Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness

Abstract

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Keywords: Massachusetts; RAVIOLI; United States; algorithms; disease surveillance; electronic health records; influenza-like illness; respiratory infections; respiratory virus–like illness; viruses.

Figure 1

Numbers of patients with a clinical encounter for respiratory virus–like illness and the percentages that met the requirements for influenza-like illness versus those of the RAVIOLI algorithm for monitoring respiratory virus–like illness, by week, Massachusetts, USA, October 2015–January 2024. Patients receiving a diagnosis code, immunization, vital sign measure, laboratory test, or prescription were considered to have a clinical encounter.

Figure 2

Numbers of patients that met the requirements for the RAVIOLI algorithm for monitoring respiratory virus–like illness, by pathogen category and week, Massachusetts, USA, October 2015–January 2024. A) October 2015–January 2024; B) January 2020–January 2024. Within each virus-specific category are counts of positive test results and diagnosis codes with a positive predictive value (PPV) ≥10% for that specific pathogen. The nonspecific category includes diagnosis codes with a PPV of ≥10% for any positive respiratory viral assay but PPV of <10% for any specific respiratory virus and includes measured fever >100°F.

Figure 3

Percentage of patients meeting the RAVIOLI algorithm for monitoring respiratory virus–like illness, by age group, Massachusetts, USA, October 2015–January 2024.