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Electronic Health Record (EHR)

Over 300 independent practices transmit monthly quality reports to a data warehouse using an automated process to summarize patient information into quality measures. All practices have implemented an EHR that captures clinical information to be aggregated for population reporting, and is designed to assist providers by generating point-of-care reminders and simplify ordering and documentation.

Objective

Comparison of automated EHR-derived data with manually abstracted patient information on smoking status and cessation intervention.

Referenced File
Submitted by uysz on

As the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (CDS). CDS for case reporting confronts two challenges: a) While EHRs are moving toward local CDS capabilities, it will take several years for EHR systems to consistently support this capability; and b) public health-related CDS knowledge, such as Zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. Therefore, there is an ongoing need to manage reporting rules in a distributed manner. Similarly, there is a need for more decentralized models of CDS execution to overcome some of the disadvantages of centralized deployment and to leverage local CDS capabilities as they become available over the next several years.

Objective: To discuss how clinical decision support (CDS) for electronic case reporting (eCR) will evolve over time to provide multiple deployment models

Submitted by elamb on

The National Institute for Drug Abuse Report, Common Comorbidities with Substance Use Disorders, states there are many individuals who develop substance use disorders (SUD) are also diagnosed with mental disorders, and vice versa.(1) Prescription opioids are amongst the most commonly used drugs that lead to illicit drug use.(2)Much of the data about the starting point of the prescription opioid addiction is in the patient health history and is recorded within the provider electronic health record and administrative systems.Description: There are a variety of addiction and misuse risk screening tools available and selecting appropriate tools screening can be confusing for providers. Examples of common screening tools: Opioid Abuse Risk Screener (OARS), Opioid Risk Tool (ORT), Screener and Opioid Assessment for Patients with Pain (SOAPP), Current Opioid Misuse Measure (COMM), Diagnosis, Intractability, Risk, and Efficacy (DIRE). These opioid risk screening tools are interview based and vary in how they survey for psychosocial factors. The screening tools are useful, but are meant only to alert the provider to conduct further investigation.(3) Understanding how the comorbidities recorded in the patient's clinical interactions may help improve risk assessment investigations and ongoing monitoring programs. Studying the chronic pain patients' longitudinal clinical, operational, and laboratory records provides the basis for better study controls than those using population based on emergency department admission and mortality events.

Objective: Assessing mental health and opioid addiction comorbidities among chronic pain patients using a large longitudinal clinical, operational, and laboratory data set.

Submitted by elamb on

Chronic diseases, including hypertension, type 2 diabetes mellitus (diabetes), obesity, and hyperlipidemia, are some of the leading causes of morbidity and mortality in the United States. Monitoring disease prevalence guides public health programs and policies that help prevent this burden. EHRs can supplement traditional sources of chronic disease surveillance, such as health surveys and administrative claims datasets, by offering near real-time data, large sample sizes, and a rich source of clinical data. However, few studies have provided clear, consistent EHR phenotypes that were developed to inform population health surveillance.

Objective: To utilize clinical data in Electronic Health Records (EHRs) to develop chronic disease phenotypes appropriate for conducting population health surveillance.

Submitted by elamb on

Public health departments have a strong interest in monitoring the incidence, care, and complications of gestational diabetes, as it is associated with poor outcomes for infants and increased risk of diabetes type II for mothers. Gestational diabetes rates are also a possible early marker for changes in the incidence of diabetes type II in the general population. However, diabetes is not generally a reportable condition and therefore, public health surveillance is limited to periodic telephone surveys (subject to self-report inaccuracies), sponsored clinical examinations (expensive, small sample size, no information about processes of care), and occasional research studies. Automated analysis of electronic health record data is a promising method to complement existing surveillance tools with longitudinal, continually updated, clinically rich data derived from large populations. We describe a pilot project to automatically survey electronic health record data in order to identify cases of gestational diabetes, describe their patterns of care and complications, and report summary data to the state health department.

 

Objective

To develop an electronic, prospective surveillance system to describe the incidence, care, and complications of gestational diabetes using live electronic health record data from a large defined population.

Referenced File
Submitted by hparton on

The American Recovery and Reinvestment Act (ARRA) brought significant incentives to providers for implementing certified EHR technologies. It specifically requires utilization of certified electronic health records (EHRs) for electronic exchange of health information and for submission of clinical quality and other measures to the federal agencies. The most important barriers in the ELR implementation are a lack of funding at health departments, shortage of staff at health departments, and the variable content and format of ELR messages. The MU is a new factor that may foster ELR technologies through implementation incentives and through standardization of EHRs.

