Evidence map›Paper›PMID 40763805›Full record

ArticleApplied clinical informatics2025

Disappearing Text as a Clinical Decision Support Layer: A Case Series.

Jared Silberlust, William Small, Darhsi Shah, Eesha Chakravartty, Katherine Moawad, Andrew Moawad, Paul Testa, Jonah Feldman

Abstract read
In one paragraph

Article in Applied clinical informatics, 2025. 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
–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

0 citing papers in PubMed.

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

8 authors.

Jared SilberlustMCIT Department of Health Informatics, NYU Langone Health, New York, New York, United States.
William SmallMCIT Department of Health Informatics, NYU Langone Health, New York, New York, United States.
Darhsi ShahRenaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States.
Eesha ChakravarttyDepartment of Medicine, Cedars-Sinai, Los Angeles, California, United States.
Katherine MoawadDepartment of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
Andrew MoawadWell by Messer, New York, New York, United States.
Paul TestaMCIT Department of Health Informatics, NYU Langone Health, New York, New York, United States.
Jonah FeldmanMCIT Department of Health Informatics, NYU Langone Health, New York, New York, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This case series aims to evaluate several applications of inline disappearing text (DT) clinical decision support (CDS) tools within clinician documentation.DT blocks were created to prompt documentation for perioperative anticoagulation planning (scenario 1), predischarge intravenous antibiotic planning (scenario 2), and advanced care planning (ACP; scenario 3). In scenario 1, DT was the only intervention. In scenario 2, DT was paired with a documentation SmartList. In scenario 3, DT was paired with a documentation SmartList and an OurPractice advisory. The number of documented perioperative anticoagulation plans, predischarge intravenous antibiotic plans, and ACP notes was measured pre- and postintervention and compared using chi-square analyses.In scenario 1, there was no statistically significant change in the percentage of perioperative anticoagulation plans documented at 0 to 24 and 24 to 48 hours before surgery. In scenario 2, documentation of antibiotic contingency planning in patients expected to be discharged within 24 hours increased from 60% (54/90 notes) to 93% (1,850/1,994 notes)

Indexed as

Decision Support Systems, ClinicalDocumentationHumans

Identifiers

PMID40763805
PMCPMC12443503

What OpenQuestion holds

Textmetadata
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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.