A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study

Haipeng Zhang; Dimitar Dimitrov; Lynn Simpson; Balaji Singh; Nina Plaks; Stephen Penney; Jo Charles; Rosemary Sheehan; Steven Flammini; Shawn Murphy; Adam Landman

Over the course of the first full week of deployment across the enterprise (Mar 30, 2020 -April 5, 2020), 154,730 employee attestation logs were processed. Over this 7-day period, 558 (0.36%) employees reported positive symptoms. Table 1 summarizes the number of employee attestations (using either COVID Pass or manual process) by day and site. In most sites, most employees (~80-90%) used the COVID Pass application, with a smaller percentage (~10-20%) using manual attestation ( Table 2) . Hospital staff continued to work around the clock, but as expected staff attestations peaked during shift changes between 7-8am, 2-3pm, 4-6pm, and 11pm-midnight ( Figure 3 ). author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Making COVID Pass mandatory to gain access to work and pairing it with the distribution of masks quickly made the potential burden of a mandatory pre-work self-attestation more of a palatable proposition. COVID Pass would further benefit from the minimization of friction to complete the attestation process. We did this by minimizing the amount of interactions users would need to complete COVID Pass while maximizing the data captured. By auto-populating demographic information after login, the user only had to answer one question symptom review. If asymptomatic, we required attestation with their initials to receive the COVID Pass for the day. We also simplified access to COVID Pass through the creation of multiple distribution channels including a simple URL that was sent with all communication to staff throughout our organization, a QR code that was used on flyers in the entranceways of our facilities, and also an iOS and Android version of COVID Pass made available through our employee facing App Catalog.