Selected article for: "application method and COVID Pass manual screening automatic screening"
Author: Haipeng Zhang; Dimitar Dimitrov; Lynn Simpson; Balaji Singh; Nina Plaks; Stephen Penney; Jo Charles; Rosemary Sheehan; Steven Flammini; Shawn Murphy; Adam Landman
Title: A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study
  • Document date: 2020_4_22
    • ID: ey2onl0j_25
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.17.20069211 doi: medRxiv preprint All versions of COVID Pass recorded employee attestations. The data were aggregated at the site level and shared with site operational leaders daily; individuals reporting positive symptoms were shared with Human Resources leadership daily. In this report, we share data from the first week of full enterpr.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.17.20069211 doi: medRxiv preprint All versions of COVID Pass recorded employee attestations. The data were aggregated at the site level and shared with site operational leaders daily; individuals reporting positive symptoms were shared with Human Resources leadership daily. In this report, we share data from the first week of full enterprise COVID Pass use (March 30, 2020 -April 5, 2020) from the REDCap application database, the Office 365 spreadsheets, and the .NET Framework COVID Pass application. Aggregate descriptive statistics including overall employee attestations by day and site, employee attestations by application method (COVID Pass automatic screening vs. manual screening), employee attestations by time of day, and % of employees reporting COVID-19 symptoms were compiled using SAS (Enterprise Guide 7.1, SAS Institute Inc, Cary, NC) and Microsoft Excel (Microsoft Corporation, Redmond, WA). This study was approved by the Partners HealthCare Institutional Review Board.

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