Abstract
Background: Legislation and good citizenship compel practitioners to report key diagnoses that affect the public good. Public health reporting, however, is frequently paper-based, incomplete, lacking key details, and delayed. We sought to leverage electronic medical record (EMR) systems to automate the detection and reporting of notifiable diseases to a health department.
Methods: The Electronic Support for Public Health (ESP) system consists of an independent server within the central data processing center of Atrius Health, a large multisite medical practice with over 600,000 patients. The server is populated by nightly extracts generated by Atrius Health’s Epic Care EMR. The extracts contain codified data on every patient encounter from the preceding 24 hours including demographics, diagnoses, lab orders, lab results, vital signs, vaccinations, and prescriptions. Relevant codes are mapped to standard nomenclatures. EMR data on the server is analyzed using novel algorithms to detect patients with notifiable conditions. When a patient is identified, an HL7 electronic message is securely transmitted to the state health department. The ESP server is deliberately decoupled from the source EMR in order to minimize interference with clinical computing, ease modification of case-detection algorithms, and permit compatibility with different proprietary EMR systems. The number and completeness of electronic case reports are compared to concurrent, conventional, paper-based reports.
Results: ESP has been operational since January 2007 and has reported 1121 cases of chlamydia, 146 cases of gonorrhea, 25 cases of pelvic inflammatory diseases, and five cases of acute hepatitis A. Comparison with ongoing traditional reporting shows a 44% increase in the number of chlamydia and gonorrhea cases reported. ESP includes treatment details on 100% of reports versus 88% of manual reports and has noted 86 cases of concurrent pregnancy versus five in paper reports. Algorithms to detect additional disease are being validated.
Conclusions: Automated analysis of electronic medical record data can increase the number, clinical detail, and accuracy of notifiable disease reporting. The architecture pioneered for this system can potentially support additional public health objectives such as novel vaccine or medication adverse event detection and reporting. Collaborators are invited to help refine the system and test implementation in a new site.
- Received September 11, 2008.
