Evidence map›Paper›PMID 36162434›Full record

ArticleApplied clinical informatics2022

Decision Support to Improve Critical Care Services Documentation in an Academic Emergency Department.

Robert W Turer, John C Champion, Brian S Rothman, Heather S Dunn, Kenneth M Jenkins, Olayinka Everham, Tyler W Barrett, Ian D Jones, Michael J Ward, Nathaniel M Miller

Abstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Robert W TurerDepartment of Emergency Medicine and Clinical Informatics Center, UT Southwestern Medical Center, Dallas, Texas, United States.
John C ChampionDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Brian S RothmanDepartment of Anesthesiology, Vanderbilt Medical Center, Nashville, Tennessee, United States.
Heather S DunnDepartment of Finance, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Kenneth M JenkinsDepartment of Compliance, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Olayinka EverhamHealth IT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Tyler W BarrettDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Ian D JonesDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Michael J WardDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Nathaniel M MillerDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCritical care services (CCS) documentation affects billing, operations, and research. No studies exist on documentation decision support (DDS) for CCS in the emergency department (ED). We describe the design, implementation, and evaluation of a DDS tool built to improve CCS documentation at an academic ED.

methodsThis quality improvement study reports the prospective design, implementation, and evaluation of a novel DDS tool for CCS documentation at an academic ED. CCS-associated ED diagnoses triggered a message to appear within the physician note attestation workflow for any patient seen in the adult ED. The alert raised awareness of CCS-associated diagnoses without recommending specific documentation practices. The message disappeared from the note automatically once signed. We measured current procedural terminology (CPT) codes 99291 or 99292 (representing CCS rendered) for 8 months before and after deployment to identify CCS documentation rates. We performed state-space Bayesian time-series analysis to evaluate the causal effect of our intervention on CCS documentation capture. We used monthly ED volume and monthly admission rates as covariate time-series for model generation.

resultsThe study included 92,350 ED patients with an observed mean proportion CCS of 3.9% before the intervention and 5.8% afterward. The counterfactual model predicted an average response of 3.9% [95% CI 3.5-4.3%]. The estimated absolute causal effect of the intervention was 2.0% [95% CI 1.5-2.4%] (

conclusionA DDS tool measurably increased ED CCS documentation. Attention to user workflows and collaboration with compliance and billing teams avoided alert fatigue and ensures compliance.

Indexed as

DocumentationEmergency Service, HospitalAdultBayes TheoremCritical CareHumansWorkflow

Identifiers

PMID36162434
PMCPMC9668511

What OpenQuestion holds

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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.