Haney Gillian

In Massachusetts, syndromic surveillance (SyS) data have been used to monitor injection drug use and acute opioid overdoses within EDs. Currently, Massachusetts Department of Public Health (MDPH) SyS captures over 90% of ED visits statewide. These real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance. Other surveillance data have shown elevated rates of opioid overdose related ED visits, Emergency Medical Service incidents, and fatalities in Massachusetts from 2014-20171,2,3. Injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including HIV infection. An investigation of an HIV outbreak among persons reporting IDU identified homelessness as a social determinant for increased risk for HIV infection.

Objective: We sought to measure the burden of emergency department (ED) visits associated with injection drug use (IDU), HIV infection, and homelessness; and the intersection of homelessness with IDU and HIV infection in Massachusetts via syndromic surveillance data.

Clinician reporting of notifiable diseases has historically been slow, labor intensive, and incomplete. Manual and electronic laboratory reporting (ELR) systems have increased the timeliness, efficiency, and completeness of notifiable disease reporting but cannot provide full demographic information about patients, integrate an array of pertinent lab tests to yield a diagnosis, describe patient signs and symptoms, pregnancy status, treatment rendered, or differentiate a new diagnosis or from follow-up of a known old diagnosis. Electronic medical record (EMR) systems are a promising resource to combine the timeliness and completeness of ELR systems with the clinical perspective of clinician initiated reporting. We describe an operational system that detects and reports patients with notifiable diseases to the state health department using EMR data.

Objective

To leverage EMR systems to improve the timeliness, completeness, and clinical detail of notifiable disease reporting.

Professor Hripcsak rightly points out some of the challenges inherent in disseminating and sustaining robust information systems to automate the detection and reporting of notifiable diseases using data from electronic medical records (EMR). New York City'™s experience with automated tuberculosis identification and notification is a salient reminder that sophisticated technology alone is not enough to ensure broad adoption of automated electronic reporting systems. Substantial resources and ongoing active support by a wide range of public health stakeholders are also essential ingredients. We have attempted to engineer the Electronic medical record Support for Public health (ESP) system to make it suitable for widespread adoption but the ultimate success of this endeavour will depend upon sustained collaboration between many parties including commercial EMR vendors, clinical administrators, state health departments, the Centers for Disease Control and Prevention (CDC), the Council of State and Territorial Epidemiologists (CSTE), and others.

Clinician initiated reporting of notifiable conditions is often delayed, incomplete, and lacking in detail. We report on the deployment of Electronic medical record Support for Public health (ESP), a system we have created to automatically screen electronic medical record (EMR) systems for evidence of reportable diseases, to securely transmit disease reports to health authorities, and to respond to queries from health departments for clinical details about laboratory detected cases. ESP consists of software that constructs and analyzes a temporary database that is regularly populated with comprehensive codified encounter data from a medical practice's EMR system. The ESP database resides within the host medical practice's firewall, configured on either a central workstation to service large multi-site, multi-physician practices or as a software module running alongside a small practice's EMR system on a personal computer. The encounter data sent to ESP includes patient demographics, diagnostic codes, laboratory test results, vital signs, and medication prescriptions. ESP regularly analyzes its database for evidence of notifiable diseases. When a case is found, the server initiates a secure Health Level 7 message to the health department. The server is also able to respond to queries from the health department for demographic data, treatment information, and pregnancy status on cases independently reported by electronic laboratory systems. ESP is designed to be compatible with any EMR system with export capability: it facilitates translation of proprietary local codes into standardized nomenclatures, shifts the analytical burden of disease identification from the host electronic medical record system to the ESP database, and is built from open source software. The system is currently being piloted in Harvard Vanguard Medical Associates, a multi-physician practice serving 350,000 patients in eastern Massachusetts. Disease detection algorithms are proving to be robust and accurate when tested on historical data. In summary, ESP is a secure, unobtrusive, flexible, and portable method for bidirectional communication between EMR systems and health departments. It is currently being used to automate the reporting of notifiable conditions but has promise to support additional public health objectives in the future.

Approximately one quarter of people treated for tuberculosis (TB) have no supporting microbiology, and thus are not detectable through laboratory reporting systems. Health departments depend upon clinicians to report these cases, but there is important underreporting. We previously described the performance of several algorithms for TB detection using electronic medical record (EMR) and claims data, and noted good sensitivity when screening for >2 anti-TB drugs; however, the positive predictive value was only 30%. We re-evaluated this and other algorithms in light of evolving TB treatment practices and enhanced ability to apply complex decision rules to EMR data in real time.

Objective

To develop algorithms for case detection of TB using EMR data to improve notifiable disease reporting.