OBJECTIVE: To investigate the current status of pre-hospital and in-hospital emergency medical information connectivity in China and provide evidence for optimizing the emergency medical system. METHODS: A multi-center cross-sectional study was conducted using a multi-level convenience sampling method to select provincial-level administrative regions and their corresponding capital cities, prefectural cities, and county-level emergency medical institutions. The questionnaire included basic information about respondents, the institutions, the current status of pre-hospital and in-hospital emergency information connectivity, and the satisfaction with the connectivity. The questionnaire has undergone reliability testing and split-half reliability testing, supplemented by semi-structured interviews. Data collection was carried out from January to May 2024, with one responsible person from each institution completing the questionnaire. Multiple Logistic regression analysis to investigated the relevant factors of pre-hospital and in-hospital information connectivity. RESULTS: A total of 225 questionnaires were distributed, and 199 valid responses were collected, with a response rate of 88.4%. Participants were from 199 emergency medical institutions across 13 provincial-level administrative regions. Of the institutions, 112 (56.3%) could achieve pre-hospital and in-hospital information connectivity. The proportion of pre-hospital to in-hospital information connection between emergency institutions in different provinces varies (χ² = 39.398, P < 0.001), with Beijing and Zhejiang having the highest proportion of information connection (both at 100%), and Hainan having the lowest (11.8%). The proportion of information integration in county-level emergency institution was lower than that of provincial and municipal level emergency institutions [40.4% (19/47) vs. 61.7% (29/47), 61.0% (64/105), χ² = 6.304, P = 0.043]. Provinces with high per capita disposable income have a higher proportion of information connectivity than provinces with low per capita disposable income [77.3% (34/44) vs. 50.3% (78/155), χ² = 10.122, P = 0.001]. The information connection ratio of independent pre-hospital emergency centers was higher than that of hospital emergency departments/hospital records [74.6% (47/63) vs. 47.8% (65/136), χ² = 12.581, P < 0.001]. The proportion of information integration in advanced provinces with digital development was higher than that in other provinces [77.6% (38/49) vs. 49.3% (74/150), χ² = 11.849, P = 0.001]. Logistic regression analysis showed that the per capita disposable income of residents in the province was an independent risk factor for the information connection between pre-hospital and in-hospital emergency institutions [odds ratio (OR) = 3.21, 95% confidence interval was 1.56-6.62, P < 0.01]. 72.3% institutions used the information connection mode for less than 5 years. Telephone and WeChat were the main communication methods (83.0%), and 17.0% of emergency institutions use dedicated APP for communication. 52.7% of respondents were very or relatively satisfied with the information integration before and after the hospital. The main deficiencies in current information integration were insufficient, untimely, inaccurate communication and delayed feedback between pre-hospital and in-hospital information. Optimizing top-level design and improving network quality are the directions for improving the integration of pre-hospital and in-hospital information in the future. CONCLUSIONS Pre-hospital and in-hospital emergency information connectivity in some provinces in China remains underdeveloped, with significant regional and institutional disparities. Future efforts should focus on integrating digital technologies and strengthening grassroots-level connectivity systems.
Cross-Sectional Studies ; China ; Humans ; Surveys and Questionnaires ; Emergency Medical Services ; Emergency Service, Hospital ; Hospital Information Systems

OBJECTIVE: To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model. METHODS: The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score. RESULTS: Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)]. CONCLUSIONS The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.
Female ; Male ; Humans ; Aged ; Adult ; Middle Aged ; Adolescent ; Critical Illness ; Hospitalization ; Length of Stay ; APACHE ; Hospital Information Systems

OBJECTIVE: To solve the ESB bus performance and safety problems caused by the explosive growth of the hospital's business, and to ensure the stable interaction of the hospital's business system. METHODS: Taking the construction of our hospital's information system as an example, we used AlwaysOn, load balancing and other technologies to optimize the ESB bus architecture to achieve high availability and scalability of the hospital's ESB bus. RESULTS: The ESB bus high-availability architecture effectively eliminates multiple points of failure. Compared with the traditional dual-machine Cluster solution, the security is significantly improved. The nodes based on load balancing can be scaled horizontally according to the growth of the hospital's business volume. CONCLUSIONS The construction of the ESB bus high-availability architecture effectively solves the performance and security issues caused by business growth, and provides practical experience for medical information colleagues. It has certain guiding significance for the development of regional medical information.
Hospital Information Systems ; Information Systems

