for investigation. The collected data were analyzed using SPSS 25.0, and AMOS 24.0 software was used to construct the Actor-Partner Interdependence Model of dyadic coping, self-fatigue regulation ability, and quality of life. Result: The results of stratified chi-square test showed that males aged 50 years, males and females with low educational levels, married males and females, and those with a per capita monthly household income of less than 3,000 yuan were risk factors for MHD patients and caregivers, with statistically significant differences (P < 0.05). The self-fatigue regulation ability of MHD patients was higher than that of their caregivers, while their dyadic coping and quality of life were lower than those of their caregivers, with statistically significant differences (P < 0.05). Pearson correlation analysis showed that dyadic coping was negatively correlated with self-fatigue regulation ability and positively correlated with quality of life in both MHD patients and their caregivers (P < 0.05). In terms of actor effects, dyadic coping and self-fatigue regulation ability of both MHD patients and their caregivers could predict their own quality of life, showing a positive correlation (b = 0.681, 0.623, 0.604, 0.649, P < 0.001). In terms of partner effects, dyadic coping and self-fatigue regulation ability of MHD patients could predict the quality of life of caregivers, and dyadic coping and self-fatigue regulation ability of caregivers could also predict the quality of life of MHD patients, all showing a positive correlation (b = 0.623, 0.561, 0.604, 0.628, all P < 0.001). Conclusion: The quality of life of MHD patients is comprehensively affected by themselves and their caregivers. Paying attention to the interactive effects of dyadic coping level and self-fatigue regulation ability between MHD patients and their caregivers can improve the quality of life of both MHD patients and their caregivers.
  • ChenYing, CheLiping, YuanDalu
    Chinese Journal of Hospital Statistics. 2025, 32(5): 342-347. https://doi.org/10.3969/j.issn.1006-5253.2025.05.005
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    Objective: To construct a WeChat-based remote rehabilitation nursing program for patients with Alzheimer's disease and evaluate its effect, so as to provide a reference for the rehabilitation nursing of Alzheimer's disease. Method: From June 2022 to June 2024, based on literature analysis, two rounds of expert letter consultations were conducted to revise the items and construct the WeChat-based remote rehabilitation nursing program; the program was initially applied in 300 patients with Alzheimer's disease, and its effect was evaluated. Result: The effective recovery rate of the two rounds of expert letter consultation questionnaires was 100%. The expert authority coefficient was 0.88, the coefficient of variation was 0.05-0.21, and the Kendall's coordination coefficients were 0.211 and 0.095 respectively. After the application of the program, the patients' depression level, quality of life and language function were all improved compared with those before the application, and the differences were statistically significant (P < 0.05). Conclusion: The WeChat-based remote rehabilitation nursing program for patients with Alzheimer's disease constructed in this study has good scientificity and practicability, and can provide a reference for the implementation of rehabilitation nursing for patients with Alzheimer's disease.
