Consensus-based technical guidance for electronic case reporting (eCR) of sexually transmitted infections was implemented within existing health information technologies to automatically detect chlamydia and gonorrhea cases based on diagnosis and laboratory observation codes and build a case report using industry standards. The process was evaluated using 12 420 ambulatory encounters among adolescents and adults 15 years and older seen at 8 Chicago-area community health centers between May 1 and June 30, 2017. We tabulated the frequency of matches between the case detection logic and patient data and compared the eCR identified cases with paper case reports. This study found that eCR increased provider reporting when compared with paper reporting alone. While additional work across stakeholder groups is needed, these early findings suggest that broadly adopted eCR will decrease both provider and public health burden while improving reporting timeliness and data completion to support case investigation.

Background: Reactive syphilis serologies are investigated by health departments to determine if they represent new infection, reinfection, or treatment failure. Serologies prioritized for investigation based on nontreponemal test titer and age (using a "reactor grid") undergo manual record search and review. We developed a computerized algorithm that automates the record search and review. Methods:We developed and tested the algorithm using a Florida Department of Health data set containing serologies reported January 2016 to December 2018 and previous records linked to each individual. The algorithm was based on the syphilis case definition, which requires (except primary cases with signs and symptoms) (1) a positive treponemal test result and a newly positive nontreponemal test result or (2) a 4-fold increase in nontreponemal test titer. Two additional steps were added to avoid missing cases. New York City Department of Health and Mental Hygiene validated this algorithm. Results:The algorithm closed more investigations (49.9%) than the reactor grid (27.0%). The algorithm opened 99.4% of the individuals investigated and labeled as cases by the health department; it missed 75 cases. Many investigations opened by the algorithm were closed by the reactor grid; we could not assess how many would have been cases. In New York City, the algorithm closed 70.9% of investigations, likely because more individuals had previous test in the database (88.2%) compared with Florida (56.5%). Conclusions:The automated algorithm successfully searched and reviewed records to help identify cases of syphilis. We estimate the algorithm would have saved Florida 590 workdays for 3 years.

Public health agencies including federal, state, and local governments routinely send out public health advisories and alerts via e-mail and text messages to health care providers to increase awareness of public health events and situations. Agencies must ensure that practitioners have timely and accessible information at the critical point-of-care. Electronic health record (EHR) systems have the potential to alert physicians of emerging health conditions deemed important for public health at the most critical time of need. To understand how public health agencies can leverage existing alerting mechanisms in EHR systems, it is important to understand characteristics of public health alerts to determine their suitability for alerting in EHR systems. Authors conducted a review and analysis of public health alerts for a 3-year period to identify critical data attributes necessary to support public health alerting in EHR systems. The alerts were restricted to those most relevant for clinical care. The results showed that there is an opportunity for disseminating actionable information to clinical practitioners at the point of care to guide care and reporting. Public health alerts in EHR systems can be useful in reporting, recommending specific tests, as well as suggesting secondary prevention.