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Use of a Telemedicine Risk Assessment Tool to Predict the Risk of Hospitalization of 496 Outpatients With COVID-19: Retrospective Analysis.
O'Keefe, James B; Tong, Elizabeth J; Taylor, Thomas H; O'Keefe, Ghazala A Datoo; Tong, David C.
  • O'Keefe JB; Division of General Internal Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States.
  • Tong EJ; Division of General Internal Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States.
  • Taylor TH; Taylor Engineering, Inc, Atlanta, GA, United States.
  • O'Keefe GAD; Section of Vitreoretinal Surgery and Diseases, Section of Uveitis and Vasculitis, Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA, United States.
  • Tong DC; Division of Hospital Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States.
JMIR Public Health Surveill ; 7(4): e25075, 2021 04 30.
Article in English | MEDLINE | ID: covidwho-2141297
ABSTRACT

BACKGROUND:

Risk assessment of patients with acute COVID-19 in a telemedicine context is not well described. In settings of large numbers of patients, a risk assessment tool may guide resource allocation not only for patient care but also for maximum health care and public health benefit.

OBJECTIVE:

The goal of this study was to determine whether a COVID-19 telemedicine risk assessment tool accurately predicts hospitalizations.

METHODS:

We conducted a retrospective study of a COVID-19 telemedicine home monitoring program serving health care workers and the community in Atlanta, Georgia, with enrollment from March 24 to May 26, 2020; the final call range was from March 27 to June 19, 2020. All patients were assessed by medical providers using an institutional COVID-19 risk assessment tool designating patients as Tier 1 (low risk for hospitalization), Tier 2 (intermediate risk for hospitalization), or Tier 3 (high risk for hospitalization). Patients were followed with regular telephone calls to an endpoint of improvement or hospitalization. Using survival analysis by Cox regression with days to hospitalization as the metric, we analyzed the performance of the risk tiers and explored individual patient factors associated with risk of hospitalization.

RESULTS:

Providers using the risk assessment rubric assigned 496 outpatients to tiers Tier 1, 237 out of 496 (47.8%); Tier 2, 185 out of 496 (37.3%); and Tier 3, 74 out of 496 (14.9%). Subsequent hospitalizations numbered 3 out of 237 (1.3%) for Tier 1, 15 out of 185 (8.1%) for Tier 2, and 17 out of 74 (23%) for Tier 3. From a Cox regression model with age of 60 years or older, gender, and reported obesity as covariates, the adjusted hazard ratios for hospitalization using Tier 1 as reference were 3.74 (95% CI 1.06-13.27; P=.04) for Tier 2 and 10.87 (95% CI 3.09-38.27; P<.001) for Tier 3.

CONCLUSIONS:

A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified populations with low, intermediate, and high risk of hospitalization.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Telemedicine / Risk Assessment / Ambulatory Care / COVID-19 / Hospitalization Type of study: Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: JMIR Public Health Surveill Year: 2021 Document Type: Article Affiliation country: 25075

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Telemedicine / Risk Assessment / Ambulatory Care / COVID-19 / Hospitalization Type of study: Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: JMIR Public Health Surveill Year: 2021 Document Type: Article Affiliation country: 25075