Kidney malignant tumor is a type of primary renal cell carcinoma, and mainly refers to renal cancer. The incidence of kidney cancer and the number of hospital cases in China have been increasing. Based on the clinical medicine information of patients in the hospital information system(HIS) database of 37 hospitals in China, the combined medication of patients with kidney malignant tumor were analyzed by Tabu search algorithm, so as to analyze the combined medication of patients with kidney malignant tumor in real world. A total of 7 095 patients with kidney malignant tumor were included, the ratio of males to females was 2.11∶1, and the ratio of male patients increased gradually with age. About 3 933 patients(55.43%) showed a superior effect among those patients. The common therapies of patients with kidney malignant tumor were anti-tumor therapies and symptomatic therapies, including anti-infection, regulation of electrolyte balance, sedation and analgesia, analgesic, regulation of gastrointestinal function. The whole population of patients with kidney malignant tumor were mostly treated with anti-tumor drugs combined with more symptomatic therapies, while the anti-tumor therapies of the superiority population of patients were less combined with other drugs, with less combined medication. The result may be related to the stage of tumor or individual response to the therapeutic regimen. No matter for the whole population or for the superiority population of patients with kidney malignant tumor, the therapies was mainly Western medicines. Based on the pathogenesis of deficiency in origin and excess in superficiality with kidney malignant tumor, Chinese subgroups with formula for clearing heat and removing toxicity, formula for vigorate Qi and replenish the blood, formula for regulate Qi and invigorate the blood, laxative and hemostatic were more commonly used. In the future, further studies shall be conducted for combined therapies for patients of different stages, so as to play the advantages of multi-target, overall regulation, toxicity reduction and efficacy enhancement of traditional Chinese medicine, improve the life quality of patients with kidney malignant tumor, prolong their life time, and improve the survival rate of patients.
Asian Continental Ancestry Group ; China ; Drugs, Chinese Herbal ; Female ; Hospital Information Systems ; Humans ; Kidney Neoplasms ; Male ; Medicine, Chinese Traditional

OBJECTIVE: To explore the integration method and technical realization of artificial intelligence bone age assessment system with the hospital RIS-PACS network and workflow. METHODS: Two sets of artificial intelligence based on bone age assessment systems (CHBoneAI 1.0/2.0) were developed. The intelligent system was further integrated with RIS-PACS based on the http protocol in Python flask web framework. RESULTS: The two sets of systems were successfully integrated into the local network and RIS-PACS in hospital. The deployment has been smoothly running for nearly 3 years. Within the current network setting, it takes less than 3 s to complete bone age assessment for a single patient. CONCLUSIONS The artificial intelligence based bone age assessment system has been deployed in clinical RIS-PACS platform and the "running in parallel", which is marking a success of Stage-I and paving the way to Stage-II where the intelligent systems can evolve to become more powerful in particular of the system self-evolution and the "running alternatively".
Age Determination by Skeleton ; Artificial Intelligence ; Bone and Bones ; Hospital Information Systems ; Hospitals ; Humans ; Radiology Information Systems ; Systems Integration

BACKGROUND: The medical service delivery system in Korea works inefficiently and patients tend to visit tertiary hospitals by means of the emergency department (ED). Overcrowding of the ED threatens the health and life of emergency patients as a result of the inability to effectively distribute emergency medical resources in the community. To solve this problem, improvement in the medical delivery system and dispersion of patients by strengthening primary care may be helpful. In order to make policy decisions for this, it is necessary to estimate the scale of patients who can be distributed to primary care.METHODS: From January 1 to December 31, 2016, we analyzed the National Emergency Department Information System (NEDIS) data of patients who visited a tertiary ED to examine the proportion of patients eligible for primary medical care. The inclusion and exclusion criteria for primary care were made through the consensus of three physicians.RESULTS: A total of 65,061 NEDIS records were analyzed. Among them, by inclusion criteria, 29,818 cases were Korean Triage and Acuity Scale level 4 and 5, and 11,791 patients visited the ED during the day. After considering the exclusion criteria, there were 6,468 cases who may be suitable for primary medical care.CONCLUSION: Of the patients who visited the ED of tertiary hospitals, approximately 10% of them may be suitable for primary care. There should be a discussion and social consensus to reduce overcrowding in EDs and deliver better medical services.
Consensus ; Emergencies ; Emergency Service, Hospital ; Humans ; Information Systems ; Korea ; Primary Health Care ; Tertiary Care Centers ; Tertiary Healthcare ; Triage

OBJECTIVE: This study examined the inflow and outflow patterns of emergency department patients with si-gun-gu in the Gwangju, Jeonbuk, and Jeonnam areas. METHODS: Data from the Gwangju, Jeonbuk, and Jeonnam were extracted from the National Emergency Department Information System in 2016. The extracted data (on 42 areas in Gwangju, Jeonbuk, and Jeonnam) using the variables of the patient's address (zip code) and the emergency medical institution code (emergency medical institution address) were used to calculate the relevance index and commitment index. The calculated indices were classified into the regional types by applying NbClust and cluster analysis (K-means) of the R package. RESULTS: The relevance indices ranged from 12.5% to 90.4%, and the commitment indices ranged from 9.2% to 90.3%. The results of cluster analysis with the relevance indices and commitment indices revealed three types for 39 areas. In cluster 1, the relevance indices ranged from 43.5% to 61.6%, and the commitment indices ranged from 9.2% to 49.5%. Three out of the thirty-nine areas were classified as the inflow type. In cluster 2, the relevance indices ranged from 12.5% to 56.0% and the commitment indices ranged from 62.5% to 90.3%; 12 areas were classified as the outflow type. The areas in cluster 3 were classified as the self-sufficient type, with relevance indices ranging from 60.1% to 90.4% and commitment indices ranging from 59.0% to 89.7% for 24 areas. CONCLUSION: Three area types and 11 out of 12 areas classified as outflow types were found to be emergency medical vulnerable areas. The results of this study can be used to establish local emergency medical policies.
Cluster Analysis ; Emergencies ; Emergency Service, Hospital ; Gwangju ; Humans ; Information Systems ; Jeollabuk-do ; Jeollanam-do

