Leveraging a Statewide Clinical Data Warehouse to Expand Boundaries of the Learning
            Health System

Turley, Christine B.

doi:10.13063/2327-9214.1245

Cited by 20 publications

(18 citation statements)

References 14 publications

(9 reference statements)

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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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“…We found one access policy that outlined the governance arrangements of CDWs and fulfilled our inclusion criteria, as determined by checking the web pages of the 23 CDWs of the publications included in the study. The reference check of all included publications did not yield additional literature or documents [11,14,15,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Section: Resultsmentioning

confidence: 99%

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

Pavlenko

Strech

Langhof

2020

BMC Med Inform Decis Mak

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Background: The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible for data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures are required in order to maintain objective oversight and control. Therefore, the development of practice oriented and evidence-based policies is crucial. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally. Methods: We performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies. Results: Twenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations. Conclusions: The description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability. CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated

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“…One solution is to shift the burden of data transformation centrally. [14] Each site is then only required to write a smaller data extraction routine. This routine identifies data items associated with critical care admissions (e.g.…”

Section: Limitationsmentioning

confidence: 99%

Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database

Harris

Shi

Brealey

et al. 2018

International Journal of Medical Informatics

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Leveraging a Statewide Clinical Data Warehouse to Expand Boundaries of the Learning Health System

Cited by 20 publications

References 14 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

Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database

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