Objective To survey the current status of residents’ health examination in township medical units in Shuangliu county, so as to provide references for exploring the residents’ health examination mode suitable for the well-off rural hospitals, and for improving the effects of physical examination. Methods The self-designed questionnaires were made to survey the current situation of health examination for the residents in 24 township medical units in Shuangliu County, including 3 central township hospitals, 3 community health centers and 18 general township hospitals; and the outline was designed to interview the people responsible for managing and implementing the resident’s examination work. The Epidate 3.1 was used for data input, the SPSS 17.0 was used for descriptive statistics and stratified analysis, and the classification method was adopted for qualitative research of personal interview data. Results The number of primary health care units for carrying out residents’ health examination got gradually increased year by year from 2008 to 2010; and all 24 township-level medical units had already carried out that examination by 2010. But some issues still existed such as lower participation rate and ineffective utilization of the archives. Conclusion a) It is necessary to prepare well, enhance organizing, expand publicity, and increase participation rate when primary health care units carry out the health examination for residents; b) It is necessary requires to fasten the informatization construction of health archives, and to quicken the process and analysis of examination information; and c) It is advisable to constantly optimize the examination items according to exam information, to improve the qualification of medical staffs, and to fasten the construction of exam team.
Objective To explore the value of applying intelligent quality control systems to the quality management of hospital health examination services, and to provide a reference for quality control improvement in the health examination industry. Methods The Department of Health Management Center of Zigong Fourth People’s Hospital used an intelligent quality control system to optimize the health management service process. The work efficiency and feedback of health examinations in the Department of Health Management Center before (From October to December 2019) and after (From October to December 2023) the intelligent upgrade of the health management service system were compared. Results During the research period, 27047 individuals were tested before the intelligent upgrade, and 33868 individuals were tested after the upgrade. Before the intelligent upgrade, there were 205 cases (14.24%) of errors that the system failed to detect; 208 cases (8.72%) were missed in the initial examination system; 18 cases (13.53%) were missed in the overall examination system; 90 cases (0.33%) had overdue physical examination reports. After the intelligent upgrade, there were 38 cases (2.42%) of errors that the system failed to detect; 56 cases (1.93%) were missed in the initial examination system; 10 cases (3.31%) were missed in the overall examination system; 67 cases (0.20%) had overdue physical examination reports. After the intelligent upgrade, the system showed better performance than before in detecting errors in health examinations, avoiding initial and total leak diagnosis, timely report submission, and improving the satisfaction and complaint situation of health management services among examinees (P<0.05). Conclusion The intelligent quality control system is conducive to improving the quality management of hospital health examination services and enhancing the efficiency of examination services.
摘要:目的: 通過分析健康體檢者心電圖異常的發生率及類型,為當地人群心血管疾病的早期診斷、早期治療提供依據。 方法 : 采用光電三道心電圖機在體檢者安靜休息狀態下以常規12道描記,時間在15秒左右。按3個年齡段對健康體檢患者心電圖進行分組分析,同時對心電圖異常者做病因診斷。 結果 : 1356例完成十二導聯心電圖監測,異常心電圖占2257%,其中STT異常占首位1123%。41～60歲人群心電圖異常的檢出率男性較高,且多伴高血壓、血糖異常、血脂異常等; 61～81 歲組人群心電圖異常的檢出率最高,且多已存在糖尿病、高血壓和冠狀動脈供血不足等疾病。 結論 :定期進行心電圖檢查,對早期發現、預防、診斷心血管疾病有重要意義。Abstract: Objective: To provide evidences for the early diagnosis and treatment of cardiovascular diseases through the analysis of the electrocardiographic abnormality and category. Methods : Analyzing the health examination electrocardiogram according to age and etiological diagnosis were committing to cases with electrocardiographic abnormality. Results : 1356 cases finished the electrocardiography. The rate of electrocardiographic abnormality was 2257%, and the STT abnormality hold the first place (1123%). The rate of electrocardiographic abnormality increased with the increasing age and it is highest in the 61~81 ages. Conclusion : Regular health examination by electrocardiography is important for early diagnosis, prevention and treatment of potential cardiovascular disease.
Objective To develop a computer-aided diagnosis model for lung cancer based on routine health examination data for identifying individuals with a current high risk of lung cancer in health screening settings, thereby providing decision support for subsequent clinical confirmation. Methods Individuals who underwent health examinations at the Health Management Center of West China Hospital, Sichuan University, between 2010 and 2022 were enrolled. After screening, a retrospective cohort of 5257 subjects was retained, comprising 1307 patients with lung cancer and 3950 non-lung cancer controls. A three-tier feature fusion model was designed: Heterogeneous feature encoding module: a multi-layer perceptron and bidirectional encoder representations from transformers (BERT) were employed to extract feature vectors from structured data and unstructured data (medical records and imaging report texts), respectively. Heterogeneous feature fusion architecture: dimensional expansion concatenation coupled with a gated recurrent unit based gating network was implemented to achieve multi-scale feature alignment and deep interaction, thereby addressing dimensional discrepancies and information redundancy. Attention-based decision mechanism: word-level attention with weighted pooling was applied to dynamically capture key features and generate risk probability distributions. Model performance was evaluated using precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results The proposed model significantly outperformed both single-data-type models and simple concatenation approaches. On the test set, the proposed model achieved a recall of 0.861, an F1-score of 0.882, and an AUC-ROC of 0.972, substantially surpassing the best-performing model trained on structured data alone (extreme gradient boosting: recall=0.630, F1-score=0.725, AUC-ROC=0.916) and the model trained on unstructured data alone (BERT coupled with a bidirectional long short-term memory network: recall=0.833, F1-score=0.846, AUC-ROC=0.944). Feature elimination experiments demonstrated minimal performance variation across different feature subsets, confirming the model’s capability to effectively identify and mitigate the impact of irrelevant features. Subgroup analyses revealed that the model performed optimally in female subjects (recall=0.835, F1-score=0.838, AUC-ROC=0.950) and individuals aged >69 years (recall=0.913, F1-score=0.875, AUC-ROC=0.911). Conclusion The proposed model based on heterogeneous health examination data can identify high-risk individuals for lung cancer among health examination populations using only routine screening data, thereby facilitating the early diagnosis of lung cancer in this population.