CSDC — A nationwide screening platform for stroke control and prevention in China

Yu, Junxia; Mao, Huajian; Li, Mei; Ye, Dan; Zhao, Dongsheng

doi:10.1109/embc.2016.7591354

Cited by 17 publications

(10 citation statements)

References 6 publications

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“…An example of an IDR providing multiple services is the STRIDE architecture stack [ 18 ], which includes several services for data analysis or research data management. The articles describing METEOR [ 35 ], CSDC [ 34 ], models and simulation techniques for discovering diabetes-related factors (MOSAIC) [ 33 ], and Veterans Administration’s EHRs [ 32 ] provide further details about the integration of CDSS tools in the architecture. In these cases, the architecture model is divided into CDSS and data analysis modules, both of which communicate with the common database.…”

Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

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Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…In these contexts, inconsistent power supplies led to difficulties in EHR system implementation and intermittent internet availability constrained the development of web-based services [21–23]. However, a number of studies were able to leverage mobile networks and web-based platforms to wider benefit [24, 35–38]. Studies reported different levels of maturity vis-à-vis EMR systems reach within a given context [27, 29, 39–46].…”

Section: Resultsmentioning

confidence: 99%

“…Specifically with regard to the workforce, many studies assessed end-user (or potential users) evaluation of previously implemented or planned ICT interventions [23, 31, 38, 42, 43, 45, 46, 48, 50]. Multiple studies highlighted interventions based around software, web-based platforms, or mobile technologies [21, 24, 25, 36, 37, 46]. Specific ICT interventions incorporated elements of automation [28, 30, 44, 49, 52]; data standardization or quality control [17, 22, 24, 31–33, 35, 47]; data visualisations or data mapping [21, 35, 37, 38, 44, 45, 49]; and data analysis tools [22, 24, 29, 39–41, 46].…”

Section: Resultsmentioning

confidence: 99%

“…Multiple studies highlighted interventions based around software, web-based platforms, or mobile technologies [21, 24, 25, 36, 37, 46]. Specific ICT interventions incorporated elements of automation [28, 30, 44, 49, 52]; data standardization or quality control [17, 22, 24, 31–33, 35, 47]; data visualisations or data mapping [21, 35, 37, 38, 44, 45, 49]; and data analysis tools [22, 24, 29, 39–41, 46]. Finally, some studies also specifically mentioned measures protecting patient information [17, 33, 36, 38].…”

Section: Resultsmentioning

confidence: 99%

“…Several studies highlighted important recommendations related to outputs and visualisations such as standardization of unique patient identifiers; modular, flexible information systems structures; bilingual and user-friendly interfaces; and ease of uploading and sharing important clinical information based on the authors' findings [26, 31, 44, 47, 48]. Clinical and health dashboards constituted a common intervention [22, 33, 35, 37, 40, 46, 52], with additional studies also incorporating automated alert systems [28, 29, 45], and a number of studies focused on data analysis and public health reporting [21, 24, 25, 30, 32, 36, 38, 40, 41, 52]. Finally, studies in Taiwan [27]; Africa and Asia [32]; South Korea [53]; and China [47] documented the creation of online tools and data repositories as a result of their respective interventions.…”

Section: Resultsmentioning

confidence: 99%

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Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

Dornan

Pinyopornpanish

Jiraporncharoen

et al. 2019

BioMed Research International

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Introduction. Electronic health records offer a valuable resource to improve health surveillance and evaluation as well as informing clinical decision making. They have been introduced in many different settings, including low- and middle-income countries, yet little is known of the progress and effectiveness of similar information systems within Asia. This study examines the implementation of EHR systems for use at a population health level in Asia and to identify their current role within public health, key success factors, and potential barriers in implementation. Material and Methods. A systematic search process was implemented. Five databases were searched with MeSH key terms and Boolean phrases. Articles selected for this review were based on hospital provider electronic records with a component of implementation, utilisation, or evaluation for health systems or at least beyond direct patient care. A proposed analytic framework considered three interactive components: the content, the process, and the context. Results. Thirty-two articles were included in the review. Evidence suggests that benefits are significant but identifying and addressing potential challenges are critical for success. A comprehensive preparation process is necessary to implement an effective and flexible system. Discussion. Electronic health records implemented for public health can allow the identification of disease patterns, seasonality, and global trends as well as risks to vulnerable populations. Addressing implementation challenges will facilitate the development and efficacy of public health initiatives in Asia to identify current health needs and mitigate future risks.

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A Detailed Study on Implication of Big Data, Machine Learning and Cloud Computing in Healthcare Domain

Abirami

Karthikeyan

2021

Learning and Analytics in Intelligent Systems

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CSDC — A nationwide screening platform for stroke control and prevention in China

Cited by 17 publications

References 6 publications

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

A Detailed Study on Implication of Big Data, Machine Learning and Cloud Computing in Healthcare Domain

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