Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

HSR&D Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic.

Shen L, Levie A, Singh H, Murray K, Desai S. Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic. Joint Commission Journal on Quality and Patient Safety. 2022 Feb 1; 48(2):71-80.

Related HSR&D Project(s)

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions


INTRODUCTION: COVID-19 exposed systemic gaps with increased potential for diagnostic error. This project implemented a new approach leveraging electronic safety reporting to identify and categorize diagnostic errors during the pandemic. METHODS: All safety event reports from March 1, 2020, to February 28, 2021, at an academic medical center were evaluated using two complementary pathways (Pathway 1: all reports with explicit mention of COVID-19; Pathway 2: all reports without explicit mention of COVID-19 where natural language processing [NLP] plus logic-based stratification was applied to identify potential cases). Cases were evaluated by manual review to identify diagnostic error/delay and categorize error type using a recently proposed classification framework of eight categories of pandemic-related diagnostic errors. RESULTS: A total of 14,230 reports were included, with 95 (0.7%) identified as cases of diagnostic error/delay. Pathway 1 (n? = 1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV]? = 2.5%), of which 35.6% (16/45) were attributed to pandemic-related strain. In Pathway 2, the NLP-based algorithm flagged 110 safety reports for manual review from 12,450 eligible reports. Of these, 50 reports had diagnostic error/delay (PPV? = 45.5%); 94.0% (47/50) were related to strain. Errors from all eight categories of the taxonomy were found on analysis. CONCLUSION: An event reporting-based strategy including use of simple-NLP-identified COVID-19-related diagnostic errors/delays uncovered several safety concerns related to COVID-19. An NLP-based approach can complement traditional reporting and be used as a just-in-time monitoring system to enable early detection of emerging risks from large volumes of safety reports.

Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.