Cargando…
An Automated Dashboard to Improve Laboratory COVID-19 Diagnostics Management
Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685224/ https://www.ncbi.nlm.nih.gov/pubmed/34939067 http://dx.doi.org/10.3389/fdgth.2021.773986 |
Sumario: | Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 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, and hospitalization status etc. Important KPIs were identified and the visualization was achieved using an in-house dashboard based on the open-source language R (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 a custom charting of all the available variables, which represents more than 182 combination. The “Data” page, gives the user an access to the raw data in tables format, with possibility of filtering, allowing for a deeper analysis and data download. Informations are updated every 4 h. Conclusions: By giving a rapid access to a huge number of up-to-date information, 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. The dashboard represents a reliable and user-friendly tool improving the decision-making process, resource planning and quality management. |
---|