Evidence map›Paper›PMID 37953157›Full record

ArticleJournal of pediatric surgery2024

SCAN for Abuse: Electronic Health Record-Based Universal Child Abuse Screening.

Nolan R Martin, Anneke L Claypool, Modupeola Diyaolu, Katelyn S Chan, Elizabeth A'Neals, Karan Iyer, Christopher C Stewart, Melissa Egge, Krysta Bernacki, Michelle Hallinan and 7 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pediatric surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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

17 authors at 4 institutions in 1 country.

Nolan R MartinEastern Virginia Medical School, Norfolk, VA, USA. Electronic address: martinnr@evms.edu.
Anneke L ClaypoolDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Modupeola DiyaoluStanford University School of Medicine, Stanford, CA, USA.
Katelyn S ChanUniversity of Washington School of Medicine, Seattle, WA, USA.
Elizabeth A'NealsLucile Packard Children's Hospital, Stanford, CA, USA.
Karan IyerLucile Packard Children's Hospital, Stanford, CA, USA.
Christopher C StewartLucile Packard Children's Hospital, Stanford, CA, USA.
Melissa EggeLucile Packard Children's Hospital, Stanford, CA, USA.
Krysta BernackiLucile Packard Children's Hospital, Stanford, CA, USA.
Michelle HallinanLucile Packard Children's Hospital, Stanford, CA, USA.
Linda ZuoDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Urvi GuptaDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Navleen NaruStanford University School of Medicine, Stanford, CA, USA.
David ScheinkerDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
Arden M MorrisStanford University School of Medicine, Stanford, CA, USA.
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Stephanie ChaoStanford University School of Medicine, Stanford, CA, USA.
Stanford University · USLucile Packard Children's Hospital · USEastern Virginia Medical School · USUniversity of Washington · US

Funding

Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
NCATS NIH HHS UL1 TR003142
6 · The paper itself

Abstract

backgroundIdentification of physical abuse at the point of care without a systematic approach remains inherently subjective and prone to judgement error. This study examines the implementation of an electronic health record (EHR)-based universal child injury screen (CIS) to improve detection rates of child abuse.

methodsCIS was implemented in the EHR admission documentation for all patients age 5 or younger at a single medical center, with the following questions. 1) "Is this patient an injured/trauma patient?" 2) "If this is a trauma/injured patient, where did the injury occur?" A "Yes" response to Question 1 would alert a team of child abuse pediatricians and social workers to determine if a patient required formal child abuse clinical evaluation. Patients who received positive CIS responses, formal child abuse work-up, and/or reports to Child Protective Services (CPS) were reviewed for analysis. CPS rates from historical controls (2017-2018) were compared to post-implementation rates (2019-2021).

resultsBetween 2019 and 2021, 14,150 patients were screened with CIS. 286 (2.0 %) patients screened received positive CIS responses. 166 (58.0 %) of these patients with positive CIS responses would not have otherwise been identified for child abuse evaluation by their treating teams. 18 (10.8 %) of the patients identified by the CIS and not by the treating team were later reported to CPS. Facility CPS reporting rates for physical abuse were 1.2 per 1000 admitted children age 5 or younger (pre-intervention) versus 4.2 per 1000 (post-intervention).

conclusionsIntroduction of CIS led to increased detection suspected child abuse among children age 5 or younger. LEVEL OF EVIDENCE: Level II. TYPE OF STUDY: Study of Diagnostic Test.

Indexed as

Child AbuseElectronic Health RecordsChildChild, PreschoolChild Protective ServicesHospitalsHumansPhysical AbuseAbuse screeningChild abuseClinical decision supportElectronic health recordEmergency departmentMeasurement

Identifiers

PMID37953157
PMCPMC10842334
OpenAlexW4387801869

What OpenQuestion holds

Textmetadata
LicenceTDM
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.