老年多病共患临床数据库数据集

老年人多病共患临床数据库汇聚了来自全国范围内超过1000家医疗机构的数据，包括电子病历、实验室检查、影像、治疗情况等信息。经过精心规划，通过大数据技术实现了数据的统一、安全的交换，解决了临床数据收集的挑战。数据来源包括中软智通区域医疗大数据和全国心血管诊疗能力提升项目（CDQI）数据，涵盖了河北唐山、四川乐山、内蒙古自治区通辽等地区的200余家二、三级医疗机构，还覆盖了超过1000万例65岁及以上的老年人多病共患患者，为深入研究老年多病共患症状提供了充分的数据支持。数据采集过程中，严格定义了不同数据的接入标准，设置了访问权限和导入规则，确保了数据的质量和安全性。此外，不同类型的数据被整合到分类数据仓库中，根据数据源特征进行管理，包括临床表型数据库、实验室及影像数据库、临床诊疗数据库及随访数据库，从而为研究提供了多角度、多层次的数据视角。数据融合阶段通过对国家登记数据库中的数据进行加工处理，将数据转换为标准编码数据，实现了各个数据源之间的互联互通。老年人多病共患临床数据库的成功建立，对医疗领域具有重要意义，还有望降低医疗成本、促进科学研究和创新，最终改善老年人口的医疗护理和生活质量。The Clinical Database for Multimorbidity in Elderly Patients aggregates data from over 1,000 medical institutions across the country, including electronic medical records, laboratory test results, imaging data, treatment records and other relevant information. After careful planning, it achieves unified and secure data exchange through big data technologies, addressing the long-standing challenges in clinical data collection. The data sources cover China Soft Intelligence Regional Medical Big Data and the National Cardiovascular Diagnosis and Treatment Capacity Improvement Project (CDQI), involving more than 200 secondary and tertiary medical institutions in regions such as Tangshan of Hebei Province, Leshan of Sichuan Province, Tongliao of Inner Mongolia Autonomous Region and other areas. Additionally, the database encompasses more than 10 million multimorbid elderly patients aged 65 years and above, providing robust data support for in-depth research on geriatric multimorbidity. During the data collection process, strict access standards for various data types are formulated, and access permissions and import rules are set to guarantee data quality and security. Furthermore, different types of data are integrated into a categorized data warehouse and managed according to the characteristics of their sources, including the Clinical Phenotype Database, Laboratory and Imaging Database, Clinical Diagnosis and Treatment Database, and Follow-up Database, thus offering multi-angle and multi-dimensional data perspectives for research. In the data fusion phase, data from national registration databases are processed and converted into standard coded data, enabling seamless interconnection and interoperability between all data sources. The successful establishment of this database holds profound significance for the medical field, as it is expected to reduce medical costs, promote scientific research and innovation, and ultimately improve the medical care and quality of life for the elderly population.