Mandatory reporting of infectious diseases forms a cornerstone of cost-efficient, preventive public health programs. Timely, complete and accurate case reports facilitate contact tracing, appropriate treatment, and follow-up, decreasing the risk of an infection being spread. 1,2
In the United States, voluntary systematic disease reporting by physicians dates from 1874. 3
Reporting of patients found to have specific diseases such as common sexually transmitted infections, as well as less common infections like tuberculosis, is now a legal requirement for health care practitioners in all U.S. states. Unfortunately, despite substantial progress, including the introduction of electronic reporting forms, 4,5
many of these important surveillance mechanisms still depend on practitioner initiated, manual data entry and submission.
Busy clinicians find it frustrating and burdensome to manually transcribe clinical and demographic patient details between independent data systems. Not surprisingly, most evidence suggests that practitioner initiated, manual reporting systems provide delayed 6–8
and inaccurate data, with many omissions and errors. 9
Reporting systems based on laboratory test results are increasingly able to supplement practitioner initiated reporting, potentially detecting many more cases and reporting with less delay, 10
but are not always able to provide complete reporting 7,11
and cannot identify conditions defined by clinical criteria, such as acute pelvic inflammatory disease (PID). More importantly, laboratory reporting systems lack crucial clinical details 6,11,12
for public health practitioners involved in managing reported cases, such as vital signs, drug, dose, and route of antibiotic or other treatment, and in the case of a female patient, pregnancy status.
We based the work described here on the premise that if suitable data are available in electronic form, an appropriate software application could support automated detection of notifiable diseases, facilitating the timely reporting of cases, including all relevant clinical information, without requiring practitioner initiation or error-prone manual data transcription. Such a system could be adapted to work with data integrated from diverse sources of electronic health data, such as regional health information exchanges, the electronic medical records of large medical practices, and, for more limited purposes, laboratory reporting systems, or pharmacy benefits managers.
The Electronic Support for Public Health (ESP) application 13,14
is an automated, platform independent disease detection and reporting system, developed in an ongoing collaboration between the Centers for Disease Control and Prevention (CDC), the CDC funded Center of Excellence in Public Health Informatics based at Harvard, Harvard Vanguard Medical Associates (HVMA) and Atrius, and the Massachusetts Department of Public Health (MDPH). The initial installation of ESP uses a fully automated data flow from an integrated commercial EMR system used by more than 700 physicians spread over 30 practice sites, serving more than 600,000 patients, providing near real-time notifiable disease case detection, and secure, standards-based, automated, electronic communication to the relevant public health authority. We have previously outlined some aspects of the planning and implementation of the ESP project. 14 .
In this report, we describe key informatics issues and features, and summarize nearly two years of continuous, live ESP operation, including potentially useful lessons learned from this effective, interoperable, and extensible public health informatics application.