Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.

Automated electronic laboratory reporting improves the completeness and timeliness of disease surveillance, which will enhance public health awareness and reporting efficiency.

A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance.

As data sources become larger and more complex, the ability to effectively explore and analyze patterns amongst varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contains multiple variables, high signal to noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal datasets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geo-spatiotemporal datasets.

The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Proliferation of health information technologies creates opportunities to improve clinical and public health, including high quality, safer care and lower costs. To maximize such potential benefits, health information technologies must readily and reliably exchange information with other systems. However, evidence from public health surveillance programs in two states suggests that operational clinical information systems often fail to use available standards, a barrier to semantic interoperability. Furthermore, analysis of existing policies incentivizing semantic interoperability suggests they have limited impact and are fragmented. In this essay, we discuss three approaches for increasing semantic interoperability to support national goals for using health information technologies. A clear, comprehensive strategy requiring collaborative efforts by clinical and public health stakeholders is suggested as a guide for the long road towards better population health data and outcomes.

Objective To support public health surveillance and response to COVID-19 through rapid development and implementation of novel visualization applications for data amalgamated across sectors. Materials and methods We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. Results Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133,637 times by 74,317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. Discussion Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. Conclusion The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Linking electronic health record (EHR) systems with community information systems (CIS) holds great promise for addressing inequities in social determinants of health (SDH). While EHRs are rich in location-specific data that allow us to uncover geographic inequities in health outcomes, CIS are rich in data that allow us to describe community-level characteristics relating to health. When meaningfully integrated, these data systems enable clinicians, researchers, and public health professionals to actively address the social etiologies of health disparities.This article describes a process for exploring SDH by geocoding and integrating EHR data with a comprehensive CIS covering a large metropolitan area. Because the systems were initially designed for different purposes and had different teams of experts involved in their development, integrating them presents challenges that require multidisciplinary expertise in informatics, geography, public health, and medicine. We identify these challenges and the means of addressing them and discuss the significance of the project as a model for similar projects.