Diagnostic error has been increasingly recognized as an important and evolving patient safety issue. Amid a global pandemic of infection due to a novel coronavirus (SARS-CoV-2), delayed diagnosis of COVID-19 may lead to preventable transmission to other individuals, including exposed family members and health care workers,1 and delayed initiation of effective treatment.2 Perhaps even more importantly, other treatable diagnoses may be missed as clinicians focus on suspected or confirmed COVID-19.3 As state4 and federal5 governments relax restrictions on social activity, there is increasing focus on the health care system’s ability to diagnose cases rapidly and accurately, and the public health system’s ability to follow up with thorough contact tracing and evaluation.6
This Primer is an early review, applying well-established principles of diagnostic error and improving diagnostic accuracy to the topic of COVID-19. It will be updated over time to incorporate new information and evidence in this rapidly evolving field. The embedded links are intended to refer readers to potentially relevant resources previously published on PSNet, while resources from other Federal agencies and the peer-reviewed literature are listed at the end.
Biases in the Diagnostic Process
Clinician decision-making often relies on cognitive thought processes - called heuristics - that facilitate rapid diagnosis and treatment in stressful circumstances. However, these same heuristics that support “fast and frugal” decision-making can lead to diagnostic errors. To avoid such errors, it is useful to understand why these heuristics lead to biases in the diagnostic process, and how these biases can be mitigated. This process has been described as “cognitive debiasing” or “meta-cognition.” It entails switching from “automatic thinking” (also called “intuitive” or System 1 reasoning) to more “reflective thinking” (also called “analytical” or System 2 reasoning), informed by communication with other team members and use of online resources.
The availability heuristic leads clinicians to overdiagnose conditions that are relatively available in their memories, based on recent reading or clinical encounters. At the same time, clinicians tend to underdiagnose conditions with which they have little direct or indirect experience. Specifically, availability affects how health care providers estimate the pre-test or prior probability of disease. In two WebM&M (Morbidity and Mortality Rounds on the Web) cases previously published on Patient Safety Network (PSNet), the serious diagnosis of aortic dissection – a diagnosis with which emergency department physicians have little experience – was missed as physicians focused on more common causes of “crushing chest pain” and right-sided abdominal pain.
In communities where the cumulative incidence of COVID-19 is low, the availability heuristic may cause clinicians to miss the diagnosis of COVID-19. This problem may arise in settings where health system policies or resource constraints limit testing,1 based on historical incidence data. Many communities in the US and elsewhere have seen sudden, exponential increases in the incidence of COVID-197 that illustrate the danger of relying on recent experience in estimating the probability of disease.
On the other hand, with heavy coverage of COVID-19 in both lay media and professional journals, availability bias may lead clinicians to miss other respiratory infections (e.g., Legionella, Pneumococcus, Mycoplasma, Chlamydia), exacerbations of asthma or chronic obstructive lung disease, or acute cardiovascular or neurologic disease because of so much recent experience with COVID-19.1 One commentator recently described this phenomenon as “COVID blindness.”8 Given the availability of effective therapies for these other conditions, it is important not to miss alternative diagnoses that may present with similar symptoms.9 The seasonal pattern of influenza virus transmission, and the effectiveness of antiviral therapy when initiated within 48 hours after onset of symptoms, will make this issue especially salient as winter approaches.9
The anchoring heuristic leads clinicians to resist altering their initial diagnostic impression, despite subsequent information that contradicts that impression. This phenomenon has also been described as premature closure on a diagnosis that turns out to be incorrect. Specifically, anchoring affects how much health care providers adjust their post-test or posterior probability estimates after new findings appear, or new test results become available. In a WebM&M case previously discussed in PSNet, a patient was treated six times over several months for presumed diabetic neuropathy while a more serious diagnosis of peripheral artery disease was missed. Other WebM&M cases have discussed a patient with glioblastoma multiforme whose physicians prematurely closed on a diagnosis of vasculitis, and a patient with a perforated esophagus whose physicians prematurely closed on a diagnosis of pneumonia.