  • TengHaiyan, FangYanqin
    Chinese Journal of Hospital Statistics. 2025, 32(5): 348-354. https://doi.org/10.3969/j.issn.1006-5253.2025.05.006
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    Objective To explore the influencing factors of medication adherence in female patients with schizophrenia in the recovery period and construct a risk prediction model. Methods A total of 140 female patients with schizophrenia in the recovery period admitted to Hengfeng Branch of Shangrao Third People's Hospital from April 2022 to October 2023 were selected as the modeling set, and another 60 female patients with schizophrenia in the recovery period admitted from November 2023 to October 2024 were selected as the validation set. The Morisky Medication Adherence Scale was used to assess the patients' medication adherence. Patient data were collected, and univariate analysis and multivariate logistic regression analysis were used to screen the independent influencing factors of patients' medication adherence. R software was used to draw a nomogram. Calibration curves were used to evaluate the consistency of the model; ROC curves were used to evaluate the predictive efficacy of the model; and the Hosmer-Lemeshow (H-L) test was used to judge the goodness of fit of the model. Results Among the 140 patients in the modeling set, 67 cases (47.86%) had poor medication adherence. Univariate analysis showed that age, educational level, personal monthly income, course of disease, disease awareness, social support, and family care were influencing factors of medication adherence (P < 0.05). Multivariate logistic regression analysis showed that age, educational level, personal monthly income, disease awareness, social support, and family care were independent influencing factors of medication adherence. A risk prediction model was constructed based on the above 6 factors. For the modeling set, the area under the ROC curve (AUC) was 0.836 (95% CI: 0.768-0.903), with a sensitivity of 89.6% and a specificity of 69.9%. The calibration curve of the prediction model for the modeling set was close to the standard curve, indicating good consistency of the model. The results of the H-L goodness-of-fit test showed χ² = 11.323 and P = 0.184. For the external validation set, the AUC was 0.990 (95% CI: 0.972-1.000), with a sensitivity of 96.4% and a specificity of 96.9%. Conclusion Age, educational level, personal monthly income, disease awareness, social support, and family care are influencing factors of medication adherence. The risk prediction model constructed based on these factors has good clinical predictive value.
  • Liu Qing, Zhang Yue, Li Zenghua, Qi Wenfang, Liu Xia
    Chinese Journal of Hospital Statistics. 2025, 32(5): 355-359. https://doi.org/10.3969/j.issn.1006-5253.2025.05.007
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    Objective To analyze the correlation between psychological resilience and job burnout of nursing staff in the rehabilitation department. Methods From February 26 to May 31, 2024, 102 nursing staff engaged in nursing work in the rehabilitation department were selected as the research subjects. A general information questionnaire, Connor-Davidson Resilience Scale (CD-RISC), and Maslach Burnout Inventory-General Survey (MBI-GS) were used for the survey. Spearman correlation analysis (rs value) was applied to explore the correlation between psychological resilience and job burnout of the nursing staff. Results Among the 102 nursing staff in the rehabilitation department, the total score of CD-RISC (psychological resilience) was 69 (64, 81) [median (interquartile range)], and the total score of MBI-GS (job burnout) was 3 (3, 4) [median (interquartile range)]. The total score of CD-RISC and its dimensions were negatively correlated with the total score of MBI-GS and its dimensions (P < 0.05). Conclusion Psychological resilience of nursing staff in the rehabilitation department is negatively correlated with their job burnout.
  • LuoJun, MeiChengyan
    Chinese Journal of Hospital Statistics. 2025, 32(5): 360-364. https://doi.org/10.3969/j.issn.1006-5253.2025.05.008
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    Objective To study the influencing factors of in-hospital hemorrhagic transformation in elderly patients with acute ischemic stroke undergoing intravenous thrombolysis, construct a risk prediction model, and explore related countermeasures for early warning and prevention.Methods A total of 195 elderly patients with acute ischemic stroke undergoing intravenous thrombolysis from May 2018 to May 2023 were selected retrospectively as research subjects. They were divided into non-hemorrhagic transformation group (140 cases) and hemorrhagic transformation group (55 cases) according to whether hemorrhagic transformation occurred. General data of patients were collected for comparison; a hemorrhagic transformation prediction model was constructed via multivariate logistic regression analysis, and the receiver operating characteristic (ROC) curve was plotted to analyze the predictive efficacy of the model.Results There were statistically significant differences between the two groups in age, presence of hyperlipidemia, presence of atrial fibrillation, time from onset to intravenous thrombolysis, admission NIHSS score, NLR, FPG level, and family history of stroke (P < 0.05). Multivariate logistic regression analysis identified 7 independent risk factors (P < 0.05). For the prediction model, the Hosmer-Lemeshow test showed χ² = 6.536 and P = 0.587. Nomogram validation indicated that the model had an AUC of 0.909 (good discrimination), with a maximum Youden index of 0.659, sensitivity of 0.909, and specificity of 0.750. The theoretical and actual values of the calibration curve showed good consistency.Conclusion The Nomogram model for in-hospital hemorrhagic transformation in elderly patients with acute ischemic stroke undergoing intravenous thrombolysis has good predictive value. It can provide a reference for early screening of high-risk groups and further help formulate more accurate prevention and treatment plans. 
