Evidence map›Paper›PMID 36593483›Full record

ArticleBMC health services research2023

Real word challenges in integrating electronic medical record and administrative health data for regional quality improvement in diabetes: a retrospective cross-sectional analysis.

Rukia Swaleh, Taylor McGuckin, Denise Campbell-Scherer, Brock Setchell, Peter Senior, Roseanne O Yeung

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Rukia SwalehDivision of Endocrinology & Metabolism, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Taylor McGuckinOffice of Lifelong Learning & the Physician Learning Program, Faculty of Medicine and Dentistry, University of Alberta, AB, Edmonton, Canada.
Denise Campbell-SchererOffice of Lifelong Learning & the Physician Learning Program, Faculty of Medicine and Dentistry, University of Alberta, AB, Edmonton, Canada.
Brock SetchellOffice of Lifelong Learning & the Physician Learning Program, Faculty of Medicine and Dentistry, University of Alberta, AB, Edmonton, Canada.
Peter SeniorDivision of Endocrinology & Metabolism, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Roseanne O YeungDivision of Endocrinology & Metabolism, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada. ryeung@ualberta.ca.
University of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLinked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure.

methodsWe conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described.

resultsThe process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases.

conclusionElectronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged.

Indexed as

Diabetes MellitusElectronic Health RecordsCross-Sectional StudiesHumansQuality ImprovementRetrospective StudiesAdministrative health dataDiabetesElectronic medical recordLearning health systemRegional quality improvement

Identifiers

PMID36593483
PMCPMC9806899
OpenAlexW4313476385

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.