In current circumstances, the anchoring heuristic may lead clinicians to miss the diagnosis of COVID-19 by putting insufficient weight on new findings that emerge after the patient’s initial presentation, failing to repeat diagnostic testing after an initially negative result, or failing to consider SARS-CoV-2 after another pathogen has been identified. Recent case series have shown that 2-6% of patients hospitalized with COVID-19 can have co-infections with other respiratory pathogens such as rhinoviruses, parainfluenza virus 3, respiratory syncytial virus, and Chlamydia pneumoniae.10 A positive test result for one of these pathogens may lead to premature closure and failure to consider the possibility of co-infection with COVID-19.
On the other hand, premature closure may lead clinicians to make a presumptive diagnosis of COVID-19 (allowing the patient to self-quarantine at home) while failing to order diagnostic tests that would point to other diagnoses. Even when the diagnosis of COVID-19 is confirmed, anchoring may impede clinicians from recognizing secondary bacterial infections and other treatable complications.1,9 To minimize anchoring and avoid premature closure, health care providers may consider such approaches as:
- Take a diagnostic time-out, or a deliberate pause to reassess the working diagnosis and consider other possibilities;
- Deliberately look for evidence that would question or challenge the working diagnosis, recognizing that clinicians tend to look for prototypical manifestations of disease through pattern recognition and fail to consider atypical variants (a problem known as “representativeness restraint”);
- Explicitly consider the risk of two co-occurring diagnoses, as in a WebM&M case where the diagnosis of sepsis was missed because of co-occurring tumor lysis syndrome;
- Use “artificial intelligence” or other predictive analytics to provide clinical decision support that estimates the probabilities of alternative diagnoses, electronically triggers testing for COVID-19, and/or queries patients automatically regarding symptoms (including symptoms recently identified by CDC);
- Carefully assess the response to initial treatment, such as antibiotic therapy for presumed bacterial pneumonia, and consider alternative diagnoses if that response is poor.
Framing biases lead clinicians to different diagnostic impressions based on how information (including irrelevant information) is presented or framed. In a WebM&M case previously discussed in PSNet, the diagnosis of colon perforation in a patient with opioid dependence was delayed because the patient was described to the covering physician as a "strung out shooter," leading that physician not to reevaluate the patient when he developed tachycardia and increased abdominal pain. In another case, a subdural (intracranial) hematoma was nearly missed because the triage nurse in the emergency department had framed a homeless patient who complained of “feeling wobbly” as having "bilateral knee pain."
In the context of COVID-19, framing biases may lead to diagnostic error if irrelevant information about patients’ national origin, race or ethnicity is included in case presentations and handoffs. For example, when clinicians focus on race/ethnicity instead of relevant exposures, such as labeling of SARS-CoV-2 as a “Wuhan” or Chinese virus, it may lead to overdiagnosis of COVID-19 in the Asian-American population or underdiagnosis of COVID-19 in other at-risk populations. Clinicians may consider the following approaches that have been effective in other settings:
- Avoid presenting identifying information that may not be relevant to clinical decision-making, such as the patient’s national origin, race or ethnicity, during consultations and handoffs. If the clinical topic of interest is recent international travel, for example, then clinicians should ask about and present findings related to travel history, without assuming a relationship between national origin and recent travel.
- Do not refer to patients as their disease state, such as COVID-19, because such framing dehumanizes the patient11 and may lead clinicians to miss comorbid diagnoses.
- Explicitly communicate diagnostic uncertainty, and the reasons for that uncertainty, during consultations and handoffs to avoid framing the patient’s condition in a way that fosters premature closure.
