Objective To explore the impact of personal digital assistant (PDA) information system on surgery operations, so as to provide basis for improving the efficiency of surgery operations and building medical research databases. Methods The data of patients undergoing surgical treatment in Northern Jiangsu People’s Hospital between October 1, 2018 and September 30, 2020 were retrospectively analysised. According to whether to operate the PDA information system, the patients who did not use the PDA information system for surgical treatment between October 1, 2018 and September 30, 2019 were taken as the control group (before the operation), and the patients who used the PDA information system for surgical treatment between October 1, 2019 and September 30, 2020 were taken as the intervention group (after the operation). The quality of surgical operation, the time of anesthesia opening, the time of opening operation, the length of operation, and other operation indicators before and after the operation of the PDA information system were analyzed. Results A total of 59 610 patients were enrolled, including 27 726 in the control group and 31 884 in the intervention group. Compared with before the operation of the PDA information system, the total annual operation increased by 4 158 cases (15.00%), and the average turnover of per operation room increased (17.10%). The average anesthesia opening time is 14.52 minutes earlier. The average operation opening time is 18.25 minutes earlier. Except for gastrointestinal center surgery, thoracic surgery, neurology surgery, trauma center surgery, intensive care unit ward surgery, biliary and pancreatic surgery, hepatosplenic surgery, and other types of surgery (P>0.05), other types of surgeries were statistically significant differences in the operation duration before and after other operations (P<0.05). Conclusions The PDA information system developed based on "VariFlight" quantifies the quality of surgical operations more finely. It can effectively improve the operation efficiency and economic benefits of surgery, shorten the operation time, contribute to the construction of medical research databases.
As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention in both academia and industry. Traditional Chinese medicine (TCM) is characterized by individual syndrome differentiation as well as personalized treatment with personality analysis, which makes the common law mining technology of big data and artificial intelligence appear distortion in TCM diagnosis and treatment study. This article put forward an intelligent diagnosis model of TCM, as well as its construction method. It could not only obtain personal diagnosis varying individually through active learning, but also integrate multiple machine learning models for training, so as to form a more accurate model of learning TCM. Firstly, we used big data extraction technique from different case sources to form a structured TCM database under a unified view. Then, taken a pediatric common disease pneumonia with dyspnea and cough as an example, the experimental analysis on large-scale data verified that the TCM intelligent diagnosis model based on active learning is more accurate than the pre-existing machine learning methods, which may provide a new effective machine learning model for studying TCM diagnosis and treatment.
This paper expounds the classification and characteristics of healthcare-associated infections (HAI) surveillance systems from the perspective of the informatization needs of HAI monitoring, explains the determination requirements of numerator and denominator in the surveillance statistical data, and introduces the regular verification for auditing the quality of HAI surveillance. The basic knowledge of machine learning and its achievements are introduced in processing surveillance data as well. Machine learning may become the mainstream algorithm of HAI automatic monitoring system in the future. Infection control professionals should learn relevant knowledge, cooperate with computer engineers and data analysts to establish more effective, reasonable and accurate monitoring systems, and improve the outcomes of HAI prevention and control in medical institutions.
The era of big data has brought a big revolution that will transform the way we live, work, and think. In medical field, as the development of social economics and medicine since 21 century, the human disease spectrum has been changing, the disease type has been increasing, and the complexity of the etiology, diagnosis and treatment of disease have been gradually increasing. In order to improve the healthy level, and explore the law of disease occurrence and development, we should constantly research to find discipline in enormous knowledge by fully mining and using the big medical data. It will be helpful to improve the level medical information management. And it can be supportive to the diagnosis, treatment, clinical practice and decision-making. We did the review under the background of big data, and the mean contact of this review is about the origin, meaning, classification, features of big data as well as the research process, application and future development of data mining, especially clinical data mining.
