ICD-10 codes don’t accurately capture COVID-19 symptoms, study shows

Kat Jercich for Healthcare IT News

A study published this past week in JAMA Network Open suggests symptom-specific ICD-10 codes don’t always capture COVID-19-related symptoms.

An electronic health record review of 2,201 patients who had been tested for COVID-19 found that ICD-10 codes had low sensitivity and negative predictive value for capturing cough, fever and labored breathing.

“Symptoms are an essential part of data collection for SARS-CoV-2 and COVID-19 surveillance and research, but symptom-specific ICD-10 codes lack sensitivity and fail to capture many patients with relevant symptoms; the false-negative rate is unacceptably high,” wrote researchers.


Researchers at the University of Utah reviewed the EHRs of 2,201 patients who had been tested at UU Health for COVID-19. Most of the patients had been tested in an outpatient setting; 7% had been tested in the emergency department; and 3% were tested in an inpatient setting.

On the basis of EHR review, which researchers referred to as the reference standard, 66% of patients had fever; cough was present in 88%; and dyspnea was present in 64%.

For fever, the sensitivity – or the ability to correctly identify those with a condition – of ICD-10 codes, when compared with the reference standard, was 0.26. For cough, it was 0.44, and for dyspnea, it was 0.24.

The ICD-10 codes fared better when it came to specificity, or the ability to correctly identify those without a condition. For fever, the specificity was 0.98. For cough, it was 0.88, and for dyspnea, it was 0.97.

ICD-10 code performance was better for inpatients than outpatients when it came to fever and dyspnea, but not for cough.

Negative predictive value was also poor for all symptoms.

“The proportion of patients with a false-negative ICD-10 code result ranged from 35.8% for fever among patients older than 64 years to 54.5% for fever among patients who tested positive for SARS-CoV-2 infection,” wrote the researchers.

“Common data models and other aggregation tools rely heavily on ICD-10 codes to capture clinical concepts; inaccuracy has implications for any downstream scientific discovery or surveillance,” they continued.

“For example, symptom surveillance could be important to detect subsequent waves of COVID-19, similar to the US Outpatient Influenza-Like Illness Surveillance Network. A substantial number of patients would be missed if ICD-10 codes were used for this task,” researchers added.

Regarding possible limitations, researchers pointed out that their study only included a single center and that it used data from March 10 to April 6 – early in the pandemic. They also noted that clinicians may not document all symptoms in every case.


Researchers noted that patient-reported outcomes, such as those self-reported through smartphone apps, could lead to more reliable symptom capture than billing codes or clinician documentation.

A wave of such apps – including informational, tracking, assessment and science research tools – rolled out in the spring.

“With New York City among the cities with the largest number of cases – a number that continues to grow – we see a critical and urgent need to understand more about the clinical course of the disease,” said Dr. Girish Nadkarni, clinical director of the Hasso Plattner Institute for Digital Health, regarding an app released by Mt. Sinai in April.

“This is a unique opportunity to collect data in a diverse population during an outbreak surge, which could provide powerful predictions of the clinical outcomes of our most vulnerable patients,” Nadkarni continued.

But privacy concerns have also dogged some apps’ release, with only 16 out of 50 apps in one study stating they would anonymize and encrypt users’ data.


“Critical data elements require careful validation to ensure that discoveries translate into effective interventions that reduce morbidity and mortality,” researchers wrote. “As with many aspects of this pandemic, we must pay careful attention to socioeconomically vulnerable populations, including racial minorities, rural patients, and low-income patients, for whom the gap between ICD-10 coding and clinical reality could be greater.”

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