Streichert Laura

Discusses the current state of syndromic surveillance using inpatient and ambulatory clinical data in the United States and the potential utility of the data. The Meaningful Use Stages 2 and 3 regulations incentivize the use of these data sources. Existing systems effectively perform a range of activities from influenza-like illness surveillance to heart disease risk factor surveillance. With further development, ambulatory and inpatient data could become an integral part of syndromic surveillance practice.

Objective

To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emer- gency department, urgent care clinic, hospital inpatient, and ambula- tory clinical care data.

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

National efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. Measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. They also reveal opportunities for innovations and improvements. Similar quality improvement efforts in the health care system are beginning to bear fruit. There has been a need, however, for a framework for assessing public health quality that provides a standard, yet is flexible and relevant to agencies at all levels.

The U.S. Health and Human Services (HHS) Office of the Assistant Secretary for Health, working with stakeholders, recently developed and released a Consensus Statement on Quality in the Public Health System that introduces a novel evaluation framework. They identified nine aims that are fundamental to public health quality improvement efforts and six cross-cutting priority areas for improvement, including population health metrics and information technology; workforce development; and evidence-based practices.

Applying the HHS framework to surveillance expands measures for surveillance quality beyond typical variables (e.g., data quality and analytic capabilities) to desired characteristics of a quality public health system. The question becomes: How can disease surveillance help public health services to be more population centered, equitable, proactive, health-promoting, risk-reducing, vigilant, transparent, effective, and efficient—the desired features of a quality public health system? Any agency with a public health mission, or even a partial public health mission (e.g., tax-exempt hospitals), can use these measures to develop strategies that improve both the quality of the surveillance enterprise and public health systems, overall. At this time, input from stakeholders is needed to identify valid and feasible ways to measure how surveillance systems and practices advance public health quality. What exists now and where are the gaps?

Objective

To examine disease surveillance in the context of a new national framework for public health quality and to solicit input from practitioners, researchers, and other stakeholders to identify potential metrics, pivotal research questions, and actions for achieving synergy between surveillance practice and public health quality.

Data quality monitoring is necessary for accurate disease surveillance. However it can be challenging, especially when “real-time” data are required. Data quality has been broadly defined as the degree to which data are suitable for use by data consumers. When compromised at any point in a health information system, data of low quality can impair the detection of data anomalies, delay the response to emerging health threats, and result in inefficient use of staff and financial resources. While the impacts of poor data quality on biosurveillance are largely unknown, and vary depending on field and business processes, the information management literature includes estimates for increased costs amounting to 8-12% of organizational revenue and, in general, poorer decisions that take longer to make.

Objective

To highlight how data quality has been discussed in the biosur- veillance literature in order to identify current gaps in knowledge and areas for future research. 

Spurred by recent advances in PH informatics, the implementation of the Medicare and Medicaid Electronic Health Records Incentive Programs (Meaningful Use), and the opportunities provided by the availability of the redesigned BioSense program, SyS has become an increasingly important component of the biosurveillance enterprise. Knowing how and when jurisdictions use SyS, as well as challenges faced, allows ISDS, ASTHO, CDC, and other partners to provide relevant CBA – information transfer, training, and technical assistance – to further biosurveillance practice.

Objective

To present the results of a nationwide survey designed to assess the syndromic surveillance (SyS) practices and capacity-building assistance (CBA) needs of U.S. state public health authorities (PHAs).

The US Department of Health and Human Services has mandated that after October 1, 2015, all HIPAA covered entities must transition from using International Classification of Diseases version 9 (ICD- 9) codes to using version 10 (ICD-10) codes (www.cms.gov). This will impact public health surveillance entities that receive, analyze, and report ICD-9 encoded data. Public health agencies will need to modify existing database structures, extraction rules, and messaging guides, as well as syndrome definitions and underlying analytics, statistical methodologies, and business rules. Implementation challenges include resources, funding, workforce capabilities, and time constraints for code translation and syndrome reclassification.

Objective

To describe the process undertaken to translate syndromic surveillance syndromes and sub-syndromes from ICD-9 diagnostic codes to ICD-10 codes and how these translations can be used to improve syndromic surveillance practice.

Knowledge Management is defined as “the process of capturing, distributing, and effectively using knowledge.” ISDS members have varying degrees of experience with public health surveillance and syndromic surveillance specifically, and will all benefit from more structured access to documentation on components related to syndromic surveillance, including but not limited to, the onboarding of facilities, data quality monitoring tools, case definitions, and data processing tools. To build a knowledge management capability, the first step is to gather initial requirements and priorities from the CoP.

Objective

The purpose of the roundtable is to seek feedback from attendees on the components needed to improve syndromic surveillance practice through access to the shared knowledge, practices, and tools of the ISDS Community of Practice (CoP).

BioSense 2.0 has become a platform for technical receipt and analysis of syndromic surveillance data for many jurisdictions nationwide, as well as a collaborative effort that has engaged a larger community of syndromic surveillance practitioners, Governance Group, and federal agencies and organizations. The potential longterm benefits of BioSense 2.0 for resource and data sharing have at times been overshadowed by the short-term limitations of the system and disconnected efforts among the CoP. In May 2014, representatives from 41 jurisdictions attended a 2-day, in-person meeting where four workgroups were formed to address on-boarding, data quality, data sharing and syndrome definition in an effort to advance changes that resonate with actual surveillance practice.

Objective

This roundtable will provide a forum for the syndromic surveillance Community of Practice (CoP) to learn about activities of the BioSense 2.0 User Group (BUG) workgroups that address priority issues in syndromic surveillance. It will be an opportunity to discuss key challenges faced by public health jurisdictions in the era of Meaningful Use and identify further needs and best practices in the areas of data quality, data sharing, onboarding, and developing syndrome definitions.