Evidence map›Paper›PMID 31821766›Full record

ArticleJDR clinical and translational research2020

Finding Dental Harm to Patients through Electronic Health Record-Based Triggers.

M F Walji, A Yansane, N B Hebballi, A M Ibarra-Noriega, K K Kookal, S Tungare, K Kent, R McPharlin, V Delattre, E Obadan-Udoh and 3 more

Abstract read
In one paragraph

Article in JDR clinical and translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

13 authors.

M F WaljiSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
A YansaneSchool of Dentistry, University of California, San Francisco, CA, USA.
N B HebballiSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
A M Ibarra-NoriegaSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
K K KookalSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
S TungareSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
K KentSchool of Dentistry, Oregon Health and Science University, Portland, OR, USA.
R McPharlinSchool of Dentistry, Oregon Health and Science University, Portland, OR, USA.
V DelattreSchool of Dentistry at Houston, University of Texas Health Science Center, Houston, TX, USA.
E Obadan-UdohSchool of Dentistry, University of California, San Francisco, CA, USA.
O TokedeHarvard School of Dental Medicine, Boston, MA, USA.
J WhiteSchool of Dentistry, University of California, San Francisco, CA, USA.
E KalenderianSchool of Dentistry, University of California, San Francisco, CA, USA.

Funding

Developing A Patient Safety System For DentistryR01DE022628 · NIDCR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI WALJI, MUHAMMAD · 2013 to 2018
$4.8M
NIDCR NIH HHS R01 DE022628
6 · The paper itself

Abstract

backgroundPatients may be inadvertently harmed while undergoing dental treatments. To improve care, we must first determine the types and frequency of harms that patients experience, but identifying cases of harm is not always straightforward for dental practices. Mining data from electronic health records is a promising means of efficiently detecting possible adverse events (AEs).

methodsWe developed 7 electronic triggers (electronic health record based) to flag patient charts that contain distinct events common to AEs. These electronic charts were then manually reviewed to identify AEs.

resultsOf the 1,885 charts reviewed, 16.2% contained an AE. The positive predictive value of the triggers ranged from a high of 0.23 for the 2 best-performing triggers (failed implants and postsurgical complications) to 0.09 for the lowest-performing triggers. The most common types of AEs found were pain (27.5%), hard tissue (14.8%), soft tissue (14.8%), and nerve injuries (13.3%). Most AEs were classified as temporary harm (89.2%). Permanent harm was present in 9.6% of the AEs, and 1.2% required transfer to an emergency room.

conclusionBy developing these triggers and a process to identify harm, we can now start measuring AEs, which is the first step to mitigating harm in the future. KNOWLEDGE TRANSFER STATEMENT: A retrospective review of patients' health records is a useful approach for systematically identifying and measuring harm. Rather than random chart reviews, electronic health record-based dental trigger tools are an effective approach for practices to identify patient harm. Measurement is one of the first steps in improving the safety and quality of care delivered.

Indexed as

Electronic Health RecordsPatient HarmHumansPatient SafetyPrecipitating FactorsRetrospective Studiesadverse eventsdentistryEHRsinformaticspatient safetytrigger tool

Identifiers

PMID31821766
PMCPMC7298886

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.