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Objective To extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems. Introduction Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. However, the quality of data in these systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers 1 . Current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. Better, automated tools for examining data quality are desired by the surveillance community. Methods Using the existing, open-source platform Atlas developed by the Observational Health Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org), we added new functionality to measure and visualize the quality of data electronically reported from disparate information systems. Our extensions focused on analysis of data reported electronically to public health agencies for disease surveillance. Specifically, we created methods for examining the completeness and timeliness of data reported as well as the information entropy of the data within syndromic surveillance messages sent from emergency department information systems. Results To date we transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients into the OHDSI common data model. We further measured completeness, timeliness and entropy of the syndromic surveillance data. In Figure-1, the OHDSI tool Atlas summarizes the analysis of data completeness for key fields in over one million syndromic surveillance messages sent to Indiana’s health department in 2014. Completeness is reported by age category (e.g., 0-10, 20-30, 60+). Gender is generally complete, but both race and ethnicity fields are often complete for less than half of the patients in the cohort. These results suggest areas for improvement with respect to data quality that could be actionable by the syndromic surveillance coordinator at the state health department. Conclusions Our project remains a work-in-progress. While functions that assess completeness, timeliness and entropy are complete, there may be other functions important to public health that need to be developed. We are currently soliciting feedback from syndromic surveillance stakeholders to gather ideas for what other functions would be useful to epidemiologists. Suggestions could be developed into functions over the next year. We are further working with the OHDSI collaborative to distribute the Atlas enhancements to other platforms, including the National Syndromic Surveillance Platform (NSSP). Our goal is to enable epidemiologists to quickly analyze data quality at scale. References 1. Dixon BE, Rosenman M, Xia Y, Grannis SJ. A vision for the systematic monitoring and improvement of the quality of electronic health data. Studies in health technology and informatics. 2013;192:884-8.

Reaching contagious people in a timely manner is essential to limit disease spread. We examined communicable disease cases with undelivered mail to ascertain failure causes and rates. We identified the source of address, reason for return and patient demographics from the case files. This yielded a framework of address information flow. This framework allows us to establish a baseline for future research including cost-effectiveness analyses for streaming lining the process for improvements.

Provider reporting of notifiable disease is a critical component of public health assessment and surveillance. To efficiently and effectively assess and respond to disease, health departments require complete data from providers. To better understand the level of completeness in provider reporting, we examined key data elements across 7 notifiable diseases reported to a local health department. We found that some fields are incomplete more than half the time and provider demographics are frequently missing. We discuss the implications on public health workflow and opportunities for improvement.

Public health surveillance depends, in part, on provider-submitted reports of communicable diseases. The 'Improving Population Health through Enhanced Targeted Regional Decision Support' research project is implementing a novel technology to pre-populate report forms in order to streamline reporting workflows, lower reporting barriers, increase data completeness, improve timeliness and, ultimately, provide access to higher quality surveillance data for public health organizations. We will share preliminary evaluation results regarding barriers and facilitators to implementation and utilization of the intervention; and differences in perceptions regarding reporting burden on clinical and public health workers.

Gathering information to adjudicate notifiable disease cases can be complex and time-consuming. Greater accessibility to electronic health record systems could improve disease investigation processes. We analyzed case information, comparing cases where the health department had access to an EHR system and those without such access. Completeness was significantly higher where EHR system access was available.

Objective To characterize state and local health agency relationships with health information exchange organizations. Introduction There is growing interest in leveraging available health information exchange (HIE) infrastructures to improve public health surveillance (1). The Health Information Technology for Clinical and Economic Health Act and Meaningful Use criteria for electronic health record (EHR) systems are among the factors driving the development, adoption and use of HIEs. HIEs deliver or make accessible clinical and administrative data as patients are admitted, discharged, and transferred across hospitals, clinics, medical centers, counties, states and regions (2). While several HIE infrastructures exist (3), there is little evidence on the engagement in HIE initiatives by state and local health agencies. Methods An online survey of state and local health officials was conducted in six states where HIEs were known to be present. Half of the states were funded by the Centers for Disease Control and Prevention (CDC) to engage public health agencies in HIE activities; the other half received no such funding. A total of 143 officials were invited to participate; 73 (51%) responded. The survey asked respondents about their agencies awareness, engagement, and data exchange with HIEs. The survey further asked agencies about their perceptions of barriers and challenges to public health engagement with HIE organizations. Results Just 25% of agencies had a formal relationship, typically created through a memorandum of understanding or data usage agreement, with at least one nearby HIE. The majority (54%) of agencies either had no relationship (20%) or only an informal relationship (34%) with an HIE. The remaining agencies (18%) reported that no HIE existed in their jurisdiction. Agencies in states that had received CDC funding for HIE engagement were more likely (14 versus 2) to be formally partnered with an HIE. Conclusions Few public health agencies are formally engaged in HIE. Financial costs, human resources, and concerns regarding privacy/security were the top cited barriers to broader engagement in HIE. For public health to be an active participant in and reap the benefits of HIE, greater investment in state and local public health informatics capacity, including human resources, and education regarding HIE privacy and security practices are needed.

Despite the likelihood of poor quality data flowing from clinical information systems to public health information systems, current policies and practices are pushing for the adoption and use of even greater numbers of electronic data feeds. However, using poor data can lead to poor decision-making outcomes in public health. Therefore public health informatics professionals need to assess, and periodically re-evaluate, the quality of electronic data and their sources. Unfortunately there is currently a paucity of tools and strategies in use across public health agencies. Our Center of Excellence in Public Health Informatics is working to develop and disseminate tools and strategies for supporting on-going assessment of data quality and solutions for overcoming data quality challenges. In this article, we outline the need for better data quality assessment and our approach to the development of new tools and strategies. In other words, public health informatics professionals need to ask questions about the electronic data received by public health agencies, and we hope to create tools and strategies to help informaticians ask questions that will lead to improved population health outcomes.

Objective: To provide formulas for estimating notifiable disease reporting volume from ‘meaningful use’ electronic laboratory reporting (ELR). Methods: We analyzed two years of comprehensive ELR reporting data from 15 metropolitan hospitals and laboratories. Report volumes were divided by population counts to derive generalizable estimators. Results: Observed volume of notifiable disease reports in a metropolitan area were more than twice national averages. ELR volumes varied by institution type, bed count, and by the level of effort required of health department staff. Conclusions: Health departments may experience a significant increase in notifiable disease reporting following efforts to fulfill meaningful use requirements, resulting in increases in workload that may further strain public health resources. Volume estimators provide a method for predicting ELR transaction volumes, which may support administrative planning in health departments.