Evidence map›Paper›PMID 25589914›Full record

ArticleApplied clinical informatics2014

User centered clinical decision support tools: adoption across clinician training level.

L J McCullagh, A Sofianou, J Kannry, D M Mann, T G McGinn

Abstract read
In one paragraph

Article in Applied clinical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  13. Clinical diabetes : a publication of the American Diabetes Association · 2019
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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.

L J McCullaghDepartment of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine , Manhasset, NY.
A SofianouDepartment of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine , NYC, NY.
J KannryDepartment of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine , NYC, NY.
D M MannDepartment of Medicine, Section of Preventive Medicine & Epidemiology, Boston University School of Medicine , Boston, MA.
T G McGinnDepartment of Medicine, Division of Internal Medicine, Hofstra North Shore-LIJ School of Medicine , Manhasset, NY.

Funding

Evidence Based Decision Making: Integrating Clinical Prediction Rules into ElectrR18HS018491 · AHRQ · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MCGINN, THOMAS G · 2009 to 2011
$1.3M
AHRQ HHS 7R18HS018491-03AHRQ HHS R18 HS018491
6 · The paper itself

Abstract

backgroundDissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act's Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that 'early adopters' are more inclined to use CDS tools and younger physicians tend to be ranked in this category.

objectiveThis study examined the differences in adoption of CDS tools across providers' training level.

participantsFrom November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial.

interventionThe intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits. MAIN MEASURES: The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings. KEY

resultsThe completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents.

conclusionThere is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians' training levels.

Indexed as

AdultData CollectionDecision Support Systems, ClinicalFemaleHealth PersonnelHumansMaleclinical decision supportclinical prediction rulesevidence based medicineImplementation scienceprimary care

Identifiers

PMID25589914
PMCPMC4287678

What OpenQuestion holds

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Registered trials

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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.