With the development of society and the progress of technology, artificial intelligence (AI) and big data technology have penetrated into all walks of life in social production and promoted social production and lifestyle greatly. In the medical field, the applications of AI, such as AI-assisted diagnosis and treatment, robots, medical imaging and so on, have greatly promoted the development and transformation of the entire medical industry. At present, with the support of national policy, market, and technology, we should seize the opportunity of AI development, so as to build the first-mover advantage of AI development. Of course, the development and challenges are coexisted. In the future development process, we should objectively analyze the gap between our country and developed countries, think about the unfavorable factors such as AI chips and data problems, and extend the application and service of AI and big data to all links of medical industry, integrate with clinic fully, so as to better promote the further development of AI medicine treatment in China.
According to the characteristics of the diversified employment system of general hospitals, we have independently developed a set of personnel information platforms suitable for our hospital's operating model and work-flow which achieved establishing a novel big data management model for big personnel. After a year of trial operation, the big data management of personnel has completely covered the target management and requirements of the hospital, covering basic quality, public services, teaching work, medical work, scientific research, and other dimensions of information, which helped the hospital constructed a systematically networked and full-coveraged, personnel information system with strong early warning functions and incentives, enabling the reasonable utilization rate of human capital and continuous improvement of the quality of talent training.
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.
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.
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.
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.
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.
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.
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.
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.