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.
Platt Richard
Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.
Objective
This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.
To compare the ability to detect disease outbreaks of separate and combined data streams from ambulatory care and emergency department from Harvard Pilgrim Health Care.
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.
We describe the development and implementation of a protocol for identifying syndromic signals and for assessing their value to public health departments for routine (non-bioterrorism) purposes. The specific objectives of the evaluation are to determine the predictive value positive, sensitivity, and timeliness of the surveillance system, as well as its costs and benefits to public health.
Our goal was to assess the impact of sentinel sample size and criteria for a signal on performance of daily prospective space-time permutation detection by comparing results in varying size random samples from a large health plan to results found in the full membership.
We sought to compare ambulatory care (AC) and emergency department (ED) data for the detection of clusters of lower gastrointestinal illness, using AC and ED data and AC+ED data combined, from two geographically separate health plans participating in the National Bioterrorism Syndromic Surveillance Demonstration Program [1].
Despite decades of attempts to promote judicious AU, the US has high rates of per-person antimicrobial consumption, and extremely high rates of carbapenem use. Such profligate use is a key factor in the high rate of antimicrobial-resistant infections seen in US healthcare facilities. Antimicrobial stewardship (AS) programs have been identified as a critical component of intervention strategies. A core component of AS programs is tracking AU, which is needed to monitor trends in use, focus interventions on aberrant behaviors, promote judicious use, and evaluate the effectiveness of interventions. A system designed to extend two national data models would provide a scalable platform for rapid adoption of AU reporting.
Objective:
Plan, develop, and pilot an open source system that could be integrated into the PCORnet (PCORI) and Sentinel (FDA) national common data models (CDMs) to generate antimicrobial use (AU) reports submittable to CDCâs National Healthcare Safety Network (NHSN). The system included ancillary tables, and data quality and report generation queries. The DataMIME system will allow hospitals to generate comparable AU reports for hospital inpatients.