Optimizing the Electronic Health Records Through Big Data Analytics: A Knowledge-Based View

Zhang, Caifeng; Ma, Rui; Sun, Shiwei; Li, Yujie; Wang, Yichuan; Yan, Zhijun

doi:10.1109/access.2019.2939158

Cited by 24 publications

(17 citation statements)

References 31 publications

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“…A total of 241 articles were identified in the literature search [ 11 , 13 - 19 , 21 - 29 , 31 , 33 - 35 , 37 , 43 , 44 , 47 - 49 , 52 - 264 ]; the largest number of articles were identified in IEEE Xplore (n=112), followed by MEDLINE (n=95), and Google Scholar (n=71). After removing duplicates (n=24), we added 3 articles that were frequently cited in the selected articles but were missing from our search results [ 30 , 36 , 42 ].…”

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

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“…Furthermore, information security and protection, information quality, ongoing preparing, combination of heterogeneous or unique information, and guidelines for medical services information are likewise difficulties of Big Data examination in medical care frameworks. [10] In this article, Zhang et al, 2019 describes how we can improve the electronic health record from adaptation of the big data analytics tools and can be get meaningful information using big data analytics with EHR. Over past years, EHRs has been recommended to upgrade the medical care administration productivity and adequacy, yet it doesn't imply that basically embracing the EHRs framework could prompt those advantages.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The research gap for future work is that while using big data analytics it is possible to enhance the performance using machine or deep learning to produce fundamental innovation in the field of medical. [10] The proposed model methodology includes collection of data, storage of data and utilization of data. According to the results, proposed research model is a decent indicator of significant utilization of EHRs with regards to nursing office as the R2 represents 60.70% of the change.…”

Section: Refmentioning

confidence: 99%

“…The articles that we examined the accompanying stages are utilized in huge information investigation viz Intelligent Medical Platform, content-based sifting, EHR (Electronic Health Records), methodical writing convention, SNOMED CT all CP information, metaphysics based demonstrating and HL 7 correspondence, WSRMax, PF and Hadoop [1], [2], [11], [12], [3]- [10]. As per our perspective after steps have the accompanying downsides which are as per the following; intelligent clinical stage can charge a big amount of time and money to construct, modify, and fix.…”

Section: Critical Analysis and Future Workmentioning

confidence: 99%

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The Role of Big Data Analytics in Healthcare

Khan¹,

Younas²,

Khan³

et al. 2021

IJSCE

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Recently a rapid increase has been seen in the technology and health care became an unavoidable sector because an enormous amount of data is collected day to day. The Big data analytics has many uses and it is associated to big data. It gives an important judgment in the area of health care. This paper aims to present a review of big data in health care that how big data can aid the healthcare. In this paper multiple research paper has been reviewed and the limitations and research gaps of these papers has been discussed accordingly.

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“…Researchers build the consensus that BD is transforming information sciences, manufacturing, retail as well as the healthcare sector (Keeso, 2014; Zhang et al , 2019). Traditionally the application of BD in healthcare is lagging in comparison to other industries due to structural and operational challenges.…”

Section: Introductionmentioning

confidence: 99%

Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality

Nisar

Nasir

Jamshed

et al. 2020

JEIM

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PurposeThis study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.Design/methodology/approachThe target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.FindingsFindings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.Practical implicationsThis study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.Originality/valueThe proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

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Optimizing the Electronic Health Records Through Big Data Analytics: A Knowledge-Based View

Cited by 24 publications

References 31 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

The Role of Big Data Analytics in Healthcare

Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality

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