Creating Surveillance Data Infrastructure Using Laboratory Analytics: Leveraging Visiun and Epic Systems to Support COVID-19 Pandemic Response

Abstract

Background: Pandemics are unpredictable and can rapidly spread. Proper planning and preparation for managing the impact of outbreaks is only achievable through continuous and systematic collection and analysis of health-related data. We describe our experience on how to comply with required reporting and develop a robust platform for surveillance data during an outbreak.

Materials and methods: At Mount Sinai Health System, New York City, we applied Visiun, a laboratory analytics dashboard, to support main response activities. Epic System Inc.'s SlicerDicer application was used to develop clinical and research reports. We followed World Health Organization (WHO); federal and state guidelines; departmental policies; and expert consultation to create the framework.

Results: The developed dashboard integrated data from scattered sources are used to seamlessly distribute reports to key stakeholders. The main report categories included federal, state, laboratory, clinical, and research. The first two groups were created to meet government and state reporting requirements. The laboratory group was the most comprehensive category and included operational reports such as performance metrics, technician performance assessment, and analyzer metrics. The close monitoring of testing volumes and lab operational efficiency was essential to manage increasing demands and provide timely and accurate results. The clinical data reports were valuable for proper managing of medical surge requirements, such as healthcare workforce and medical supplies. The reports included in the research category were highly variable and depended on healthcare setting, research priorities, and available funding. We share a few examples of queries that were included in the designed framework for research projects.

Conclusion: We reviewed here the key components of a conceptual surveillance framework required for a robust response to COVID-19 pandemics. We demonstrated leveraging a lab analytics dashboard, Visiun, combined with Epic reporting tools to function as a surveillance system. The framework could be used as a generic template for possible future outbreak events.

Keywords: COVID-19; lab analytics; pandemic; surveillance; visiun.

Fig. 1

(A) Order panel for COVID-19 viral testing. (B) Order panel for COVID-19 antibody testing.

Fig. 2

The data flow of laboratory-based data and user interaction points with Visiun.

Fig. 3

Ad-hoc report setting.

Fig. 4

Heat map shows the distribution of COVID-19-positive patients in New York city and surrounding counties (run on September 14, 2020).

Fig. 5

The map shows the geographical distribution marked by the density of color. Source: https://www1.nyc.gov/site/doh/covid/covid-19-data.page.

Fig. 6

This diagram shows COVID-19 viral testing TAT. The accepted TAT is set as <10 h (green line). The largest batches of samples (red dotted line) are received at 1 am, 2 pm, and 9 pm.

Fig. 7

The chart demonstrates the number of the resulted tests grouped by average TAT. About 50% of the viral tests are resulted in <5.5 h.

Fig. 8

The diagram shows the frequency of incoming batches, batch sizes, and ratio of stat samples vs. non-stat.

Fig. 9

The lag time between the first positive COVID-19 test and first negative result. The diagram shows that the COVID-19 viral test becomes negative in 3 weeks in approximately 50% of the patients.

Fig. 10

Time course of developing COVID-19 antibody. Seroconversion takes place within the first week in the majority of infected patients.