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Cited by 27Electronic Health Records SystemsMedical Coding and Health InformationMachine Learning in Healthcare

Utilizing an integrated infrastructure for outcomes research: a systematic review

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Dixon, Brian E., Whipple, Elizabeth C., Lajiness, John M., Murray, Michael D.·2016·Health Information & Libraries Journal
DOI 10.1111/hir.12127
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Abstract

Objective: To explore the ability of an integrated health information infrastructure to support outcomes research. Methods: A systematic review of articles published from 1983 to 2012 by Regenstrief Institute investigators using data from an integrated electronic health record infrastructure involving multiple provider organisations was performed. Articles were independently assessed and classified by study design, disease and other metadata including bibliometrics. Results: A total of 190 articles were identified. Diseases included cognitive, (16) cardiovascular, (16) infectious, (15) chronic illness (14) and cancer (12). Publications grew steadily (26 in the first decade vs. 100 in the last) as did the number of investigators (from 15 in 1983 to 62 in 2012). The proportion of articles involving non‐Regenstrief authors also expanded from 54% in the first decade to 72% in the last decade. During this period, the infrastructure grew from a single health system into a health information exchange network covering more than 6 million patients. Analysis of journal and article metrics reveals high impact for clinical trials and comparative effectiveness research studies that utilised data available in the integrated infrastructure. Discussion: Integrated information infrastructures support growth in high quality observational studies and diverse collaboration consistent with the goals for the learning health system. More recent publications demonstrate growing external collaborations facilitated by greater access to the infrastructure and improved opportunities to study broader disease and health outcomes. Conclusions: Integrated information infrastructures can stimulate learning from electronic data captured during routine clinical care but require time and collaboration to reach full potential.

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  • INPC data are aggregated from providers, payers, and public health agencies in real-time.

    “INPC data are aggregated from providers, payers, and public health agencies in real-time, which was a recommended practice in Dixon et al.’s systematic review [16], enabling online functions such as clinical alerts that can influence patient care decisions at the point of care.”

    From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [16]

    The Discussion (page unavailable) describes the INPC as an integrated resource for clinical and administrative data collected across clinical facilities, supporting the general point that it brings data together. It does not establish that data come from payers and public health agencies, are aggregated in real time, or that such aggregation was a recommended practice of the review. (LLM, conf=0.84)

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Authors
Dixon, B. E., Whipple, E. C., Lajiness, J. M., & Murray, M. D.
Affiliations
  • United States Department of Veterans Affairs (US)
  • Indiana University School of Medicine (US)
  • Eli Lilly (United States) (US)
  • Regenstrief Institute (US)
Corresponding author
Dixon, Brian E.
ORCID
Venue
Health Information & Libraries Journal
Publisher
Wiley
Published
2015-12-07
Volume
33
Issue
1
Pages
7-32
ISSN
  • 1471-1834 (ISSN-L)
  • 1471-1842
DOI
10.1111/hir.12127
OpenAlex
W2333302052
Cited by
27
References
12
Citations by year
2026: 2 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 2 · 2020: 4 · 2018: 2 · 2017: 4 · 2016: 1
FWCI
0.25
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Health Information & Libraries Journal (publishedVersion)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion)
  • Purdue University Indianapolis (Indiana University) (submittedVersion)
Funders
Robert Wood Johnson Foundation (71596)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
electronic health records, outcomes research, health information exchange, learning health systems, comparative effectiveness research, clinical trials, observational studies, research collaboration, health system data
MeSH terms
Delivery of Health Care, Humans, Outcome Assessment, Health Care, Delivery of Health Care, Integrated, Evidence-Based Medicine, Biomedical Research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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