Electronic health records (EHRs) contain rich temporal data about infectious diseases, but an optimal approach to identify infections remains undefined. Using the All of Us Research Program, we developed computable phenotypes for respiratory viruses by integrating billing codes, prescriptions, and laboratory results within 90-day episodes. Phenotypes computed from 265,222 participants yielded cohorts ranging from 238 (adenovirus) to 28,729 (SARS-CoV-2) cases. Virus-specific billing codes showed varied sensitivity (8-67%) and high positive predictive value (90-97%), except for influenza virus and SARS-CoV-2 where lower PPV (69-70%) improved with increasing billing codes. Identified infections exhibited expected seasonal patterns and virus proportions when compared with CDC data. This integrated approach identified episodic disease more effectively than individual components alone and demonstrated utility in identifying severe infections. The method enables large-scale studies of host genetics, health disparities, and clinical outcomes across episodic diseases. 

The authors have declared no competing interest.

The contents of this publication are the sole responsibility of the authors. The content of this publication does not necessarily reflect the views, opinions, or policies of the NIH, the Uniformed Services University of the Health Sciences, the US Department of Health and Human Services, the US Department of Defense, or the US Government, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. This work was prepared by a military or civilian employee of the US Government as part of the individual′s official duties. Therefore, it is in the public domain and does not possess copyright protection. Public domain information may be freely distributed and copied; however, as a courtesy, it is requested that the authors be given an appropriate acknowledgement. This work was supported by the National Human Genome Research Program Intramural Research Program, grant numbers: ZIA HG200417, ZIC HG200420; and the Division of Intramural Research of the National Institute of Allergy and Infectious Diseases. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. Funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

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