Medical practice generates and stores immense amounts of clinical process data, while integrating and utilization of these data requires interdisciplinary cooperation together with novel models and methods to further promote applications of medical big data and research of artificial intelligence. A "Datathon" model is a novel event of data analysis and is typically organized as intense, short-duration, competitions in which participants with various knowledge and skills cooperate to address clinical questions based on "real world" data. This article introduces the origin of Datathon, organization of the events and relevant practice. The Datathon approach provides innovative solutions to promote cross-disciplinary collaboration and new methods for conducting research of big data in healthcare. It also offers insight into teaming up multi-expertise experts to investigate relevant clinical questions and further accelerate the application of medical big data.

In recent years medical rebates suspected of illegal and criminal cases frequently appeared in the media and newspaper, triggering the public continuous attention to the problems of commercial bribery in the field of medicine. Starting from the definition of medical rebates concept, this paper deeply analyzed the deep cause of medical rebates, jumped out of the existing countermeasures and suggestions framework, and starting from the inter-nal supervision and restriction approach of discipline inspection and supervision, discussed the solution from four aspects of cultural atmosphere, interests chain, management process, self-supervision, in order to provide refer-ence for the comprehensive treatment of medical rebates.

Objective To construct a database containing multiple kinds of diseases that can provide "real world"data for first-aid clinical research. Methods Structured or non-structured information from hospital information system, laboratory information system, emergency medical system, emergency nursing system and bedside monitoring instruments of patients who visited department of emergency in PLA General Hospital from January 2014 to January 2018 were extracted. Database was created by forms, code writing, and data process. Results Emergency Rescue Database is a single center database established by PLA General Hospital. The information was collected from the patients who had visited the emergency department in PLA General Hospital since January 2014 to January 2018. The database included 530 585 patients' information of triage and 22 941 patients' information of treatment in critical rescue room, including information related to human demography, triage, medical records, vital signs, lab tests, image and biological examinations and so on. There were 12 tables (PATIENTS, TRIAGE_PATIENTS, EMG_PATIENTS_VISIT, VITAL_SIGNS, CHARTEVENTS, MEDICAL_ORDER, MEDICAL_RECORD, NURSING_RECORD, LAB_TEST_MASTER, LAB_RESULT, MEDICAL_EXAMINATION, EMG_INOUT_RECORD) that containing different kinds of patients' information. Conclusions The setup of high quality emergency databases lay solid ground for scientific researches based on data. The model of constructing Emergency Rescue Database could be the reference for other medical institutions to build multiple-diseases databases.

Objective:To evaluate the safety and feasibility of the laparoscopic indocyanine green (ICG) fluorescence imaging during the sentinel node navigational surgery for the early gastric cancer.Methods:Patients with <4 cm early gastric cancer were chosen. 0.5 ml ICG (2.5 mg/ml) was preoperatively injected into submucosa around the lesion in four points by the endoscopy. The sentinel lymph node basin including the stained tissue and lymph node (LN) were completely resected guided by the fluorescence mapping under ICG laparoscopy. The specimen was inspected by frozen pathology section. The radical gastrectomy was dependent on the pathology result.Result:Between 2019 and 2021, a total of 18 patients were included in the final analysis. Most tumors (16/18) located in the middle or distal stomach. Median tumor size was 2.0 cm. Lymph vessel invasion was revealed in five cases and perineural invasion in three cases. According to AJCC tumor grading system, tumor depth was classified as Tis in 2 cases, T1a in 5 cases and T1b in 11 cases. Lymph node metastasis (LNM) was revealed in four patients (4/18, 22%). Median sentinel lymph node basins per patient were 2 (range, 1-5). An average 6 (range, 2-13) LNs were harvested in each case, including 6 (1-13) ICG stained LNs and 1 (0-5) non stained LNs. All of four LNM patients were detected by sentinel node navigational surgery. The rate of the sensitivity and accuracy were 100% and 100%, respectively. The median follow-up for the entire group was 58.3 months (0.3-59.9 months), with no recurrence or metastasis observed in any patient.Conclusion:The sensitivity and accuracy of the laparoscopic indocyanine green fluorescence imaging during the sentinel node navigational surgery were satisfactory.

The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
Big Data ; Critical Care ; Databases, Factual ; Humans ; Medical Informatics