Evidence map›Paper›PMID 34734403›Full record

ArticleApplied clinical informatics2021

Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool.

Elana A Meer, Maguire Herriman, Doreen Lam, Andrew Parambath, Roy Rosin, Kevin G Volpp, Krisda H Chaiyachati, John D McGreevey

Abstract read
In one paragraph

Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Elana A Meer *Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Maguire Herriman *Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Doreen LamPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Andrew ParambathPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Roy RosinCenter for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Kevin G VolppPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Krisda H ChaiyachatiPenn Medicine Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States.
John D McGreeveyPerelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.

Funding

Research ProgramP50CA244690 · NCI · UNIVERSITY OF PENNSYLVANIA · PI SHULMAN, LAWRENCE N. · 2020 to 2024
$4.9M
Telemedicine Utilization and Impacts on Access Among Rural Medicare BeneficiariesK08AG065444 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHAIYACHATI, KRISDA H · 2021 to 2022
$340k
NCI NIH HHS P50 CA244690NIA NIH HHS K08 AG065444
6 · The paper itself

Abstract

objectiveWe describe the design, implementation, and validation of an online, publicly available tool to algorithmically triage patients experiencing severe acute respiratory syndrome coronavirus (SARS-CoV-2)-like symptoms.

methodsWe conducted a chart review of patients who completed the triage tool and subsequently contacted our institution's phone triage hotline to assess tool- and clinician-assigned triage codes, patient demographics, SARS-CoV-2 (COVID-19) test data, and health care utilization in the 30 days post-encounter. We calculated the percentage of concordance between tool- and clinician-assigned triage categories, down-triage (clinician assigning a less severe category than the triage tool), and up-triage (clinician assigning a more severe category than the triage tool) instances.

resultsFrom May 4, 2020 through January 31, 2021, the triage tool was completed 30,321 times by 20,930 unique patients. Of those 30,321 triage tool completions, 51.7% were assessed by the triage tool to be asymptomatic, 15.6% low severity, 21.7% moderate severity, and 11.0% high severity. The concordance rate, where the triage tool and clinician assigned the same clinical severity, was 29.2%. The down-triage rate was 70.1%. Only six patients were up-triaged by the clinician. 72.1% received a COVID-19 test administered by our health care system within 14 days of their encounter, with a positivity rate of 14.7%.

conclusionThe design, pilot, and validation analysis in this study show that this COVID-19 triage tool can safely triage patients when compared with clinician triage personnel. This work may signal opportunities for automated triage of patients for conditions beyond COVID-19 to improve patient experience by enabling self-service, on-demand, 24/7 triage access.

Indexed as

COVID-19TriageHumansSARS-CoV-2

Identifiers

PMID34734403
PMCPMC8566110

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.