Evaluation of the implementation and effect of the healthcare cloud information platform for diabetes self‐management: A case study in <scp>Shanghai</scp>

Chen, Cheng; Gong, Xia; Wang, Xiaoli; Chuanwei, Cao; Sun, Xiaoming; Zhang, Yimin; Jing, Limei; Chen, Ning; Liu, Qian; Hui, Wang; Tang, Lan; Wang, Zhaoxin

doi:10.1002/hpm.2869

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…Since 30 September 2018, the Shanghai Healthcare Cloud Information Platform has deployed 2672 pieces of IoT health monitoring and collection equipment to community health service centres in all 16 administrative districts of Shanghai. This served an average of 8966 persons per day who uploaded data to the cloud, totalling 12 043 508 person‐times, and its data were integrated into hospital information system (HIS) and public health system for NCD diagnosis and control …”

Section: Resultsmentioning

confidence: 99%

An evaluation of the effects of general practitioner–supported patient noncommunicable diseases control model in Shanghai, China

Shu

Ling

Sun

2019

Health Planning & Management

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Summary Noncommunicable diseases (NCDs) are the leading cause of death worldwide. A litmus test for China's health reform is its ability to effectively deal with NCDs. The general practitioners (GPs)–supported NCD control model was designed to address ways to effectively prevent and control NCDs population‐wide. This study aimed to evaluate the GPs‐supported NCD control model and its effect on China's NCD control. The model was structured with three layers of two‐way relationships between patients, GPs, and specialists. The four technologies included the community‐based NCD self‐management model, the electronic clinical‐pathways–supporting community NCD intervention, the largest regional independent medical information system engaged by the Shanghai Healthcare Cloud, and continuous and successive policy intervention. Under the influence of the model, hypertension awareness, treatment, and control were the highest in Shanghai. By 31 August 2017, 131 453 users declined to use the mobile application while 10 444 completed the type 2 diabetes mellitus (T2DM) risk assessment. By 15 August 2017, 249 000 residents had participated in early detection and screening, leading to the identification of 29 304 (11.8%) being in a prediabetic state and 24 422 (9.8%) being newly diagnosed with T2DM. From 2012 to 2016, 208 537 patients were newly identified with hypertension using Internet‐connected devices and hypertension awareness increased 5.7%. After implementing the self‐management interventions, patients' blood pressure was controlled at around 90% (from 2007 to 2015). This pilot model in Shanghai suggested positive social benefits and appears to have played an important role in advancing NCD control in China.

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Section: Resultsmentioning

confidence: 99%

An evaluation of the effects of general practitioner–supported patient noncommunicable diseases control model in Shanghai, China

Shu

Ling

Sun

2019

Health Planning & Management

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“…14,15 It enables the measuring, inferring, and understanding the ecological pointers, from subtle ecologies and natural resources to urban settings and smart homes. 16,17 The spread of the mentioned pieces of equipment in an actuating-communicating grid creates the IoT, in which sensors and actuators merge flawlessly with our peripheral areas. 18,19 The IoT addresses network-empowered technologies like mobile and wearable gadgets that can feel, activate, interact, and communicate with other identical devices through the Internet.…”

Section: What Is the Iot?mentioning

confidence: 99%

Internet of things in health management systems: A review

Huang¹,

Huang³

et al. 2020

Int J Communication

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In today's health care context, the application of the Internet of Things (IoT) offers suitability for doctors and patients as we can use them in many medical fields. So, we have emphasized the particular use of the IoT in medicine and health care, such as clinical devices management, medication management, clinical data management, distant medicine, mobile medical care, and individual health management. We have done an organized review to enhance health management systems via many up-to-date IoT-oriented health care applications. The article approbates with the methodological necessities of systematic literature reviews. We have identified several issues caused by the quick acceptance of IoT-oriented systems' health management. IoT necessitates new safety infrastructure, according to the novel practical principles. Outcomes illustrate that health experts can assist in supplying necessities, information, or standards to improve IoT devices or systems. Also, they are able to present novel fields or applications for which IoT is fit. This survey aids the hospitals and relevant institutions to recognize IoT needs. Also, the article will serve as an initial location for future IoT security management and design. Policymakers and opinion formers need to understand the IoT and its implications.

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“…In previous research, the use of (e.g., cloud-based) technology for support by healthcare professionals was reported to mostly result in positive outcomes for diabetes self-management (32)(33)(34). Wehbe, Curcio, Gajjar, and Yadlapati (35) stated that the integration of technology into diabetes care affects physicianpatient relationships positively when discussions are facilitated and when shared decision-making between physicians and patients is improved.…”

Section: Social Support and Technology-supported Diabetes Self-managementioning

confidence: 99%

Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

Brew‐Sam

Chib²,

Rossmann

2020

Preprint

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Background Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus non-professional (family/friends) support on mobile app use for diabetes self-management.Methods This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N= 21, Study 1) and an online survey (N= 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus non-professionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decision-making, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory.Results The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physician-patient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use.Conclusion The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literatureThe use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and that use of diabetes apps may decrease when such support is high (e.g., no perceived need to use technology).

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Evaluation of the implementation and effect of the healthcare cloud information platform for diabetes self‐management: A case study in Shanghai

Cited by 8 publications

References 23 publications

An evaluation of the effects of general practitioner–supported patient noncommunicable diseases control model in Shanghai, China

An evaluation of the effects of general practitioner–supported patient noncommunicable diseases control model in Shanghai, China

Internet of things in health management systems: A review

Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

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