Electronic Health Records

EHRs are increasingly being adopted to improve quality of care in health care systems, but they also have potential to monitor health at the population level. There has been relatively little focus on using EHRs for population health surveillance beyond infectious diseases. Current tools to monitor population health (vital statistics, hospital discharge data, population health surveys) are useful but can be expensive, and may be slow to conduct or produce findings. Aggregated EHR-derived data have the potential to deliver cheaper and faster data, and have the capacity to provide information on earlier disease stages, thus increasing the likelihood of timely use. If EHR data can be validated, they can be used to augment existing surveillance methods, resulting in more strategic targeting of health resources and better data to guide and evaluate public health initiatives and policies. New York City (NYC) is currently developing a pilot public health surveillance program known as NYC Macroscope, the first domestic effort to aggregate EHR data from independent primary care practices into a surveillance tool. This EHR-based population health surveillance system will compile summarized data from ambulatory electronic health records to help city health officials monitor and respond to real-time data on conditions of public health importance.

Objective

To describe the potential benefits and challenges of using electronic health record data for population health surveillance, and what to consider when establishing an electronic health record (EHR) surveillance system (EHRSS).

Disease surveillance is a core public health (PH) function. To manage and adjudicate cases of suspected notifiable disease, PH workers gather data elements about persons, clinical care, and providers from various clinical sources, including providers, laboratories, among others. Current processes often yield incomplete and untimely reporting across different diseases requiring time-consuming follow-up by PH to get needed information [1,2]. To improve the completeness and timeliness of case reporting, health departments have explored accessing EHR systems, which are increasingly available. We examine whether providing PH with EHR access to gather notifiable disease case information affects data completeness.

Objective

To assess the effect of electronic health record (EHR) system access on notifiable disease case data completeness.

Currently over 18 million students are enrolled in USA institutions of higher education (IHEs), representing more than one-third of the young adult population. In a national survey, about 16% of students reported living at home. SHCs are therefore an important resource for the majority of college students. College communities are unique settings that are geographically diverse, highly mobile, and densely populated with congregate living and learning conditions. IHEs, therefore, are highly vulnerable to the introduction of contagious diseases with subsequent transmission to surrounding communities. Thousands of counseling and student health centers exist, funded by billions of dollars. Despite these facts, there was no national database on the health care utilization of this population. In an era in which health policies and plans are typically guided by data, we were relatively blind to information about the diagnoses, epidemiologic trends and health care needs of young adults attending colleges and universities.

Objective

We received CDC funding to create and maintain a multi-institutional de-identified medical records database from student health centers (SHCs) for a nationally representative sample of colleges and universities.

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.

Objective

There were two objectives of this analysis. First, apply text-processing methods to free-text clinician notes extracted from the VA electronic medical record for automated detection of Influenza-Like-Illness. Secondly, determine if use of data from free-text clinical documents can be used to enhance the predictive ability of case detection models based on coded data.

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.

The Electronic medical record Support for Public health (ESP) project by Klompas et al. (1) promises improved public health reporting by exploiting information captured in electronic health records. This project pulls together a number of technologies (health records, terminology maintenance, inference rules, data and transmission standards, security, text processing, and user interfaces) to create a comprehensive reporting system with a public health query feature. The initial deployment at Harvard Vanguard Medical Associates is promising.

I Medical services for outpatients are well developed due to universal public health insurance. Even patients who have mild symptoms can visit a clinic freely in Japan. Thus the monitoring of outpatients provides very timely information to detect unusual events. On the other hand, EMRs haven't had much penetration, less than 10% at clinics and 20% at hospitals. Moreover, almost nobody uses HL7 or other standards for EMRs. Therefore, it is very difficult to develop a syndromic surveillance system using EMRs like the U.S. We have to develop a system for each EMR and it has a heavy cost. In Japan, there are about 40 thousand pharmaciesand almost half of drugs prescribed are delivered through pharmacies. Almost all pharmacies record prescriptions electronically. Objective: So that full automatic syndromic surveillance cover the whole of nation, we construct the system using the information of prescription.

Objective

We asked to what extent computerized processing of the full free-text clinical documentation could enhance syndrome detection compared to the sole use of structured data elements from a comprehensive electronic medical record.

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.