Evidence map›Paper›PMID 26499104›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

Provider variation in responses to warnings: do the same providers run stop signs repeatedly?

Patrick E Beeler, E John Orav, Diane L Seger, Patricia C Dykes, David W Bates

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Medication-related clinical decision support alert overrides in inpatients.Journal of the American Medical Informatics Association : JAMIA · 2018
    Observational
  10. Improving Clinical Decision Support in Pharmacy: Toward the Perfect DUR Alert.Journal of managed care & specialty pharmacy · 2017
    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

5 authors.

Patrick E BeelerDivision of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Research Center for Medical Informatics, University Hospital Zurich, Switzerland.
E John OravDivision of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
Diane L SegerPartners Healthcare Systems, Inc, Wellesley, Massachusetts, USA.
Patricia C DykesDivision of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA.
David W BatesDivision of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Partners Healthcare Systems, Inc, Wellesley, Massachusetts, USA dbates@partners.org.

Funding

Health Information Technology Center for Education and Research on Therapeutics *U19HS021094 · AHRQ · BRIGHAM AND WOMEN'S HOSPITAL · PI BATES, DAVID W., MD,MSC · 2011 to 2015
$4.1M
AHRQ HHS U19 HS021094
6 · The paper itself

Abstract

objectiveVariation in the use of tests and treatments has been demonstrated to be substantial between providers and geographic regions. This study assessed variation between outpatient providers in overriding electronic prescribing warnings.

methodsResponses to warnings were prospectively logged. Random effects models were used to calculate provider-to-provider variation in the rates for the decisions to override warnings in 6 different clinical domains: medication allergies, drug-drug interactions, duplicate drugs, renal recommendations, age-based recommendations, and formulary substitutions.

resultsA total of 157 482 responses were logged. Differences between 1717 providers accounted for 11% of the overall variability in override rates, so that while the average override rate was 45.2%, individual provider rates had a wide range with a 95% confidence interval (CI) (13.7%-76.7% ). The highest variations between providers were observed in the categories age-based (25.4% of total variability; average override rate 70.2% [95% CI, 29.1%-100% ]) and renal recommendations (24.2%; average 70% [95% CI, 29.5%-100% ]), and provider responses within these 2 categories were most often clinically inappropriate according to prior work. Among providers who received at least 10 age-based recommendations, 64 of 238 (27%) overrode ≥ 90% of the warnings and 13 of 238 (5%) overrode all of them. Of those who received at least 10 renal recommendations, 36 of 92 (39%) overrode ≥ 90% of the alerts and 9 of 92 (10%) overrode all of them.

conclusionsThe decision to override prescribing warnings shows variation between providers, and the magnitude of variation differs among the clinical domains of the warnings; more variation was observed in areas with more inappropriate overrides.

Indexed as

Drug Therapy, Computer-AssistedMedical Order Entry SystemsPractice Patterns, Physicians'Attitude of Health PersonnelDecision Support Systems, ClinicalDrug HypersensitivityDrug InteractionsElectronic PrescribingHumansMedication Errorsattitude of health personnelclinical decision support systemsclinical practice variationcomputer-assisted drug therapymedical order entry systems

Identifiers

PMID26499104
PMCPMC4954628

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.