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, Lausanne University Hospital
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)

Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 reverse transcription 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

Introduction

In December 2019, a new virus causing pneumonia of unknown etiology emerged in China. Its incidence exploded rapidly, first in the Wuhan region (Hubei province), then in the other regions of China and other countries in Southeast Asia. On January 30, 2020, the World Health Organization (WHO) declared this new coronavirus a “public health emergency of international concern.” [1] On the February 20, the first patient was diagnosed in Italy, in the Lombardy region. The epidemic then spread to other European countries, including Switzerland [2], and the first case was admitted to Lausanne University Hospital on February 28. On March 11, 2020 the WHO declared a pandemic, referring to the disease as Coronavirus disease 2019 or COVID-19. [3–5]

To face the COVID-19 pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), diagnostic laboratories had to develop reverse transcription polymerase chain reaction (RT-PCR) tests allowing the detection of SARS-CoV-2 RNA in patients suspected of contracting COVID-19. Our laboratory, the Institute of Microbiology (IMU), located in one of the five teaching hospitals of Switzerland, the Lausanne University Hospital (CHUV), rapidly developed RT-PCR to detect SARS-CoV-2 in clinical specimens. [6] Microbiological diagnosis of SARS-CoV-2 represents one of the pillars of the diagnosis of COVID-19. Indeed, RT-PCR is also the heart of the patient care and epidemic control process and will be the mainstay of several clinical studies.

Although our laboratory has extensive experience in the development of RT-PCR, the introduction of this new parameter represented a challenge in terms of speed of development. [7] It is also the first time that an introduced parameter has been used on such a large scale in such a short time; more than 10,000 tests were carried out in one month in the spring of 2020 [6], and even in a single week during the fall of 2020. This was possible thanks to automation and digitalization, to allow high-throughput and acceptable time to results. [7]. In this context, the IMU set strategies to ensure the quality and reliability of RT-PCR. This included the monitoring of key performance indicators (KPIs) for quality management such as the proportions of positive tests or the virus load, both per day, per instruments, and per requester. These indicators aimed to identify variations not explained by epidemiological changes. Indeed, abnormal variations could be synonymous with pre-analytical problems (e.g., a sampling problem, transport medium, etc.) or even analytical problems (e.g., mutation in the target sequences of PCRs associated with losses of sensitivity or specificities). The IMU also defined KPIs for operations management, such as the turnaround time (TAT). [8]

Before COVID-19, such indicators were monitored periodically, for example in the context of an annual report or retrospective studies. At the beginning of the COVID-19 outbreak, the IMU decided to follow these indicators frequently. Because the manual analyses were time-consuming, the monitoring of analytical and operational KPIs was carried out once a week initially, and then twice a week depending on the period. These analyses were also prone to error, due to multiple sources of information, repeated manual actions (e.g., copy/cut and paste), and the diversity of the data. All this information required a dashboard.

A dashboard is a graphical user interfaces (GUI) with a database. It allows users to retrieve the relevant information—often KPIs—in a specific context by representing the data in a meaningful and usable way. [9] (See Eckerson 2010 [10] for more details.) In management and business contexts, dashboards aim at turning the overwhelming information volume into an informative opportunity [11] and are part of visual analytics, defined by Cook and Thomas as the “science of analytical reasoning facilitated by interactive visual interface.” [12]

Like any other information technology in the healthcare industry, the dashboard is intended to improve efficiency. [13] Dashboards help monitor daily activities [14], such as tracking ongoing operations, a priority in healthcare institutions. [15] Providing easy access to this information helps the team to make better informed decisions [16], which could take a tremendous amount of time without the technology. [17] Correctly designed and built, dashboards improve the institution's efficiency while providing better quality of care. [18] Cheng et al. [19] offer an example of this in their study on how to build a dashboard to track respiratory viruses like influenza.

Notes

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.