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.

/pdf/what-you-need-to-know-before-implementing-a-clinical-xwb4ddzzv1.pdf

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

Background: There are a variety of challenges to health workforce planning, but access to data is critical for effective evidence-based decision-making. Many agencies and organizations throughout Mississippi have been collecting quality health data for many years. Those data have historically resided in data silos and have not been readily shared. A strategy was developed to build and coordinate infrastructure, capacity, tools, and resources to facilitate health workforce and population health planning throughout the state. Objective: Realizing data as the foundation upon which to build, the primary objective was to develop the  capacity to collect, store, maintain, visualize, and analyze data from a variety of disparate sources -- with the ultimate goal of improving access to health care. Specific aims were to: 1)  build a centralized data repository and scalable informatics platform, 2)  develop a data management solution for this platform and then, 3)  derive value from this platform by facilitating data visualization and analysis. Methods: We designed and constructed a managed data lake for health data from disparate sources throughout the state of Mississippi. A data management application was developed to log and track all data sources, maps and geographies, and data marts.  With this informatics platform as a foundation, we use a variety of tools to visualize and analyze data. Results: Samples of data visualizations that aim to inform health planners and policymakers are presented. Many agencies and organizations throughout the state benefit from this platform. Conclusion : The overarching goal is that by providing timely, reliable information to stakeholders, Mississippians in general will experience improved access to quality care.

/pdf/data-lakes-and-data-visualization-an-innovative-approach-to-16g15ys7j3.pdf

Data Lakes and Data Visualization: An Innovative Approach to Addressing the Many Challenges of Health Workforce Planning

The current status and challenges of establishment and utilization of medical big data in China

The objective of this paper is to investigate the experiences of implementing a pharmacy automation drug dispensing system in Saudi Arabia. Key informant interviews, meeting documents, and experience of the researcher were the data collection sources used in the study. A thematic analysis of the data was conducted. Study results discuss the organizational challenges prior to implementation as well as details of the implementation process. Preliminary results show improvements in the services provided by the pharmaceutical department. Lessons learned are also discussed. The work presented in this paper is preliminary and more research is needed to evaluate the overall impact of the new pharmacy automation system on services provided by the pharmaceutical department.

The implementation experiences of a pharmacy automation drug dispensing system in saudi arabia

Introduction: A clinical data warehouse (CDW) is a powerful resource that supports clinical decision-making and secondary data use by integrating and presenting heterogeneous data sources. Despite considerable effort within healthcare organizations (HCOs) to develop CDWs, scientific literature surrounding clinical data warehousing methods is limited. Objectives: The scoping review aims to characterize the current state of CDW methods within HCOs, to identify extant evidence for practice recommendations, and ultimately to advance the design, implementation, and use of CDWs. Methods: The review encompasses CDW articles published from 2011 through 2021 identified through a systematic PubMed search. Article abstracts were systematically screened by two authors. Full-text articles were reviewed and abstracted independently by two authors with discrepancies resolved through consensus. Results: 137 articles, from 55 journals and 3 conference proceedings, were categorized and analyzed. Areas for increased CDW focus include CDW design (such as data integration of increased data types and sources; extract-transform-load (ETL) optimization; data quality improvement processes; semantic data representation) and CDW governance (such as support tools/documentation and data literacy efforts for staff and end-users; governance structure; and business model/financial support for CDWs including staffing). Conclusion: The study indicates the topics that have been significantly developed and the aspects that need additional focus and reporting in CDW between existing general data management best practices and recently articulated requirements for research data. Also, more multi-site and multi-aspect studies are needed to foster maturity at CDWs.

Title Pending 320

Secondary use or single source is still far from routine in healthcare, although lots of data are available either structured or unstructured. As data are stored in multiple systems, using them for biomedical research is difficult. Clinical data warehouses already help overcoming this issue, but currently they are only used for certain parts of biomedical research. A comprehensive research platform based on a generic architecture pattern could increase the benefits of existing data warehouses for both patient care and research by meeting two objectives: serving as a so called single point-of-truth and acting as a mediator between them strengthening interaction and close collaboration. Another effect is to reduce boundaries for the implementation of data warehouses. Taking further settings into account the architecture of a clinical data warehouse supporting patient care and biomedical research needs to be integrated with biomaterial banks and other sources. This work provides a solution conceptualizing a comprehensive architecture pattern of a Health Information Research Platform (HIReP) derived from use cases of the patient care and biomedical research domain. It serves as single IT infrastructure providing solutions for any type of use case.

Health Information Research Platform (HIReP)--an architecture pattern.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Theresia Eichner

Author Tools

Chat about Author

Papers

Health Information Research Platform (HIReP)--an architecture pattern.