Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic.
Jt Comm J Qual Patient Saf
; 48(2): 71-80, 2022 02.
Article
in English
| MEDLINE | ID: covidwho-1487819
ABSTRACT
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 (nâ¯=â¯1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV]â¯=â¯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 (PPVâ¯=â¯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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Jt Comm J Qual Patient Saf
Journal subject:
Health Services
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS