Evidence map›Paper›PMID 25896648›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2015

Associations between healthcare quality and use of electronic health record functions in ambulatory care.

Jessica S Ancker, Lisa M Kern, Alison Edwards, Sarah Nosal, Daniel M Stein, Diane Hauser, Rainu Kaushal, HITEC Investigators

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–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

27 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Association between evidence-based training and clinician proficiency in electronic health record use.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing.International journal of environmental research and public health · 2020
    Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Algorithm to detect pediatric provider attention to high BMI and associated medical risk.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  18. Article
  19. Article
  20. 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

8 authors.

Jessica S AnckerDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA Health Information Technology Evaluation Collaborative (HITEC), New York, NY, USA jsa7002@med.cornell.edu.
Lisa M KernDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA Health Information Technology Evaluation Collaborative (HITEC), New York, NY, USA.
Alison EdwardsDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA Health Information Technology Evaluation Collaborative (HITEC), New York, NY, USA.
Sarah NosalInstitute for Family Health and the Mount Sinai Department of Family Medicine and Community Health, New York, NY, USA.
Daniel M SteinDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA.
Diane HauserInstitute for Family Health and the Mount Sinai Department of Family Medicine and Community Health, New York, NY, USA.
Rainu KaushalDepartment of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, USA Health Information Technology Evaluation Collaborative (HITEC), New York, NY, USA.
HITEC Investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesContemporary electronic health records (EHRs) offer a wide variety of features, creating opportunities to influence healthcare quality in different ways. This study was designed to assess the relationship between physician use of individual EHR functions and healthcare quality. MATERIALS AND

methodsSixty-five providers eligible for "meaningful use" were included. Data were abstracted from office visit records during the study timeframe (183 095 visits with 61 977 patients). Three EHR functions were considered potential predictors: acceptance of best practice alerts, use of order sets, and viewing panel-level reports. Eighteen clinical quality measures from the "meaningful use" program were abstracted.

resultsUse of condition-specific best-practice alerts and order sets was associated with better scores on clinical quality measures capturing processes in diabetes, cancer screening, tobacco cessation, and pneumonia vaccination. For example, providers above the median in use of tobacco-related alerts had higher performance on tobacco cessation intervention metrics (median 80.6% vs. 66.7%; P < .001), and providers above the median in use of diabetes order sets had higher quality on diabetes low density lipoprotein (LDL) testing (68.2% vs. 59.5%; P == .001). Post hoc examination of the results showed that the positive associations were with measures of healthcare processes (such as rates of LDL testing), whereas there were no positive associations with measures of healthcare outcomes (such as LDL levels). DISCUSSION: Among primary care providers in the ambulatory setting using a single EHR, intensive use of certain EHR functions was associated with increased adherence to recommended care as measured by performance on electronically reported "meaningful use" quality measures. This study is relevant to current policy as it uses quality metrics constructed by contemporary certified EHR technology, and quantitative EHR use metrics rather than self-reported use.

conclusionIn the early stages of the "meaningful use" program, use of specific EHR functions was associated with higher performance on healthcare process metrics.

Indexed as

Meaningful UseQuality of Health CareAdultAmbulatory CareDiabetes MellitusEarly Detection of CancerElectronic Health RecordsFemaleGuideline AdherenceHumansMalePhysiciansPneumococcal VaccinesTobacco Use CessationUnited StatesPneumococcal Vaccines

Identifiers

PMID25896648
PMCPMC11737649

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.