Evidence map›Paper›PMID 37693846›Full record

ArticleFrontiers in big data2023

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

Mengyun Sui, Minna Cheng, Sheng Zhang, Yuheng Wang, Qinghua Yan, Qinping Yang, Fei Wu, Long Xue, Yan Shi, Chen Fu

Abstract read
In one paragraph

Article in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mengyun Sui *Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Minna Cheng *Division of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Sheng ZhangDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Yuheng WangDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Qinghua YanDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Qinping YangDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Fei WuDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Long XueMedical Department, Huashan Hospital, Fudan University, Shanghai, China.
Yan ShiDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Chen FuDivision of Chronic Non-communicable Diseases and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

big datachronic disease managementdigital technologyIntegrated Community Multimorbidity Care ModelShanghai

Identifiers

PMID37693846
PMCPMC10483282

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.