  • Zhu Li, Li Shuhui, Yan Xiaojing
    Chinese Journal of Hospital Statistics. 2025, 32(5): 365-371. https://doi.org/10.3969/j.issn.1006-5253.2025.5.009
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    Objective To construct a prediction model for prolonged hospitalization in patients with acute pancreatitis (AP) based on bedside simple indicators.Methods A total of 240 AP patients admitted to the Department of Gastroenterology of a certain hospital of integrated traditional Chinese and Western medicine from January 2020 to January 2024 were selected as the study subjects of the modeling set. According to whether the patients had prolonged hospitalization, the patients were divided into the prolonged group and the non-prolonged group. The general data, bedside simple indicator data, serological indicator data, Revised Atlanta Classification (RAC), and Bedside Index for Severity in Acute Pancreatitis (BISAP) of the patients were collected. Multivariate logistic regression analysis was used to screen the related factors for prolonged hospitalization in AP patients, and a risk prediction model was established and a nomogram was drawn for evaluation. According to the ratio of modeling set to validation set of 7:3, 103 AP patients admitted to another hospital from February 2024 to November 2024 were selected as the validation set for external validation of the model.Results The modeling set included all factors with statistically significant differences between groups in the univariate analysis (P < 0.05). Multivariate analysis results showed that heart rate, pain status, blood urea nitrogen, RAC, BISAP, and peritoneal irritation sign were all risk factors for prolonged hospitalization in AP patients (P < 0.05). The sensitivity was 0.957, specificity was 0.972, area under the curve was 0.982, Youden index was 0.884, and the Hosmer-Lemeshow goodness-of-fit test showed a good effect (χ² = 2.455, P = 0.964). For the validation set, the prediction model had a sensitivity of 1.000, specificity of 0.892, area under the curve of 0.985, Youden index of 0.892, and the Hosmer-Lemeshow goodness-of-fit test also showed a good effect (χ² = 1.538, P = 0.992). The calibration curves of the two groups of models were both close to the ideal curve, which verified that the model had good goodness-of-fit and relatively good predictive performance.Conclusion The prediction model for prolonged hospitalization in AP patients constructed based on bedside simple indicators has good predictive performance, which can provide a reference for predicting the prolonged hospitalization of AP patients. In clinical practice, it can be used as an evaluation method to assess whether the hospitalization time of AP patients is prolonged according to the actual use timing and needs.
  • Hua Shibin, Ren Jinwen, Zhu Jiaying
    Chinese Journal of Hospital Statistics. 2025, 32(5): 372-379. https://doi.org/10.3969/j.issn.1006-5253.2025.05.010
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    Objective To group patients with gastric malignant tumors into Diagnosis-Related Groups (DRGs) using the Exhaustive CHAID (ECHAID) decision tree model, and empirically analyze the influencing factors of inpatient expenses.Methods Retrospectively extract the first-page data of medical records of inpatients with gastric malignant tumors who received treatment in our hospital from June 2022 to August 2024. Taking inpatient expenses as the dependent variable, 16 clinical and expense-related variables including age, gender, main diagnosis, surgical method, and Comorbidity and Complication Index (CCI) were selected as independent variables to construct the ECHAID decision tree model. The model parameters were set as follows: maximum tree depth of 5 layers, minimum sample size of 100 for parent nodes, minimum sample size of 50 for child nodes, and significance level α = 0.05. The average expense and Case Mix Index (CMI) of the terminal DRGs nodes were calculated to form the final DRGs grouping scheme. The 10-fold cross-validation method was used to evaluate the stability of the model.Results The binary logistic multivariate regression model showed that the tolerance and Variance Inflation Factor (VIF) of each index had good discrimination, and there was no multicollinearity. Surgical method, chemotherapy regimen, and Charlson Comorbidity Index were the main factors affecting the median inpatient expenses (P < 0.001). By incorporating surgical method, chemotherapy regimen, and Charlson Comorbidity Index as independent variables into the ECHAID decision model and establishing tree-like nodes, a total of 4 layers with 27 nodes were generated, including 15 DRGs grouping nodes. The range of Coefficient of Variation (CV) was 0.17-0.31, and the range of Relative Weight (RW) was 1.05-3.26, indicating good discrimination between groups. The median of average inpatient expenses among the 15 DRGs groups ranged from 40,321.46 yuan to 128,565.90 yuan, and the over-limit range between groups was 2.27%-3.76%.Conclusion The DRGs grouping scheme for gastric malignant tumors based on the ECHAID decision tree model has good homogeneity and heterogeneity, which can provide a reference for the formulation of relevant payment policies.
