The Use of a Self-triage Tool to Predict COVID-19 Cases and Hospitalizations in the State of Georgia.

INTRODUCTION: The coronavirus 2019 (COVID-19) pandemic has created significant burden on healthcare systems throughout the world. Syndromic surveillance, which collects real-time data based on a range of symptoms rather than laboratory diagnoses, can help provide timely information in emergency response. We examined the effectiveness of a web-based COVID-19 symptom checking tool (C19Check) in the state of Georgia (GA) in predicting COVID-19 cases and hospitalizations. METHODS: We analyzed C19Check use data, COVID-19 cases, and hospitalizations from April 22-November 28, 2020. Cases and hospitalizations in GA were extracted from the Georgia Department of Public Health data repository. We used the Granger causality test to assess whether including C19Check data can improve predictions compared to using previous COVID-19 cases and hospitalizations data alone. Vector autoregression (VAR) models were fitted to forecast cases and hospitalizations from November 29 - December 12, 2020. We calculated mean absolute percentage error to estimate the errors in forecast of cases and hospitalizations. RESULTS: There were 25,861 C19Check uses in GA from April 22-November 28, 2020. Time-lags tested in Granger causality test for cases (6-8 days) and hospitalizations (10-12 days) were significant (P= <0.05); the mean absolute percentage error of fitted VAR models were 39.63% and 15.86%, respectively. CONCLUSION: The C19Check tool was able to help predict COVID-19 cases and related hospitalizations in GA. In settings where laboratory tests are limited, a real-time, symptom-based assessment tool can provide timely and inexpensive data for syndromic surveillance to guide pandemic response. Findings from this study demonstrate that online symptom-checking tools can be a source of data for syndromic surveillance, and the data may help improve predictions of cases and hospitalizations.

Development and Usability Testing of a Web-based COVID-19 Self-triage Platform.

INTRODUCTION: The development and deployment of a web-based, self-triage tool for severe respiratory syndrome coronavirus 2 (COVID-19 disease) aimed at preventing surges in healthcare utilization could provide easily understandable health guidance with the goal of mitigating unnecessary emergency department (ED) and healthcare visits. We describe the iterative development and usability testing of such a tool. We hypothesized that adult users could understand and recall the recommendations provided by a COVID-19 web-based, self-triage tool. METHODS: We convened a multidisciplinary panel of medical experts at two academic medical schools in an iterative redesign process of a previously validated web-based, epidemic screening tool for the current COVID-19 pandemic. We then conducted a cross-sectional usability study over a 24-hour period among faculty, staff, and students at the two participating universities. Participants were randomly assigned a pre-written health script to enter into the self-triage website for testing. The primary outcome was immediate recall of website recommendations. Secondary outcomes included usability measures. We stratified outcomes by demographic characteristics. RESULTS: A final sample of 877 participants (mean age, 32 years [range, 19-84 years]; 65.3% female) was used in the analysis. We found that 79.4% of the participants accurately recalled the recommendations provided by the website. Almost all participants (96.9%) found the website easy to use and navigate. CONCLUSION: Adult users of a COVID-19 self-triage website, recruited from an academic setting, were able to successfully recall self-care instructions from the website and found it user-friendly. This website appears to be a feasible way to provide evidence-based health guidance to adult patients during a pandemic. Website guidance could be used to reduce unnecessary ED and healthcare visits.