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Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool.
Meer, Elana A; Herriman, Maguire; Lam, Doreen; Parambath, Andrew; Rosin, Roy; Volpp, Kevin G; Chaiyachati, Krisda H; McGreevey, John D.
  • Meer EA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Herriman M; Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Lam D; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Parambath A; Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Rosin R; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Volpp KG; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • Chaiyachati KH; Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
  • McGreevey JD; Penn Medicine Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States.
Appl Clin Inform ; 12(5): 1021-1028, 2021 10.
Article in English | MEDLINE | ID: covidwho-1500797
ABSTRACT

OBJECTIVE:

We 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.

METHODS:

We 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.

RESULTS:

From 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%.

CONCLUSION:

The 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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Triage / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: Appl Clin Inform Year: 2021 Document Type: Article Affiliation country: S-0041-1736627

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Triage / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Journal: Appl Clin Inform Year: 2021 Document Type: Article Affiliation country: S-0041-1736627