Our objective is to connect healthcare at scale. Virginia is a perfect example. You have 130-some hospitals and health systems, hundreds of post-acute operators, and thousands of ambulatory providers across the state, along with Medicaid, Medicare, and commercial health plans. The state’s objective was not only to reach a level of interoperability in terms of data sharing, but even more so, to reach a level of collaboration to manage down medically unnecessary utilization, avoidable friction, or risk.
Collective Medical improves outcomes and lowers costs on an impressive scale. In a Brookings Institution review of Medicaid patients who visited emergency rooms in Washington State, Collective Medical’s network and EDIE application—allowing actionable, real-time coordination across organizations—was one of the core strategies for lowering the number of ED visits by patients with patterns of high ED utilization. By partnering with Collective Medical to focus on these patients, Washington State reported $34 million in savings in emergency costs and a decline of 9.9 percent in emergency department visits in its first year of use in 2013.
“Event notification systems and care coordination applications have historically struggled to provide actionable information to providers at the point-of-care,” Noah Knauf, partner at Kleiner Perkins, said in a statement. “Collective Medical is the first technology we’ve seen that allows the providers and payers in a local healthcare system to efficiently collaborate, delivering significantly better outcomes through risk analytics, real-time notifications, and shared care planning tools. Supporting this team is a rare opportunity to be a part of something that is meaningfully changing the way care is delivered in this country.”
Collective Medical will use the funding to expand and advance its network with the goal of empowering care teams across the country to provide patients with the most effective care. As a part of this effort, Collective Medical plans to expand its leadership team and scale its engineering, clinical support, sales and marketing organizations. The company anticipates hiring more than 100 additional team members in the next 12 – 18 months, with the majority based in its Salt Lake City headquarters.
Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”