How a National Health Care Database Could Address Mission-Critical Health Data Gaps

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It’s time to consider a cloud-based national health care database to make better public health decisions and prepare for any future health crises.

While many valuable health care data sets live in public and private sectors, the data landscape is extremely fragmented. The inability to share information in real time across those sectors for population health or monitoring major chronic diseases is nonexistent. The devastating effects of COVID-19 uncovered significant gaps in public health data across federal, state and local agencies interfering with the ability to conduct surveillance, outbreak modeling and research. This lack of connectivity would have benefitted from a central repository of U.S. population health data. Instead, there were devastating effects on the health of millions of people due to the inability to pinpoint outbreaks quickly.

Government health care organizations deliver tremendous benefits in support of the American people. However, after many pandemic-related missteps, U.S. public health leaders can improve the exchange of health care-related information (i.e., clinical, public health and research data). This requires new standards, organization and streamlining to offer a complete, centralized data repository to inform public health responses. It’s time to consider a cloud-based national health care database to make better public health decisions and prepare for any future health crises.

Centralizing siloed data

Having a holistic view of de-identified data is a critical element of making population health predictions, but there are currently significant issues when it comes to accessibility of these massive amounts of data. Typically, health data is collected independently across agencies–with data coming from federal and commercial databases alongside state and local health agencies, and inevitably it sits in disparate information silos. This creates serious challenges when accessing information for real-time insights and inhibiting the ability to quickly act for better population health. 

Streamlining data for the power of decisioning  

A centralized system can pull information from each disparate data set and feed it into a main repository. Once the data has been aggregated and anonymized agencies can use the power of decisioning to act on this information to make predictions on health trends or analyze emerging health factors. 

For example, a public sector health care organization in Norway responsible for connecting health services across the country was tasked with developing, managing and operating a national e-health solutions and infrastructure. With data sources across the Norwegian health care system, the infrastructure was disjointed between groups. Some of the structured data was gathered for very specific purposes, while other large databases had strict limitations on use, such as patient records. Anyone from an agency who needed access to data for research had to send an application to each registry that housed it, with some approvals taking 17 months on average. To curb this significant delay, the agency built a single, comprehensive web platform, accelerating health research and projects while reducing application processing time from 17 months to 17 minutes. 

Operationalizing dataflow for intelligent automation and case management

AI and decisioning capabilities are at the center of what it takes to get underway. Starting with the right business architecture methodology will help government agencies ensure that the technology is future-proof, and robust enough to handle both current and future public health needs. To achieve this, they must design a unified platform that can offer integration without limits, manage end-to-end orchestration of work, automate work and processes, incorporate AI and decisioning, and securely store and/or process health information. 

It’s more than aggregating data–government agencies need the tools and technology to make decisions based on that data to improve population health and adapt to changing public health needs. This is where case management and intelligent automation come in. Case management can help manage a set of processes to collect, track, and consolidate data to achieve a specific outcome, going beyond workflow to automate the response and resolution of the work. For example, case management can trigger an investigation if the number of positive influenza cases in a particular region was above a certain threshold over a set period. 

The sheer size and magnitude of data sets for public health seems intimidating but the technology is here to reduce that stress. A central data repository will not only support the collection, aggregation and continual updating of health information, it will also help improve the inevitably siloed and broken approach that’s hindering our health care system. A strategic approach to a national health care database will help community and federal agencies prepare for, track, and respond to population health needs and work toward eliminating health disparities across the country.

Kelli Bravo is vice president of health care and life sciences at Pegasystems.

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