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The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai.
Sui, Mengyun; Cheng, Minna; Zhang, Sheng; Wang, Yuheng; Yan, Qinghua; Yang, Qinping; Wu, Fei; Xue, Long; Shi, Yan; Fu, Chen.
Afiliación
  • Sui M; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Cheng M; School of Public Health, Fudan University, Shanghai, China.
  • Zhang S; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Wang Y; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Yan Q; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Yang Q; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Wu F; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Xue L; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Shi Y; Medical Department, Huashan Hospital, Fudan University, Shanghai, China.
  • Fu C; Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Front Big Data ; 6: 1241296, 2023.
Article en En | MEDLINE | ID: mdl-37693846
Background: Chronic disease management (CDM) falls under production relations, and digital technology belongs to the realm of productivity. Production relations must adapt to the development of productivity. Simultaneously, the prevalence and burden of chronic diseases are becoming increasingly severe, leveraging digital technology to innovate chronic disease management model is essential. Methods: The model was built to cover experts in a number of fields, including administrative officials, public health experts, information technology staff, clinical experts, general practitioners, nurses, metrologists. Integration of multiple big data platforms such as General Practitioner Contract Platform, Integrated Community Multimorbidity Management System and Municipal and District-Level Health Information Comprehensive Platform. This study fully analyzes the organizational structure, participants, service objects, facilities and equipment, digital technology, operation process, etc., required for new model in the era of big data. Results: Based on information technology, we build Integrated Community Multimorbidity Care Model (ICMCM). This model is based on big data, is driven by "technology + mechanism," and uses digital technology as a tool to achieve the integration of services, technology integration, and data integration, thereby providing patients with comprehensive people-centered services. In order to promote the implementation of the ICMCM, Shanghai has established an integrated chronic disease management information system, clarified the role of each module and institution, and achieved horizontal and vertical integration of data and services. Moreover, we adopt standardized service processes and accurate blood pressure and blood glucose measurement equipment to provide services for patients and upload data in real time. On the basis of Integrated Community Multimorbidity Care Model, a platform and index system have been established, and the platform's multidimensional cross-evaluation and indicators are used for management and visual display. Conclusions: The Integrated Community Multimorbidity Care Model guides chronic disease management in other countries and regions. We have utilized models to achieve a combination of services and management that provide a grip on chronic disease management.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Front Big Data Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Risk_factors_studies Idioma: En Revista: Front Big Data Año: 2023 Tipo del documento: Article