Design and development of a disease-specific clinical database system to increase the availability of hospital data in China

Abstract

Purpose In order to meet restrictions and difficulties in the development of hospital medical informatization and clinical databases in China, in this study, a disease-specific clinical database system (DSCDS) was designed and built. It provides support for the full utilization of real world medical big data in clinical research and medical services for specific diseases. Methods The development of DSCDS involved (1) requirements analysis on precision medicine, medical big data, and clinical research; (2) design schematics and basic architecture; (3) standard datasets of specific diseases consisting of common data elements (CDEs); (4) collection and aggregation of specific disease data scattered in various medical business systems of the hospital; (5) governance and quality improvement of specific disease data; (6) data storage and computing; and (7) design of data application modules. Results A DSCDS for liver cirrhosis was created in the gastrointestinal department of a 3A grade hospital in China and had more than nine data application modules. Based on this DSCDS, a series of clinical studies are being carried out, such as retrospective or prospective cohorts, prognostic studies using multimodal data, and follow-up studies. Conclusion The development of the DSCDS for liver cirrhosis in this paper provides experience and reference for the design and development of DSCDSs for other specific diseases in China; it can even expand to the development of DSCDSs in other countries if they have the demand for DSCDS and the same or better medical informatization foundation. DSCDS has more accurate, standard, comprehensive, multimodal and usable data of specific diseases than the general clinical database system and clinical data repository (CDR) and provides a credible data foundation for medical research, clinical decision-making and improving the medical service quality of specific diseases.

Development and Implementation of a Dynamically Updated Big Data Intelligence Platform Using Electronic Medical Records for Secondary Hypertension

“…At present, the method of collecting and sorting data is still mainly manual, which provides some problems, such as heavy workload, low efficiency, high error rate, and difficulty sharing and utilizing collected and sorted data. Following the construction of the diversified scientific research disease database for clinical departments under the big data environment, a number of scientific research databases have also been produced based on electronic medical record system [ 30 , 31 , 32 , 33 , 34 ], which is the result of the continuous development and intersection of computer technology, information technology, and medicine. Moreover, CRF visual personalized customization and template management, data integration and integration, batch import, automation and auxiliary control filling, and multi-way data security management, have been implemented to assist the department in performing clinical research to the highest quality and efficiency.…”