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Data Sharing

This paper describes the value of a distributed approach to population health efforts that span clinical research, quality measurement and public health. The goal of the paper is to challenge the traditional paradigm which relies on centralized data repositories with more distributed models where data collection and analysis remains as close to local data sources as possible. We will propose that a distributed approach is desirable because it allows for information to reside more closely with those who can act upon it and it can overcome existing barriers by allowing information to be shared more rapidly and effectively while minimizing privacy risks.

Referenced File
Submitted by elamb on

Automated Electronic Disease Surveillance has become a common tool for most public health practitioners. Users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. The insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other nonpublic health officials. Disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. This abstract describes the development of tools necessary to support information sharing between public health and partner organizations.

Objective

The objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health ESSENCE users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. This capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. These interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. This project provides tools that can enhance emergency management situational awareness of public health events. It also allows external partners a mechanism for providing feedback to support public health investigations.

Submitted by uysz on

Accurately gauging the health status of a population during an event of public health significance (e.g. hurricanes, H1N1 2009 pandemic) in support of emergency response and situation awareness efforts can be a challenge for established public health surveillance systems in terms of geographic and population coverage as well as the appropriateness of health indicators. The demand for timely, accurate, and event-specific data can require the rapid development of new data assets to “fill-in” existing information gaps to better characterize the scope, scale, magnitude, and population health impact of a given event within a very narrow time-window. Such new data assets may be concurrently under development and evaluation while being used to support response efforts. Recent examples include the “drop-in” surveillance processes deployed at evacuation centers following Hurricane Katrina1 and the illness and injury surveillance systems established for response workers during the Deepwater Horizon Oil spill response. During the 2009 H1N1 pandemic response, CDC acquired access to data from several national-level health information systems that previously had been un-vetted as public health information sources. These sources provided data extracts from massive administrative or electronic medical records (EMR) based in hospital and primary care settings. It was hoped that such data could supplement existing influenza surveillance systems and aid in the characterization of the pandemic. Few of these new data sources had formal documentation or concise information on the underlying populations and geographies represented.

 

Objective

To describe data management and analytic processes undertaken to rapidly acquire and use previously unavailable data during a public health emergency response.

Submitted by hparton on

Since 2009, the Cook County Department of Public Health (CCDPH) has created and disseminated weekly surveillance reports to share seasonal influenza data with the community and our healthcare partners. Surveillance data is formatted into tables and graphs using Microsoft Excel, pasted into a Word document, and shared via email listserv and our website in PDF format.

Submitted by Anonymous on

Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health. While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1. A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language. The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors. LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.

Objective:

LANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.

Submitted by elamb on

In 2016, the BioSense Platform for national syndromic surveillance made substantial enhancements including data processing changes, a national ESSENCE instance, and management tools to support diverse data sharing needs. On August 21, 2017, a total solar eclipse occurred over much of the United States. The event resulted in large gatherings over multiple days to areas in the Path of Totality (PoT). In the days leading up to the event, public health and emergency preparedness included syndromic surveillance in their monitoring plans. To support this effort, Illinois (IL), Kentucky (KY), and Tennessee (TN) established inter-jurisdictional aggregate data sharing to get a more inclusive view of cause-specific illness or injury in Emergency Department (ED) visits before, during, and after the eclipse.

Objective:

Describe cross-jurisdictional data sharing practices using ESSENCE and facilitated by the BioSense Platform for a national mass gathering event, and the dashboard views created to enhance local data for greater situational awareness.

Submitted by elamb on

With increasing awareness of SyS systems, there has been a concurrent increase in demand for data from these systems – both from researchers and from the media. The opioid epidemic occurring in the United States has forced the SyS community to determine the best way to present these data in a way that makes sense while acknowledging the incompleteness and variability in how the data are collected at the hospital level and queried at the user level. While significant time and effort are spent discussing optimal queries, responsible presentation of the data - including data disclaimers - is rarely discussed within the SyS community.

Objective:

To discuss data disclaimers and caveats that are fundamental to sharing syndromic surveillance (SyS) data

Submitted by elamb on

Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing eICR of sexually transmitted infections (STI) with a clinical partner. Existing standard vocabulary codes were identified to represent previously-identified information gaps, or the need for new codes or concepts was identified.

Objective:

Previous research identified data gaps between traditional paper-based STI notifiable condition reporting and pilot electronic initial case reporting (eICR) relying on Continuity of Care Documents (CCDs) exported from our clinical partner’s electronic health record (EHR) software. Structured data capture is needed for automatic processing of eICR data imported into public health repositories and surveillance systems, similar to electronic laboratory reporting (ELR). Coding data gaps (between paper and electronic case reports) using standardized vocabularies will allow integration of additional questions into EHR or other data collection systems and may allow creation of standard Clinical Data Architecture (CDA) templates, Logical Observation Identifiers Names and Codes (LOINC) panels, or Fast Healthcare Interoperability Resources (FHIR) resources. Furthermore, identifying data gaps can inform improvements to other standards including nationwide standardization efforts for notifiable conditions.

Submitted by elamb on

A report jointly released by the de Beaumont Foundation and Johns Hopkins University, Using Electronic Health Data for Community Health: Example Cases and Legal Analysis provides public health departments with a framework that will allow them to request data from hospitals and health systems in order to move the needle on critical public health challenges.

Submitted by ctong on

Recent efforts to share syndromic surveillance data have focused on developing national systems, namely BioSense 2.01 . The problems with creating and implementing national systems, such as legal issues, difficulties in standardizing syndrome definitions, data quality, and different objectives, are well documented. In contrast, several local health departments have successfully shared data and analyses with each other, primarily during emergency events. The benefits of locally-driven data sharing include: (1) faster dissemination of data and analyses that have been created by those who understand the nuances of their own data, (2) easier process of standardizing syndrome definitions, (3) quickly designing appropriate analyses for the event, (4) smaller group of partners for consensus-building, and (5) ultimately improved timeliness in detection of public health events. The strategies used to share data and analyses between local and state health departments during planned and unplanned events may be informative to national systems.

Objective

To outline successful strategies for regional data-sharing and discuss how these strategies can be applied to other regions.

Submitted by teresa.hamby@d… on
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