Journal:An automated dashboard to improve laboratory COVID-19 diagnostics management
| Full article title | An automated dashboard to improve laboratory COVID-19 diagnostics management |
|---|---|
| Journal | Frontiers in Digital Health |
| Author(s) | Maury, Emma; Boldi, Marc-Olivier; Greub. Gilbert; Chavez, Valérie; Jaton, Katia; Opota, Onya |
| Author affiliation(s) | University of Lausanne, University Hospital of Lausanne |
| Primary contact | Email: onya dot opota at chuv dot ch |
| Editors | Hochheiser, Harry |
| Year published | 2021 |
| Volume and issue | 3 |
| Article # | 773986 |
| DOI | 10.3389/fdgth.2021.773986 |
| ISSN | 2673-253X |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/articles/10.3389/fdgth.2021.773986/full |
| Download | https://www.frontiersin.org/articles/10.3389/fdgth.2021.773986/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) systems in a very short time. More than 148,000 tests have been performed over 12 months, which represents about 405 tests per day, with peaks to more than 1,500 tests per days during the second wave. This was only possible thanks to automation and digitalization, to allow high-throughput, acceptable time to results and to maintain test reliability. An automated dashboard was developed to give access to key performance indicators (KPIs) to improve laboratory operational management.
Methods: RT-PCR data extraction of four respiratory viruses—SARS-CoV-2, influenza A and B, and RSV—\—from our laboratory information system (LIS) was automated. This included age, gender, test result, RT-PCR instrument, sample type, reception time, requester, hospitalization status, etc. Important KPIs were identified and the visualization was achieved using an in-house dashboard based on the R open-source language (Shiny).
Results: The dashboard is organized into three main parts. The “Filter” page presents all the KPIs, divided into five sections: (i) general and gender-related indicators, (ii) number of tests and positivity rate, (iii) cycle threshold and viral load, (iv) test durations, and (v) not valid results. Filtering allows to select a given period, a dedicated instrument, a given specimen, an age range, or a requester. The “Comparison” page allows custom charting of all the available variables, which represents more than 182 combinations. The “Data” page gives the user access to the raw data in a tabular format, with the possibility of filtering, allowing for a deeper analysis and data download. Information is updated every four hours.
Conclusions: By giving rapid access to a huge number of up-to-date data points, represented using the most relevant visualization types without the burden of timely data extraction and analysis, the dashboard represents a reliable and user-friendly tool for operational laboratory management, improving the decision-making process, resource planning, and quality management.
Keywords: COVID-19, medical microbiology, dashboard, digitalization, operations management, quality management, key performance indicator
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This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. In some cases important information was missing from the references, and that information was added. The original article lists references in alphabetical order; however, this version lists them in order of appearance, by design.