  • Zou Fang, Lu Jianqi, Dong Yaowen, Wang Hanyan
    Chinese Journal of Hospital Statistics. 2025, 32(5): 380-384. https://doi.org/10.3969/j.issn.1006-5253.2025.05.011
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    Objective To conduct a retrospective analysis of 2,789 death cases in a tertiary grade A hospital in a certain prefecture-level city from 2019 to 2023, explore the factors related to deceased patients, and provide a reference basis for continuously improving the level of treatment and reducing the mortality rate. Methods The first-page information of inpatient medical records from 2019 to 2023 was collected using the medical record statistics system. Combined with the International Classification of Diseases (ICD) rules, Excel 2007 and SPSS 19.0 software were used to conduct statistical analysis on the data of 2,789 deceased patients. Statistical charts were used to describe the distribution differences in mortality rates across different years, genders, age groups, emergency/critical care patients, and low-risk groups. The composition and ranking of the main causes of death (by disease category) and the composition and ranking of specific disease types of the main causes of death among inpatient deceased patients were analyzed, and the chi-square test (χ² test) was used for inter-group comparison.Results Over the 5-year period from 2019 to 2023, the mortality rate of the hospital was 0.61%, and the male-to-female ratio of inpatients was 1:1.07. The mortality rate was 0.80% for male patients and 0.43% for female patients. The age group with the highest mortality rate among deceased patients was those aged 81 years and above (3.2%), followed by the 71-80 years group (1.11%). In terms of the proportion of deaths by age group, the 71-80 years group accounted for the highest proportion (24.60%), followed by the 61-70 years group (22.30%). The top 3 disease categories in terms of the proportion of causes of death were tumors (29.94%), circulatory system diseases (28.11%), and respiratory system diseases (11.98%). The top 3 specific disease types causing death were acute myocardial infarction, lung cancer, and intracranial injury. Patients admitted with acute conditions had the highest mortality rate (5.51%), followed by those admitted with critical conditions (0.67%). A total of 88 patients died in the low-risk group over the 5 years, with a low-risk group mortality rate of 0.36‰. The highest low-risk group mortality rate in the 5 years was in 2022 (0.64‰), and the lowest was in 2023 (0.11‰).Conclusion To improve the comprehensive service capacity of disease diagnosis and treatment in tertiary public hospitals, it is necessary to optimize the allocation of hospital resources, strengthen the discipline construction and specialized service capacity of departments such as oncology, cardiovascular medicine, and critical care medicine, continuously enhance the ability and level of diagnosis and treatment of difficult and critical diseases, and promote the high-quality development of the hospital.
