HEALTH[at]SCALE Data Intelligence Contextualizes Data for Better Care Decisions

Payers, providers, and patients suffer when disjointed and inaccurate data guide the decision-making process. Data quality issues pose a major challenge to every aspect of care delivery and management, leading to worse outcomes, unnecessary costs, and decreased satisfaction and member trust.

HEALTH[at]SCALE’s Data Intelligence deals with noisy, siloed, incomplete health care data streams for payers and providers, weaving together multiple data assets into a unified, enterprise gold-standard data asset, using machine intelligence to identify and address the imperfections of data collection in common operational settings; and organizing it into patient, provider, and encounter data objects and relationships.

HEALTH[at]SCALE’s contextualized data is helping organizations manage member health, streamline operations, prevent gaps in care, and increase patient satisfaction. No more missed care, incorrect or low-value treatments, or members seeking care in suboptimal settings. 

To learn more about HEALTH[at]SCALE’s Data Intelligence, payers and providers can email us at info@healthatscale.com, or visit healthatscale.com.

Video transcript:

Laura Carlson: Hello, my name is Laura Carlson. I'm director of growth at HEALTH[at]SCALE, and today I'm talking with Chief Technology Officer and MIT professor John Guttag – and with Chief Medical Officer, Dr. Mohammed Saeed, who's a cardiologist and a faculty member at the University of Michigan. We're here to talk about HEALTH[at]SCALE's Data Intelligence, a patented technology that HEALTH[at]SCALE has used to build a number of successful products. Data Intelligence takes noisy, hard-to-use administrative and clinical health care data from disparate sources and turns it into productive insights. Mohammed, what are the problems that HEALTH[at]SCALE is solving for payers?

Mohammed Saeed: Thank you, Laura. HEALTH[at]SCALE has been focused on improving value-based care and this has been achieved by understanding the clinical context of the patient, the provider, and the clinical settings. Our mission has been to drive health care towards a hyper-personalized model. We're able to do this using machine intelligence that takes advantage of increasing volumes of data being collected and improves healthcare delivery and outcomes. When we identify a patient at risk of an adverse medical event, we're able to identify ways to intervene to get those patients the care they need. We're also able to navigate patients toward the best providers at the right times to achieve better outcomes. It's based on a nuanced clinical understanding of each encounter and understanding the patient and understanding how providers have performed on similar patients in the past.

Laura Carlson: And John, what advances has HEALTH[at]SCALE made that enable it to bring these capabilities to your clients?

John Guttag: HEALTH[at]SCALE's Data Intelligence is a patented set of technologies that we can use to create a cohesive fabric of data out of a collection of massive disparate data sets. It does four things: First, it stitches together noisy fragmented data from multiple sources. It then organizes that data around sensible data abstractions, such as patients, providers, and encounters. It then stores the data in a proprietary universal format that's suitable for building analytics. And, then of course, there are a bunch of analytics that sit on top of this that really drive our products and provide value to our customers.

Laura Carlson: Can you explain to us a bit more about, what exactly, the infrastructure is doing?

John Guttag: Let me not get involved in a lot of technical jargon and begin with an analogy. Most of you are familiar with the story of the six blind men and the elephant. Each touches a different part of the elephant and based upon what they're touching reaches a different conclusion about the animal they're touching. Medical data is a little bit like that. Different providers each see a patient from a different perspective. And they enter accurate but apparently inconsistent data. Our job is to take all of this information, as we would say the six reports from the blindmen, and synthesize a cohesive description of what the animal, or in this case, the patient actually is like. This is quite different from existing systems, which do this kind of thing or attempt to do this kind of thing either by manual work or rule based systems. These systems are slow to run, to develop, and maintain and easily become outdated. In contrast, our Data Intelligence uses advanced machine learning to synthesize this cohesive view of individuals.

Laura Carlson: What makes personalization so important and why is it so difficult?

Mohammed Saeed: As a physician, I have learned that no two patients are exactly the same. So we've approached this idea of personalization as the best way to improve healthcare delivery and outcomes. As an example, we look at post acute care delivery where patients are being discharged from the hospital and we need to try to reduce the chance of hospital readmissions. This requires an understanding of what happens to the patient in the hospital setting, what types of skilled nursing facilities patients may need to go to, what are the right home-health care agencies, and who are the right providers for those patients in the outpatient setting to prevent readmissions. And to achieve this across different geographies across the U.S. makes it an even more challenging problem that requires a scalable intelligent data solution.

Laura Carlson: John, last question for you. What makes HEALTH[at]SCALE unique?

John Guttag: First, and probably most importantly, we assembled an extraordinary team of the best minds in machine learning and health care. Our team is dedicated, highly educated and deeply committed to the company's mission of simultaneously improving health care outcomes and reducing cost. Secondly, that's kind of the subject of today. We continually invest in our technology platform. From the very beginning, we knew that our success was not going to be based upon marketing or sales. It was going to be based on having the best product sitting on top of the best infrastructure. Finally, we're a very focused company. We're not interested in applying machine learning to finance or agriculture or anything else nor are we interested in health care problems that don't require sophisticated machine intelligence. Keeping our focus has really allowed us to be excellent at what we do.

Laura Carlson: Thank you, John and Mohammed. To learn more about how HEALTH[at]SCALE uses Data Intelligence to drive high-value care, visit us at healthatscale.com