How Graph Analytics is Helping Improve Personalized Healthcare

When the world’s largest healthcare company by revenue went looking for a technology solution that could improve quality of care while reducing costs, the search took ten years. What they found—an innovative way to model healthcare data—is saving the company an estimated $150M annually and enabling its medical professionals to provide accurate and effective care path recommendations in real time. It’s a remedy with important implications for the future of healthcare. 

This same solution, graph databases and graph analytics, proved crucial at the height of the Covid-19 pandemic. A testament to its potential, the market for graph technology is projected to reach $11.25B by 2030.[1]

Graph technology isn’t new. It’s what social networking applications use to store and process vast amounts of “connected” data. It turns out graphs can do much more than connect people to their high school friends. They are also perfect for storing and visualizing large healthcare data models so it can be quickly processed and analyzed. Graphs can make previously unavailable connections from disparate data spread across many different platforms. One example would be making connections between data collected from a patient’s various doctors and pharmacies. 

Why Graph Analytics is Important for Healthcare

Hospitals deal with stockpiles of data. Every touchpoint is stored in a hospital’s electronic health record including visits, prescriptions, operations, and immunizations. Too much data can be a challenge, making it difficult to access and analyze information when and where it’s needed.

Hence the business case for graph databases. Data that’s represented in the form of a graph rather than a table enables quick analysis and faster time to insights. For healthcare professionals, sophisticated graph algorithms can return specific results, and graph visualization tools allow analysts to make useful connections and identify patterns that help solve problems.

Graph analytics is an ideal technology to help to tackle the challenges caused by large, disparate, datasets since it becomes more impactful as the volume, velocity and variety of data expands.[2] Storing and accessing this data alone is not enough. As a tool set, graph analytics prioritizes the relationships between the data—an arena where relational databases fall short.

Data scientists and leaders in the healthcare industry can use the most advanced graph analytics, known as native parallel graphs, to link datasets across multiple domains. This would allow the system to find frequent patterns and suggest the next best action. Ultimately, medical professionals would be able to rely on the most accurate data to provide patients with beneficial, real-time recommendations. 

“In the past, when somebody called into our call center, we would have had to log into 15 different systems to get a view of this member’s activity. Now users log into just one screen and have a beautiful timeline view of every touchpoint we’ve had with members,” said a distinguished engineer from a major healthcare company that recently deployed graph technology.

The Impact of Graph Technology on Covid-19

A graph-based approach to community tracing and risk detection was essential in 2020 as government officials and healthcare professionals worked overtime to understand and prevent the spread of Covid-19. For government agencies, graph technology led to agile and evidence-based emergency management and improved public health emergency response. 

Because graph analytics can sift through thousands of data sources and find relationships, even with complex and varying inputs, it was an excellent way to answer complicated questions related to the spread of disease. These capabilities helped with contact tracing used to identify, locate, and notify people who had been exposed to the virus. 

The technology also recognized relationships between data points—for example, common symptoms of people more likely to have a serious case of Covid based on pre-existing conditions. Armed with this insight, healthcare providers could warn patients when they were at higher risk. 

Future Implications for Healthcare and Beyond

As the healthcare industry moves beyond the pandemic, it emerges more prepared to respond to a wide variety of situations—from widespread health crises and everyday patient care. Healthcare companies already applying graph databases and graph analytics are experiencing the benefits. The technology supports their work to help members embrace healthier lifestyles, avoid costly pharmaceuticals, recover faster from medical procedures and more. Essentially, healthcare companies using graph technology are better equipped to provide quality care while controlling costs.

For data-centric companies looking to implement these solutions, a graph database running on Dell PowerEdge servers is the optimal offering in terms of performance, efficiency, and scale. To learn more about the business benefits of connected data, read this brief and visit to learn about solutions for analytics.



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