The disproportionate impact of COVID-19 on African-American and Hispanic communities in the US has highlighted the importance of conscious efforts to identify and address implicit biases that may be contributing to these disparities. Implicit bias involves associations outside conscious awareness that lead to a negative evaluation of a person based on irrelevant characteristics such as race and ethnicity, nationality, disability, and socio-economic status. Recognizing the presence of implicit biases in the diagnosis process and developing clinical interventions to train medical personnel may reduce biases in the diagnostic process.12 In the context of COVID-19, implicit racial/ethnic biases may hinder honest patient-provider communication about potential exposures and high-risk signs and symptoms.13
Diagnostic Testing for Active SARS-CoV-2 Infection
The National Academies’ Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats recently published a rapid expert consultation on SARS-CoV-2 laboratory testing.14 This report notes that “current clinical tests for SARS-CoV-2 rely on the detection of viral RNA, using reverse transcriptase polymerase chain reaction (RT-PCR) or loop-mediated isothermal amplification (LAMP), in nasopharyngeal (NP), oropharyngeal (OP), sputum or saliva samples.” The National Academies argue that “additional studies on the temporal dynamics of viral RNA in infected persons, across body sites and fluids, and correlations of these measurements with risk of transmission to other individuals, are sorely needed.” However, a few tentative conclusions can be drawn from recently published studies of viral antigen detection; serologic testing is outside the scope of this Primer.
Laboratory-based evaluations of currently used RT-PCR tests show high analytical sensitivity and near-perfect specificity with no misidentification of other common respiratory pathogens,15 but test sensitivity in clinical practice is lower due to variation in how specimens are obtained and handled, and the stage of illness when testing is performed.16 In recent research and clinical practice, serial testing has been used to establish the diagnosis of COVID-19 in the setting of high clinical suspicion. The false negative rate on initial testing of symptomatic patients, based on any positive result on serial testing (when clinically indicated), was reported as 2.5% to 29%17-22 in six Chinese studies, 3.2% in a more recent study from New York,10 and 4.3% in a large study from Seattle.23 According to a literature review and pooled analysis of data from seven studies with 1,330 respiratory samples, the estimated false-negative rate was 38% on the day of symptom onset, decreasing to a nadir of 20% on day 8.24 A subsequent large study from New York reported on 3,432 patients who had repeated testing; the clinical sensitivity of a single SARS-CoV-2 molecular assay was estimated between 58% and 96%, depending on the unknown number of false negative results in single-tested patients.25
In general, viral shedding appears to be greater in the nasopharynx than in the oropharynx,26 and more prevalent in specimens obtained from the lower respiratory tract (e.g., bronchoalveolar lavage fluid, sputum) than in specimens obtained from the upper respiratory tract.27-29 Clinical studies have confirmed that nasopharyngeal sampling is more sensitive than oropharyngeal sampling,29,30 and that sensitivity declines over the course of the infection.24 Recent evidence suggests that test sensitivity using posterior oropharyngeal saliva may approach that based on nasopharyngeal specimens,31,32 with better acceptability to patients, but additional studies of saliva testing are needed. Based on these findings and others, recent reviews33-36 and CDC guidance (linked below) recommend:
- For initial diagnostic testing for SARS-CoV-2, CDC recommends collecting an upper respiratory tract specimen using a nasopharyngeal swab, although oropharyngeal swabs remain an acceptable specimen type. Technical guidance regarding specimen acquisition is available from the CDC.
- Use only synthetic fiber swabs with plastic or wire shafts. Do not use calcium alginate swabs or swabs with wooden shafts, as they may contain substances that inactivate some viruses and inhibit PCR testing.
- Swabs should be placed immediately into a sterile transport tube containing 2-3 mL of an appropriate transport medium to preserve viral nucleic acid.
- For patients for whom it is clinically indicated (e.g., those receiving invasive mechanical ventilation), a lower respiratory tract aspirate or bronchoalveolar lavage sample may also be collected and tested. Collect 2-3 mL into a sterile, leak-proof, screw-cap sputum collection cup or sterile dry container.
The CDC has reported marked variation in the incidence of SARS-CoV-2 infection across the US.37 Factors contributing to this variation may include differences in the timing of introduction and early transmission of SARS-CoV-2, population density and resulting exposure to respiratory droplets, implementation and subsequent easing of community mitigation strategies, availability of SARS-CoV-2 testing, and the risk profile of the exposed populations (e.g., older adults or those with underlying medical conditions).