OBJECTIVE: The mismatch in the demand and supply of emergency medical resources has been a constant issue in Korean emergency departments (EDs). This study analyzed the characteristics and actual utilization of medical bed resources in these EDs. METHODS: The emergency department utilization (EDU) rate was calculated using the Emergency Medical Resource Information System (EMRIS) on the available beds in EDs from April 2014 to January 2015. The EDU rate was analyzed according to the season, day of the week, time of day, and ED type. Furthermore, the ratio between the maximum and minimum EDU rates was also compared between the regions. RESULTS: A total of 14,889,750 data points were included. The EDU rate was relatively high during winter and on Sundays, and was highest between 20:00 and 22:00. The ratio between the maximum and minimum EDU rates according to the time of day was highest in Gyeongbuk (5.4) and lowest in Daegu (1.4). Moreover, the EDU rate according to the ED type was highest in the regional emergency medical center (66.7%). CONCLUSION: Significant differences were observed in the EDU rates according to the season, day of the week, time of the day, region, and ED type. Therefore, EMRIS should allocate resources based on the data on both the hospital and regional characteristics.
Daegu ; Emergencies ; Emergency Service, Hospital ; Gyeongsangbuk-do ; Information Systems ; Seasons

OBJECTIVE: This study examined the clinical effects of discharge against medical advice (DAMA) on suicide reattempts and clarified the risk factors related to DAMA. METHODS: A cross-sectional observational study was conducted in the emergency department (ED) of Chungbuk National University Hospital from 2015 to 2017. The medical records of suicide attempters from the National Emergency Department Information System were reviewed. The primary outcome was the ED revisit rate. The characteristics of the patients associated with DAMA were also examined. RESULTS: Among 889 eligible suicide attempters, 328 patients (36.9%) were reported to have DAMA at least once during the study period. Patients who were discharged against medical advice were more likely to reattempt suicide compared to normal disposition patients (11.0% vs. 3.7%, adjusted odds ratio [AOR], 3.002; 95% confidence interval [CI], 1.71–5.28). The independent risk factors for DAMA were age≤60 (AOR, 1.77; 95% CI, 1.20–2.59), female (AOR, 1.45; 95% CI, 1.09–1.91), ED visit at night time (AOR, 1.41; 95% CI, 1.03–1.92), and ED discharge at night time (AOR, 1.40; 95% CI, 1.06–1.85). CONCLUSION: Patients who were discharged against medical advice revisited the ED more after suicide attempts. Public efforts will be needed for patients who are discharged against medical advice considering those risk factors.
Chungcheongbuk-do ; Emergencies ; Emergency Medical Services ; Emergency Service, Hospital ; Female ; Humans ; Information Systems ; Medical Records ; Observational Study ; Odds Ratio ; Patient Discharge ; Risk Factors ; Suicide ; Suicide, Attempted

PURPOSE: The aim of this study was to identify the predictive validity of the Korean Triage and Acuity Scale (KTAS). METHODS: This methodological study used data from National Emergency Department Information System for 2016. The KTAS disposition and emergency treatment results for emergency patients aged 15 years and older were analyzed to evaluate its predictive validity through its sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: In case of death in the emergency department, or where the intensive care unit admission was considered an emergency, the sensitivity, specificity, positive predictive value, and negative predictive value of the KTAS were 0.916, 0.581, 0.097, and 0.993, respectively. In case of death in the emergency department, or where the intensive or non-intensive care unit admission was considered an emergency, the sensitivity, specificity, and positive predictive value, and negative predictive value were 0.700, 0.642, 0.391, and 0.867, respectively. CONCLUSION: The results of this study showed that the KTAS had high sensitivity but low specificity. It is necessary to constantly review and revise the KTAS level classification because it still results in a few errors of under and over-triage. Nevertheless, this study is meaningful in that it was an evaluation of the KTAS for the total cases of adult patients who sought help at regional and local emergency medical centers in 2016.
Adult ; Classification ; Emergencies ; Emergency Service, Hospital ; Emergency Treatment ; Humans ; Information Systems ; Intensive Care Units ; Methods ; Sensitivity and Specificity ; Triage