Objective

The objective of this presentation is to evaluate the potential impact of Stage 1 meaningful use (MU) health IT certification (MUC), on development of national electronic laboratory reporting (ELR) capacities.

Referenced File
Submitted by teresa.hamby@d… on

The global H1N1 influenza A pandemic in 2009 heightened the need for automated disease surveillance capabilities. After an initial surge in confirmatory testing, clinicians

moved to diagnosis based on patient assessment for fever combined with cough or sore throat, the influenza-like indicators (ILI). Although some organizations used automated data capture or national systems with manual data entry (www.cdc.gov/flu/weekly/fluactivity.htm), there was not a turnkey national automated system in place to support syndromic surveillance for ILI among non-affiliated organizations. Semantic interoperability through standards utilization is widely expected to simplify large-scale data initiatives but is challenging with widely disparate uses of terminology.

 

Objective

This paper describes a national initiative connecting 850 non-affiliated healthcare provider organizations throughout the United States in order to provide situational awareness during the 2009–2010 H1N1 influenza A pandemic. We addressed the challenge of semantic variability between organizations through a centralized data-mapping approach.

Referenced File
Submitted by hparton on

Effective and valid surveillance of syndromes can be extremely useful in the early detection of outbreaks and disease trends. However, medical chart checks without patient identifiers and lack of diagnoses in A08 data has made validation difficult. With the rising availability of electronic health records (EHRs) to local health departments, the ability to evaluate syndromic surveillance systems (SSS) has improved. In LAC, ED data are collected from hospitals and classified into categories based on chief complaints. The most reported syndrome in LAC is the respiratory classification, which is intended to broadly capture respiratory pathogen activity trends. To test the validity of the LAC Department of Public Health (DPH) respiratory syndrome classification, ED syndromic surveillance data were analyzed using corresponding EHRs from one hospital in LAC.

Objective

To compare and validate syndromic surveillance categorization against electronic health records at one hospital emergency department (ED) in Los Angeles County (LAC).

Submitted by elamb on

In the U.S., federal programs are accelerating the meaningful use of electronic health record (EHR) technology and encouraging greater standardization in how governmental public health agencies (PHAs) establish surveillance data partnerships with healthcare providers. To qualify for the benefits of these federal programs (a.k.a., Meaningful Use), healthcare professionals and hospitals must determine: 1) Whether their jurisdictional PHA collects health data for immunization or cancer registries, reportable diseases, and/or syndromic surveillance; and 2) If the PHA does collect this data, then they must register for data on-boarding with the PHA and actively work with them to establish on-going data exchange. These requirements are predicated on participating state and local PHAs either establishing new or expanding the capacity of their existing public health data reporting services. To assist state and local PHAs in this effort, the U.S. Centers for Disease Control and Prevention (CDC) facilitates a national task force, known as the Stage 2 MU Public Health Reporting Requirements Task Force, which has recommended guidelines and clarified requirements for these new processes.

Objective

To exchange lessons learned and refine national guidelines for public health agencies to declare Meaningful Use readiness, register eligible professionals and hospitals for the public health meaningful use objectives, on-board data providers, and perform "on-going" data submission.

Submitted by elamb on

In 2010, as rules for the Centers for Medicaid and Medicare Electronic Heatlh Record (EHR) Incentive Programs (Meaningful Use)(1), were finalized, ISDS became aware of a trend towards new EHR systems capturing or sending emergency department (ED) chief complaint (CC) data as structured variables without including the free-text. This perceived shift in technology was occurring in the absence of consensus-based technical requirements for syndromic surveillance and survey data on the value of free-text CC to public health practice. On 1/31/11, ISDS, in collaboration with CDC BioSense, recommended a core set of data for public health syndromic surveillance (PHSS) to support public health's participation in Meaningful Use.

Objective

This study was conducted to better support a requirement for ED CC as free-text, by investigating the relationship between the unstructured, free-text form of CC data and its usefulness in public health practice. To better inform health IT standardization practices, specifically related to Meaningful Use, by describing how US public health agencies use unstructured, free-text EHR data to monitor, assess, investigate and manage issues of public health interest.

Referenced File
Submitted by elamb on
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