Clinical labs employed two effective data analytics-based strategies to meet COVID-19 testing demands during the height of the pandemic, the American Association for Clinical Chemistry (AACC) said Wednesday.
Findings on the efficacy of these strategies, published this month in The Journal of Applied Laboratory Medicine, give labs a blueprint for using data analytics to ensure patient access to testing during future infectious disease outbreaks.
Keeping up with testing demands during chronic supply shortages became a major challenge during the first two years of the pandemic. It’s only a matter of time before labs could be grappling with this problem again—either because of a surge in COVID-19 cases caused by a new variant, or because of a new infectious disease outbreak entirely, noted AACC, the journal publisher.
In one study, a team of researchers led by Dr. Rohit Sangal, who works in the emergency medicine department at Yale University School of Medicine, describes labs' use of electronic health record systems (EHRs) to ensure that scarce testing resources are optimally allocated during a pandemic.
During the omicron variant surge of December 2021 and January 2022, Sangal’s healthcare system developed guidelines to ensure that limited SARS-CoV-2 tests and combined tests for SARS-CoV-2, flu, and RSV were used on the appropriate patients. To help clinicians adhere to these guidelines when ordering tests, the team implemented a redesign of the EHR’s test ordering interface.
Following this, the group analyzed test ordering data from the three weeks before the redesign and the three weeks after and found that the EHR redesign successfully changed testing patterns to align with guidelines.
For symptomatic patients who were discharged from the emergency department, COVID-19 + flu/RSV testing decreased 49% while testing for COVID-19 + flu-only increased 160%.
Meanwhile, for symptomatic patients who were admitted to the hospital, COVID-19 + flu/RSV testing increased 128%. Not only does this mean that the right patients were getting the right tests, but these changes also saved approximately 437 test cartridges per week, thereby preserving the limited supply of testing resources, AACC said.
“A simple EHR order redesign was associated with increased adherence to institutional guidelines for SARS-CoV-2 and influenza testing amidst supply chain limitations necessitating optimal allocation of scarce testing resources,” Sangal said in a statement. “With continually shifting resource availability, clinician education is not sufficient. Rather, system-based interventions embedded within existing workflows can better align resources and serve the testing needs of the community.”
AACC said that the study findings build on data analytics work from the start of the pandemic led by Dr. Daniel Holmes, who works in the Department of Pathology and Laboratory Medicine at St. Paul’s Hospital in Vancouver, Canada. The data analytics work of Holmes and his colleagues, also published this month in The Journal of Applied Laboratory Medicine, showed how labs can use data automation to handle outbreak-related testing surges.
During the first year of the pandemic, supply shortages for SARS-CoV-2 testing instruments began to affect the ability of St. Paul’s Hospital to keep up with testing demand. In response, Holmes’s team used open-source software tools (Linux, bash, R, RShiny, ShinyProxy, and Docker) to develop an automated workflow that manages and optimizes all steps of the SARS-CoV-2 testing process. From September through December 2020, this automated workflow decreased the lab’s consumption of reagents for SARS-CoV-2 testing by approximately 58%.
“We describe our strategy for data automation for singleton and pooled sample [testing] for SARS-CoV-2 with extension to other viral PCR assays,” the authors said in the study. “The open-source software tools used and the software development and operational deployment strategy are explained. This work will give the reader direction on how to develop and deploy similar tools should the need arise.”