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

02/03/2022

By: Lin Shen, MD, MBI; Zoe Burns, MPH; and Sonali Desai, MD, MPH

Diagnostic error is a safety issue affecting 12 million outpatient adults and 0.7% of adult hospital admissions in the United States each year. 

But how is diagnostic error affected by a global pandemic?

COVID-19 has strained our health care system, impacting hospital capacity, staffing and burnout. The resulting disruptions (e.g., care deferment, cognitive errors, changes to workflows) can contribute to missed or delayed diagnoses. A framework proposed by Gandhi and Singh, predicted the pandemic’s effects on staff, operational workflows and hospital resources would result in eight categories of missed or delayed diagnoses:

1. Classic: Missed or delayed COVID-19 diagnosis in a patient with respiratory symptoms
2. Anomalous: Missed or delayed COVID-19 diagnosis in a patient without respiratory symptoms
3. Anchor: Missed or delayed non-COVID-19 diagnosis because it was assumed to be COVID-19
4. Secondary: Missed or delayed non-COVID-19 diagnosis or secondary diagnosis in a patient being treated with known COVID-19 disease
5. Acute Collateral: Delayed acute non-COVID-19 diagnosis because patients are not coming in for evaluation due to infection risk
6. Chronic Collateral: Delayed diagnosis of non-COVID conditions when appointments or elective procedures are canceled
7. Strain: Missed or delayed non-COVID-19 diagnosis in non-COVID-19 patient because of heightened state of attention to COVID-19 patients in an overwhelmed system
8. Unintended: Any missed or delayed diagnosis because of less direct interactions, including rapid increase of telemedicine and personal protective equipment (PPE)

Our team applied this framework to 14,230 real-world safety reports submitted to our hospital between March 1, 2020 and February 28, 2021, as detailed in the February 2022 issue of The Joint Commission Journal on Quality and Patient Safety. Safety reports were identified for manual review via one of two “Pathways”:

  • Pathway 1: Explicit mention of COVID-19 in the safety report (n=1,780)
  • Pathway 2: Natural Language Processing (NLP) and logic-based algorithm applied to identify potential diagnostic error/delay (n=110)

Manual review of these safety reports (N=1,890) yielded a total of 95 cases of diagnostic error or delay (Pathway 1 N=45; Pathway 2 N=50). Different categories of errors occurred throughout the various stages of the pandemic, as our knowledge of the virus and its management developed. At first, diagnostic error types varied, with Strain, Unintended, Anomalous and Chronic Collateral categories being most common. By June 2020, more and more diagnostic errors were attributed to Strain, meaning non-COVID-19 diagnoses were being missed or delayed because of an overwhelmed system and increased attention to COVID-19 positive patients. 

Ultimately, over the course of the year, the Stain category made up 35.6% of reports identified through Pathway 1 and 94% of those identified via Pathway 2. With such an overwhelming proportion of safety reports from Pathway 2 being attributed to Strain, the team decided to further analyze their content. 

Additional qualitative content review of the 110 safety reports identified by Pathway 2 exposed the following primary safety event contributors:

  • Supply vs demand imbalance (N=39): Hospital resources are insufficient to meet prompt clinical demand
  • Patient handoff (N=33): Communication challenge or disagreement between providers, teams or services during patient transfer or shift change
  • Care provider fatigue and burden (N=25): Decision-making error by staff
  • COVID-19 status uncertainty (N=5): Unclear COVID-19 infection status

Our study led to key takeaways and insight for future safety risk, including that real-time data mining of safety reports brings attention to drivers of safety events and can lead to detection of early signals of trends. Additionally, early recognition of safety reporting patterns helps inform a more robust response to ensure that additional resources are not overlooked in key low-visibility areas.

Lin Shen, MD, MBI, is Director of Clinical Informatics in the Division of Gastroenterology, Hepatology and Endoscopy in the Department of Medicine at Brigham and Women’s Hospital (BWH) in Boston. Dr. Shen also is an Instructor in Medicine at Harvard Medical School in Boston. 

Zoe Burns, MPH, is Program Manager for the Department of Quality and Safety at BWH. 

Sonali Desai, MD, MPH, is Interim Chief Quality Officer at BWH. Dr. Desai also is an Associate Professor of Medicine at Harvard Medical School.