Health systems transitioning to value-based payment models have to make sense of the reams of clinical and financial data they collect each day.
It's a large and complex data set—but also a highly valuable one. Buried within it are the answers to a multitude of questions that vex doctors and administrators: How can we shorten length of stay after colon surgery? Why are we seeing so many insurance denials? What is a cardiac patient's risk of having another heart attack?
Ayasdi, a technology company based in Menlo Park, Calif., relies on machine learning and machine intelligence to tackle vast, multidimensional data warehouses. Its technology can find patterns in unorganized data sets that less-sophisticated algorithms are likely to miss.
“It's really the complexity where we rise to the top,” said Dr. Francis Campion, the company's chief medical officer. “It's the breadth and depth and detail that is the exciting frontier for these health systems.”
Ayasdi's platform is the first commercial application of a mathematical technique known as topological data analysis, or TDA, which was developed by mathematicians at Stanford University. The company grew out of research conducted by Gunnar Carlsson, a Stanford math professor, and his then-graduate student, Gurjeet Singh, to apply TDA principles to real-world problems.
Carlsson's work won $10 million in grants from the National Science Foundation and the Defense Advanced Research Projects Agency. Then in 2008, Singh and Carlsson created Ayasdi, and they are now its CEO and president, respectively.
Its healthcare platforms, launched in 2014, include Ayasdi Care, which mines electronic health record and claims data, and Ayasdi Cure, which is used by pharmaceutical and biotech companies to identify biomarkers and drug targets. The company also works with health insurers and financial services firms.
“Where Ayasdi really shines is in this large, complex data,” said Daniel Druker, Ayasdi's chief marketing officer. “It finds patterns that other techniques miss. It lets you very quickly find where there are exceptions.”
Ayasdi is most often compared to IBM Watson, which also uses machine learning to find patterns in unstructured information. But Druker likened the Watson platform to a “large library in the sky,” while Ayasdi focuses on combing through an organization's own data. In that way, it can help figure out why Medicare might be denying infusions for a particular hospital's oncology patients, for example, or detect billing fraud.