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CDC Works to Improve Public Health Data Analytics, Surveillance

The CDC has made significant improvements to its public health data analytics capabilities, but is still working to enhance the nation's surveillance infrastructure.

Public health data analytics and surveillance

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- It may seem as if the clinical environment has taken the lead in collecting data and leveraging analytics for population health, but ambulatory providers and hospitals aren’t the only entities involved in surveilling and predicting community health issues.

The public health sphere, including the Centers for Disease Control and Prevention (CDC) is also working diligently to improve public health data analytics and disease surveillance in the United States and around the world, providing stakeholders with critical insights into significant population health concerns.

Public health entities face the same technical data aggregation and analytics challenges as care providers, including fragmented data sources, poor interoperability, and difficulty developing algorithms that can accurately predict and prepare stakeholders for a rapid response to disease hotspots.

“Many current systems rely on disease-specific approaches that inhibit efficiency and interoperability (e.g., manual data entry and data recoding that place a substantial burden on data partners) and use slow, inefficient, out-of-date technologies that no longer meet user needs for data management, analysis, visualization, and dissemination,” explained former CDC Director Thomas Frieden, MD, MPH and a team of CDC authors in a recent article published in Public Health Reports.

Just like in the clinical environment, public health organizations are replacing their outdated manual information-gathering systems with electronic data sources and speedier reporting tools that allow researchers to conduct timely surveillance and dive deeper into richer data sets.

READ MORE: Top 10 Challenges of Big Data Analytics in Healthcare

“CDC is making progress on improving surveillance data for public health action at the local, state, and federal levels,” the article says.  “Examples include advancing data timeliness, leveraging existing data platforms to address emerging needs, using nontraditional data sources, and improving the representativeness of data through better population coverage.”

Since 2011, for example, the CDC has dramatically increased its ability to collect mortality reports from the health system.  At the beginning of the decade, only 10 percent of national death reports reached the National Center for Health Statistics (NCHS) within 10 days of generation, making it difficult for researchers to understand mortality trends.

By 2016, as state vital registration programs due to federal investment and IT support, the CDC had access to 50 percent of mortality reports within the designated 10-day timeframe.

The National Violent Death Reporting System has also benefitted from health IT enhancements, Frieden said, and is now collecting data from 42 states, allowing local governments to target resources and interventions to regional concerns.  An enhanced web interface and improved data analytics tools allow public health officials to interact with available data more easily to guide these efforts.

Online data access has become a significant benefit for public health researchers and analysts, especially since the CDC has embraced data transparency and prioritized sharing with the healthcare community. 

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Numerous data sets are available for public consumption on the CDC’s website.  Health IT developers can also take advantage of the CDC’s application programming interface (API) option to create new tools for analyzing and leveraging the data.

The CDC also offers WISQARS (Web-based Injury Statistics Query and Reporting System), an interactive, online database that includes information about the impacts and costs of injuries and deaths.  A new mobile application includes visualization tools that make it simpler for users to view and understand the data.

Similar tools for environmental health issues and behavioral risk factors are also available, complete with interactive visualization tools that allow users to view information down to the sub-county level in some cases.

“Although the examples provided represent progress in enhanced data collection, data analysis, data visualization, and information dissemination, CDC and its partners continue to face major challenges and opportunities to improve public health surveillance,” the article acknowledges.

“Three key areas of focus for CDC to improve public health surveillance and incorporate recommendations from the [ONC’s Shared Nationwide Interoperability] Roadmap include (1) implementing shared information technology services, (2) developing the surveillance workforce, and (3) harnessing electronic health records and health care information technology systems.”

READ MORE: Which Healthcare Data is Important for Population Health Management?

The CDC plans to take the same approaches to interoperability and data sharing as its clinical partners, enlisting standards such as FHIR and other data standards to make it easier for stakeholders to communicate and aggregate big data.

The agency will also work with EHR developers and other health IT software and service providers to create electronic case reporting (eCR) tools that will not interfere with provider workflows.

“eCR can allow state and local health departments to conduct real-time surveillance without burdening health care providers,” the CDC says. “As part of fulfilling Medicare requirements for electronic health record implementation, clinicians will need to be able to send electronic case reports to state and local public health agencies by mid-2018.”

“Initial work on eCR is focused on reportable infectious diseases, and this approach may offer opportunities for timelier and better quality data on chronic health conditions, environmental health hazards, and injuries.”

The CDC will continue to collaborate with stakeholders across the healthcare system to enhance its data collection and analytics systems in support of more robust public health surveillance.

“Ultimately, systems are needed that efficiently allow data to move from clinical encounters or primary data collection at a local level to public health departments, and to be appropriately shared with CDC with minimal human effort, while also maintaining privacy and protecting confidentiality of individual data,” the article concluded.

“These systems can make the best use of technology to reduce burden, particularly on data partners, and improve both the quality and timeliness of data collection, analyses, and dissemination. Consequently, further advances in surveillance are intimately connected to both data collection and dissemination.”

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