Electronic Health Record–Based Algorithm for Monitoring Respiratory Virus–Like Illness
Noelle M. Cocoros
, Karen Eberhardt, Vu-Thuy Nguyen, Catherine M. Brown, Alfred DeMaria, Lawrence C. Madoff, Liisa M. Randall, and Michael Klompas
Author affiliations: Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA (N.M. Cocoros, V.-T. Nguyen, M. Klompas); Harvard Medical School, Boston (N.M. Cocoros, M. Klompas); Commonwealth Informatics, Waltham, Massachusetts, USA (K. Eberhardt); Massachusetts Department of Public Health, Boston (C.M. Brown, A. DeMaria, L.C. Madoff, L.M. Randall); Brigham and Women’s Hospital, Boston (M. Klompas)
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Figure 2
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.
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