Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs.

Abstract

While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks. To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool. A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization's 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control. Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country. Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.