One limitation of syndromic surveillance systems based on emergency department (ED) data is the time and expense to investigate peak signals, especially when that involves phone calls or visits to the hospital. Many EDs use electronic medical records (EMRs) which are available remotely in real time. This may facilitate the investigation of peak signals.
Electronic Health Record (EHR) data offers the researcher a potentially rich source of data for tracking disease syndromes. Procedures performed on the patient, medications prescribed (not necessarily filled by the patient), and reason for visit are just some characteristics of the patient encounter that are available through an EHR that can be used to define surveillance syndromes. Since procedures have not been used frequently in defining syndromes, encounter level procedures data, extracted from the EHR of a large local primary care practice with about 200,000 patient encounters per year was used to identify procedures associated with an established respiratory syndrome.
Objective
To investigate the utility of different sources of patient encounter information, particularly in the primary care setting, that can be used to characterize surveillance syndromes, such as respiratory or flu.
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.
This paper describes three years of electronic health record (EHR) data from a network of urban ambulatory care clinics in New York City.
The pilot near real time surveillance system ASTER, which currently monitors the French Forces in Djibouti and French Guiana [1], has been especially designed for inter-allied interoperability. This paper briefly describes the rationale of this system's interoperability framework and components, and its results from a 4 years long experience.
Health care information is a fundamental source of data for biosurveillance, yet configuring EHRs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations. Despite a $48B investment in HIT, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. An alternative approach is to reimagine EHRs as iPhone-like platforms supporting substitutable apps-based functionality. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality.
Objective
To enable public health departments to develop “apps” to run on electronic health records (EHRs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. We describe a novel health information technology platform with substitutable apps constructed around core services enabling EHRs to function as iPhone-like platforms.
MUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1). To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.
Objective
To develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data
Hypertension (HTN) is a highly prevalent chronic condition and strongly associated with morbidity and mortality. HTN is amenable to prevention and control through public and population health programs and policies. Therefore, public and population health programs require accurate, stable estimates of disease prevalence, and estimating HTN prevalence at the community-level is acutely important for timely detection, intervention, and effective evaluation. Current surveillance methods for HTN rely upon community-based surveys, such as the BRFSS. While BRFSS is the standard at the state- and national-level, they are expensive to collect, released once per year, and their confidence intervals are too wide for precise estimates at the local level. More timely, frequently updated, and locally precise prevalence estimates could greatly improve the timeliness and precision of public health interventions. The current study evaluated EHR data from a large, mature HIE as an alternative to community-based surveys for timely, accurate, and precise HTN prevalence estimation.
Objective:
To assess the equivalence of hypertension prevalence estimates between longitudinal electronic health record (EHR) data from a community-based health information exchange (HIE) and the Behavioral Risk Factor Surveillance System (BRFSS).
Reports of infants born with congenital syphilis have increased in the United States every year since 2012. Prevention depends on high performing surveillance systems and compliance with the U.S. Centers for Disease Control and Prevention (CDC) recommendations to perform syphilis testing early in pregnancy, in the third trimester and at delivery if a woman is at high risk, and following a stillbirth delivery. These guidelines exist, because untreated syphilis is associated with adverse fetal outcomes including central nervous system infection and death. Surveillance of congenital syphilis and stillbirth is challenging because available data sources are limited. Assessment of compliance with testing guidelines is particularly challenging, since public health agencies often lack access to comprehensive cohorts of tested individuals as most public health laws only require reporting of positive disease case information.
Objective:
To measure stillbirth delivery rates and syphilis screening rates among women with a stillbirth delivery using electronic health record data available in a health information exchange.
Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing eICR of sexually transmitted infections (STI) with a clinical partner. Existing standard vocabulary codes were identified to represent previously-identified information gaps, or the need for new codes or concepts was identified.
Objective:
Previous research identified data gaps between traditional paper-based STI notifiable condition reporting and pilot electronic initial case reporting (eICR) relying on Continuity of Care Documents (CCDs) exported from our clinical partnerâs electronic health record (EHR) software. Structured data capture is needed for automatic processing of eICR data imported into public health repositories and surveillance systems, similar to electronic laboratory reporting (ELR). Coding data gaps (between paper and electronic case reports) using standardized vocabularies will allow integration of additional questions into EHR or other data collection systems and may allow creation of standard Clinical Data Architecture (CDA) templates, Logical Observation Identifiers Names and Codes (LOINC) panels, or Fast Healthcare Interoperability Resources (FHIR) resources. Furthermore, identifying data gaps can inform improvements to other standards including nationwide standardization efforts for notifiable conditions.