ObjectiveTo understand the trend and problems of asthma treatment in different levels of hospitals in Chongqing, and to provide objective basis for more refined and standardized asthma management. MethodsThe outpatient and inpatient asthma diagnosis and treatment data of four hospitals of different grades in Chongqing from 2017 to 2021 were extracted by medical big data capture platform, and the trend of outpatient and prescription changes was analyzed retrospectively according to natural year. ResultsThere were 19514 outpatients asthma visits in the four hospitals, of whom 11816 (60.6%) were female. There were 1875 hospitalizations, of which 1117 (59.6%) were female. ① Changes of asthma visit mode: From 2017 to 2019, the number of outpatient asthma visits and the proportion of asthma in the total outpatient volume increased, decreased significantly in 2021, and basically recovered to the level of 2019 in 2022. Asthma hospitalizations in tertiary hospitals showed a decreasing trend, while those in secondary hospitals increased significantly. The proportion of asthma patients who chose outpatient treatment in the four hospitals increased year by year, among which the increase was more significant in non-tertiary teaching hospitals, and the proportion of asthma acute attack in outpatient and inpatient treatment increased. ② Changes of medication pattern: The rate of inhaled corticosteroids/long-acting β2-agonists (ICS/LABA) prescription in outpatient department increased year by year, the highest was 48.6%, but the rate of short-acting β2-agonists (SABA) prescription also increased year by year, especially in secondary hospitals, the rate of SABA prescription in secondary hospitals reached 39.7%. The proportion of hospitalized asthma patients treated with inhaled corticosteroids (85.1%) was higher than that of intravenous corticosteroids (50.9%), and the proportion of intravenous theophylline prescription was as high as 91.7%, while the proportion of nebulized SABA prescription was 71.4%. ConclusionsThe trend of asthma diagnosis and treatment is that the number of outpatients and the use of ICS/LABA is gradually increasing, while the number of inpatients is decreasing. However, there is still a large gap in the proportion of asthma maintenance medication used in different levels of hospitals, so it is necessary to continuously promote standardized diagnosis and treatment management of asthma in hospitals at all levels, especially primary hospitals.
ObjectiveTo analyze the clinical application and safety of Shenmai injection.MethodsWe collected clinical data of 30 012 patients using Shenmai injection from 26 hospitals nationwide from September, 2009 to June, 2013. The SPSS 15.0 software was used to analyze demographic characteristics, diagnostic information, and clinical application of the injection.ResultsAmong all patients, 14 270 were females (47.55%), 8 218 were aged 45-60 (27.38%), and 10 452 were aged 61-75 (34.83%). The primary use of Shenmai injection was as an adjuvant treatment of chemotherapy for cancer patients, and the top 3 cancers were lung cancer (1 533, 5.11%), breast cancer (1 509, 5.03%) and gastric cancer (847, 2.82%). The second important use of Shenmai injection was the treatment of coronary heart disease (5 703, 19.00%), of which the most common single dose was 50 mL (14 406, 48.00%), followed by 100 mL (10 804, 36.00%) and 200 mL (600, 2.00%). The solvents were used in 18 902 patients (62.98%), and the 5% glucose injection was used most frequently (84.64%). The adverse effects (AEs) rate was 0.15%, and 57.78% AEs occurred within 24 hours of infusion. The most common AEs were damage of the cardiovascular system, followed by damaging of blood system and respiratory system.ConclusionsShenmai injection has a wide range of applications and can be used in treatment of numerous diseases in the real-world, and the AEs have been linked to off-label uses.
As a science which focuses on evidence, the decision making process of evidence medicine encounters an opportunity for development in the big data era. The starting point is shifting forward from evidence to data. The big data technology is playing an active role in evidence's collection, process and utilization. Evidence is more objective, righteous, authentic, transparent and easier to collect. Thus, to initiate evidence-based medicine research in the big data era and to structure an evidence-based medicine intelligent service platform, a full-scaled strategy should be developed in order to improve the quality of evidence. To promote the complete publicity of clinical research data, structuralized clinical data standard should be constructed. To provide a pathway to patients' follow-up data, portable and wearable monitoring devices should be popularized. To avoid risks from utilization of clinical research big data, regulations of clinical data usage should be implemented.
Big data technology is an inevitable result of the information age, which not only promotes the development of biomedical science, but also opens up new paths for the development of traditional Chinese medicine (TCM). This paper introduced the application status of big data technology in the field of TCM in recent years, and put forward some thinkings and prospects so as to provide new insights and methods for the future development direction of TCM.
ObjectiveTo explore the methods of data management and statistical analysis for longitudinal big data collected from mobile health management applications (APP). MethodsThe data management process and statistical analysis method were proposed by summarizing the characteristics of the data from mobile health management APPs. The methods would be clarified by a practical case: an APP recording female menstruation. ResultsThe data from health management APPs belong to longitudinal big data and the original record of the APP should be reprocessed or computed before conducting statistical analysis. A two-step data cleaning procedure was suggested for data management of the original records and reprocessed data, and longitudinal models such as mixed models was recommended for statistical analysis. ConclusionsThe data from health management APPs could be used for medical research via specific data management and statistical analysis after removing suspicious data. Cloud computing could be a viable method to improve efficiency of the big data analysis of health management APPs.
The application scenarios and conditions of the burden of disease were sorted out, and the survey databases related to disease surveillance at home and abroad and the GBD research of IHME were introduced. Through the collection of literature, five cases of the burden of disease application of health big data were summarized, and their construction modes were described in detail based on different types of databases. We pointed out the problems and challenges faced by the application of health data, and put forward some ideas and prospects for future research on the application of the burden of disease of health big data.