Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 3, 2020
Date Accepted: Jul 17, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
What you need to know before implementing a clinical research data warehouse: a comparative review of integrated data repositories in health care institutions
ABSTRACT
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. The IDRs offer several advantages in clinical data reuse and the number of institutions implementing an IDR is growing steadily in the past decade.
Objective:
The architectural choices of major IDRs, also referred to as (clinical) data warehouses, are highly diverse and determining their differences can be overwhelming. In this review, we explored the underlying models and common features of IDRs. We provide a high-level overview for those entering the field and propose a set of guiding principles for small to medium size health institutions embarking on IDR implementation.
Methods:
We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2018, and selected those that specifically describe IDR architectures. Out of 80 shortlisted articles, we found 19 articles describing 23 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 four common architecture models that use different approaches for data processing and integration; such different approaches were driven by 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/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 the IDR architecture planning.
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