Tracking of the community-specific cumulative incidence and point prevalence of SARS-Cov-2 infection is now available from state and local health departments and from data aggregators such as the CDC (https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html), the University of Washington’s Institute for Health Metrics Evaluation (https://covid19.healthdata.org/united-states-of-america) and Johns Hopkins’ Coronavirus Resource Center (https://coronavirus.jhu.edu/map.html). Although these data sources do not provide identical information, due to gaps and lags in data capture, they can be used to help clinicians avoid availability bias by supporting better estimation of the pre-test or prior probability of COVID-19.
A simple example illustrates the importance of availability bias and how incorrect assumptions about disease prevalence lead to incorrect interpretations of test results (also known as post-test or posterior probabilities). These relationships are further explained in several open access papers and online tutorials.38,39 If a diagnostic test has 90% sensitivity and 100% specificity (i.e., on the upper end of values reported across the studies cited here),15,40 the accuracy or predictive value of a negative test result is about 99.5% when the true prevalence among tested patients is 5%, but only 71% when the true prevalence among tested patients is 80%. If the test sensitivity is only 70%, as may occur with inadequate specimen collection or early infection, then the false negative rate would be 23% when the pre-test probability of COVID-19 is 50%.41 Hence, the Infectious Diseases Society of America suggests repeating viral RNA testing when the initial test is negative in symptomatic individuals with an intermediate or high clinical suspicion of COVID-19.42 In individuals with a low clinical suspicion of COVID-19, a single negative test result may suffice, and serial testing may not be an effective use of limited resources.42
The COVID-19 pandemic reinforces the importance of accurate and timely communication of test results to both patients and health care providers, with appropriate interpretive guidance. Best practices regarding communication of clinically important test results,43 and design and implementation of electronic alerts for healthcare providers,44 have been published. At least two WebM&M cases previously discussed in PSNet have demonstrated the potential negative consequences of delayed or incorrect communication of abnormal test results, and suggested system improvements to prevent them.
COVID-19 Testing Guidance from CDC
See CDC’s Overview of Testing for SARS-CoV-2: https://www.cdc.gov/coronavirus/2019-ncov/hcp/testing-overview.html
See CDC’s Interim Guidelines for Collecting, Handling, and Testing Clinical Specimens for COVID-19: https://www.cdc.gov/coronavirus/2019-nCoV/lab/guidelines-clinical-specimens.html
See CDC’s Frequently Asked Questions about Coronavirus (COVID-19 for Laboratories: https://www.cdc.gov/coronavirus/2019-ncov/lab/faqs.html
Patrick S. Romano, MD MPH, on behalf of the AHRQ PSNet team
Professor of Medicine and Pediatrics, University of California Davis School of Medicine
Co-Editor-in-Chief, AHRQ PSNet
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- Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review [published online ahead of print, 2020 Jul 10]. JAMA. 2020;10.1001/jama.2020.12839.
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- California Roadmap to Modify the Stay at Home Order. Accessed April 25, 2020: https://www.gov.ca.gov/wp-content/uploads/2020/04/California-Roadmap-to-Modify-the-Stay-at-Home-Order.pdf
- Guidelines: Opening Up America Again. Accessed April 25, 2020: https://www.whitehouse.gov/openingamerica/#phase-one
- McClellan M, Gottlieb S, Mostashari F, Rivers C, Silvis L. A National COVID-19 Surveillance System: Achieving Containment. Duke Margolis Center for Health Policy. Accessed April 25, 2020: https://healthpolicy.duke.edu/sites/default/files/atoms/files/covid-19_surveillance_roadmap_final_0.pdf
- COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Accessed April 25, 2020: https://coronavirus.jhu.edu/map.html .
- Brown L. COVID Blindness. Diagnosis (Berl). 2020;7(2):83‐84. doi:10.1515/dx-2020-0042
- Coleman JJ, Manavi K, Marson EJ, Botkai AH, Sapey E. COVID-19: to be or not to be; that is the diagnostic question. Postgrad Med J. 2020;96(1137):392-398. doi:10.1136/postgradmedj-2020-137979.
- Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW; the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. doi:10.1001/jama.2020.677
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- Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas T, Payne BK, Eng E, Day SH, Coyne-Beasley T. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. Am J Public Health 2015;105(12):e60–e76. https://doi.org/10.2105/AJPH.2015.302903
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