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The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai

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 seve...

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Autores principales: Sui, Mengyun, Cheng, Minna, Zhang, Sheng, Wang, Yuheng, Yan, Qinghua, Yang, Qinping, Wu, Fei, Xue, Long, Shi, Yan, Fu, Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483282/
https://www.ncbi.nlm.nih.gov/pubmed/37693846
http://dx.doi.org/10.3389/fdata.2023.1241296
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author Sui, Mengyun
Cheng, Minna
Zhang, Sheng
Wang, Yuheng
Yan, Qinghua
Yang, Qinping
Wu, Fei
Xue, Long
Shi, Yan
Fu, Chen
author_facet Sui, Mengyun
Cheng, Minna
Zhang, Sheng
Wang, Yuheng
Yan, Qinghua
Yang, Qinping
Wu, Fei
Xue, Long
Shi, Yan
Fu, Chen
author_sort Sui, Mengyun
collection PubMed
description 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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spelling pubmed-104832822023-09-08 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 Front Big Data Big Data 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. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10483282/ /pubmed/37693846 http://dx.doi.org/10.3389/fdata.2023.1241296 Text en Copyright © 2023 Sui, Cheng, Zhang, Wang, Yan, Yang, Wu, Xue, Shi and Fu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Sui, Mengyun
Cheng, Minna
Zhang, Sheng
Wang, Yuheng
Yan, Qinghua
Yang, Qinping
Wu, Fei
Xue, Long
Shi, Yan
Fu, Chen
The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title_full The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title_fullStr The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title_full_unstemmed The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title_short The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai
title_sort digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in shanghai
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483282/
https://www.ncbi.nlm.nih.gov/pubmed/37693846
http://dx.doi.org/10.3389/fdata.2023.1241296
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