Evidence map›Paper›PMID 22250953›Full record

ArticleHealth services research2012

The effectiveness of implementing an electronic health record on diabetes care and outcomes.

Jeph Herrin, Briget da Graca, David Nicewander, Cliff Fullerton, Phil Aponte, Greg Stanek, Terianne Cowling, Ashley Collinsworth, Neil S Fleming, David J Ballard

Abstract read
In one paragraph

Article in Health services research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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

32 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Use of electronic health records to support smoking cessation.The Cochrane database of systematic reviews · 2014
    Pooled it
  5. Pooled it
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  16. Electronic health records systems and hospital clinical performance: a study of nationwide hospital data.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  17. Identifying and Prioritizing of Data Elements for the Ophthalmology Health Smart Card.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2018
    Article
  18. Article
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  20. Conceptual Framework for Developing a Diabetes Information Network.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2016
    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

10 authors.

Jeph HerrinDepartment of Medicine, Yale University, New Haven, CT, USA.
Briget da Graca
David Nicewander
Cliff Fullerton
Phil Aponte
Greg Stanek
Terianne Cowling
Ashley Collinsworth
Neil S Fleming
David J Ballard

Funding

Impact of Health IT Implementation on Diabetes Process and Outcome MeasuresR21HS020696 · AHRQ · BAYLOR RESEARCH INSTITUTE · PI BALLARD, DAVID J. · 2011 to 2012
$300k
AHRQ HHS 1R21HS020696-01AHRQ HHS R21 HS020696
6 · The paper itself

Abstract

objectiveTo assess the impact of electronic health record (EHR) implementation on primary care diabetes care. DATA SOURCES: Charts were abstracted semi-annually for 14,051 diabetes patients seen in 34 primary care practices in a large, fee-for-service network from January 1, 2005 to December 31, 2010. The study sample was limited to patients aged 40 years or older. STUDY

designA naturalistic experiment in which GE Centricity Physician Office-EMR 2005 was rolled out over a staggered 3-year schedule. DATA COLLECTION: Chart audits were conducted using the AMA/Physician Consortium Adult Diabetes Measure set. The primary outcome was the HealthPartners' "optimal care" measure: HbA1c ≤ 8 percent; LDL cholesterol < 100 mg/dl; blood pressure < 130/80 mmHg; not smoking; and documented aspirin use in patients ≥ 40 years of age. PRINCIPAL

findingsAfter adjusting for patient age, sex, and insulin use, patients exposed to the EHR were significantly more likely to receive "optimal care" when compared with unexposed patients (p < .001), with an estimated difference of 9.20 percent (95% CI: 6.08, 12.33) in the final year between exposed patients and patients never exposed. Components of the optimal care bundle showing positive improvement after adjustment were systolic blood pressure <80 mmHg, diastolic blood pressure <130 mmHg, aspirin prescription, and smoking cessation. Among patients exposed to EHR, all process and outcome measures except HbA1c and lipid control showed significant improvement.

conclusionImplementation of a commercially available EHR in primary care practice may improve diabetes care and clinical outcomes.

Indexed as

Electronic Health RecordsOutcome and Process Assessment, Health CareQuality of Health CareAdultAgedAlgorithmsAmbulatory CareChi-Square DistributionDiabetes MellitusFemaleHealth Services ResearchHumansMaleMiddle AgedPrevalencePrimary Health Care

Identifiers

PMID22250953
PMCPMC3401397

What OpenQuestion holds

Textmetadata
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.