  • Li Hui, Xu Kang, Li Xiaoli, Sun Yao, Zhou Xin, Li Wenjiang
    Chinese Journal of Hospital Statistics. 2025, 32(5): 385-389. https://doi.org/10.3969/j.issn.1006-5253.2025.05.012
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    Objectives To retrospectively analyze the changes in the disease spectrum and hospitalization expenses of elderly discharged patients from a tertiary hospital in Taizhou City, and to provide data support for hospital management and disease control. Methods Clinical data and hospitalization expense records of patients aged 60 years and above from 2018 to 2023 were extracted from the medical record system. Discharge diagnoses were grouped in accordance with the International Classification of Diseases, 10th Revision (ICD-10). The hospitalization expenses and disease prevalence were described statistically. Results A total of 194,242 patients aged 60 years and above were included in this study from 2018 to 2023, among whom 61.94% were male and 38.06% were female, with an average age of (71.89 ± 7.68) years. The top 4 disease categories based on the primary diagnosis were as follows: Factors influencing health status and contact with health services (23.00%); Diseases of the circulatory system (14.23%); Diseases of the digestive system (12.35%); Diseases of the respiratory system (10.04%). Hospitalization expenses increased with the extension of length of stay and the increase in the proportion of surgical procedures. Pharmaceutical fees, diagnostic fees, and consumable fees accounted for the dominant proportion of total hospitalization expenses. Conclusions Disease control departments should focus on the prevention and control of tumors, circulatory system diseases, and digestive system diseases. Medical institutions should optimize the structure of hospitalization expenses to reflect the labor and technical value of medical staff. Meanwhile, they should actively promote day surgery and give full play to the role of medical consortia in patient triage, so as to reasonably reduce the average length of hospital stay.
  • Hu Naibao, Zhang Yuli, Liu Hongfu, Zhang Luping, Wei Fei, Wang jiu, Han Chunlei, Hu Zhiyong
    Chinese Journal of Hospital Statistics. 2025, 32(5): 390-392. https://doi.org/10.3969/j.issn.1006-5253.2025.05.013
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    The concept of Outcome-Based Education (OBE) is an advanced educational philosophy. In the design of the teaching syllabus for Medical Statistics based on the OBE concept, students should be the main focus of teaching objectives, and teaching activities should be centered on students. Attention should be paid not only to "cultivating professional talents" but also to "fostering moral integrity". On one hand, it is necessary to add competence objectives, deepen affective objectives, and strengthen the integration of ideological and political education into the curriculum within the connotation of course objectives. On the other hand, it is essential to incorporate higher-order thinking skill objectives to enhance students' ability to solve practical problems using statistical thinking and methods. In the design of teaching activities, the amount of assignments should be increased—requiring students to complete relevant research designs and the writing of papers/reports—so that students can "stay engaged" in "doing assignments" during their spare time.
  • Yang Haoyu, Zhang Lijiang, Cui Jinqi
    Chinese Journal of Hospital Statistics. 2025, 32(5): 393-400. https://doi.org/10.3969/j.issn.1006-5253.2025.05.014
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    Objective To explore the patterns and context of research in the field of patients' medical care choice, and provide reference for future studies. Methods Using the visual analysis tool CiteSpace, a visual analysis was conducted on domestic literature related to patients' offline medical care choice, which was published between 2013 and 2023 and included in databases such as CNKI, SinoMed, Wanfang Data Knowledge Service Platform, and VIP Chinese Journal Service Platform. Results A total of 80 literatures were included. The number of literatures on patients' offline medical care choice showed an overall upward trend, but the total quantity was relatively small. Jiang Jinxing, Zhuo Lang, Zhang Yan, Miao Chunxia, etc., were high-yield authors; Sichuan University, Xuzhou Medical University, and Capital Medical University published a relatively large number of papers. The keyword co-occurrence map showed that keywords such as "medical care choice", "influencing factors", and "hierarchical medical system" had high frequencies; the keyword timeline map was mainly clustered into 9 major thematic clusters, including "primary care first consultation", "medical care choice", "medical care preference", "medical-seeking behavior", "hierarchical medical system", "patients", "health education", "rural patients", and "dermatology". Conclusions It is suggested to strengthen and promote cooperation among scholars, institutions, and regions, continuously supplement and revise existing conclusions based on dynamic changes, provide theoretical support for the formulation of national health policies, and promote the construction of a more reasonable medical system.