Evidence map›Paper›PMID 22215056›Full record

Trial reportJournal of the American Medical Informatics Association : JAMIA

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

Adam Wright, Justine Pang, Joshua C Feblowitz, Francine L Maloney, Allison R Wilcox, Karen Sax McLoughlin, Harley Ramelson, Louise Schneider, David W Bates

Registry-linked trialAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01105923 (Making Accurate Problem Lists in the EHR), which is not on this map. Cited by 48 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing 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.

NCT01105923 naunknown statusnot on this map

Making Accurate Problem Lists in the EHR

TypeinterventionalSponsorBrigham and Women's HospitalRan2010 to 2017Enrolled140ConditionsAttention Deficit Disorder With Hyperactivity, Asthma, COPD, Breast CancerArmsMAPLE
3 · Its place in the literature

Who cites it

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

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  11. Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023
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  14. Indication alerts to improve problem list documentation.Journal of the American Medical Informatics Association : JAMIA · 2022
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  19. Clinical data sharing improves quality measurement and patient safety.Journal of the American Medical Informatics Association : JAMIA · 2021
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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

9 authors.

Adam WrightDivision of General Internal Medicine, Brigham & Women's Hospital, Boston, Massachusetts 02115, USA. awright5@partners.org
Justine Pang
Joshua C Feblowitz
Francine L Maloney
Allison R Wilcox
Karen Sax McLoughlin
Harley Ramelson
Louise Schneider
David W Bates

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.

objectiveTo determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. STUDY DESIGN AND

methodsInference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009-5/2010) and intervention (5/2010-11/2010) periods.

results17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.

conclusionProblem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.

trial registrationClinicalTrials.gov: NCT01105923.

Indexed as

Ambulatory Care Information SystemsDecision Support Systems, ClinicalElectronic Health RecordsMedical Records, Problem-OrientedDocumentationFemaleHumansMaleMassachusettsMeaningful UseMiddle AgedProspective StudiesUser-Computer Interface

Identifiers

PMID22215056
PMCPMC3384110

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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.