This July 1, 2017, 1127 hospitals will be included in the Centers for Medicare and Medicaid Services (CMS's) Cardiac Bundle. Of those, 475 will also participate in the Cardiac Rehabilitation Program. Like other mandated retrospective bundled payment programs, these new models are aimed at controlling costs while ensuring quality for what are often expensive and complex procedures. In the case of the Cardiac Bundle, Acute Myocardial Infarctions (heart attacks) and Coronary Artery Bypass Grafts (CABG or bypass surgery) are at aim. Cardiac Rehabilitation has been added to a selection of participating hospitals to test the effectiveness of rehab programs in driving down risk and cost for AMI and CABG patients. Taken together, the bundle is part of a broader program driven by CMS to prevent cardiovascular disease, which causes one in three deaths within the U.S. and costs more than $300 billion each year.
The Cardiac Bundle is structured around the patient's "total experience of care." From admission to the hospital through 90-days following discharge, the hospital is responsible for driving better quality at a lower cost. The underlying premise is that by enabling greater communication and coordination across hospitals, physicians, post-acute care providers, and other clinicians, care will be improved, risk will be diminished, and best practices will be shared. For more information on the program details, please visit the CMS Fact Sheet here.
At the core of every bundled payment model is prevention: prevent complications, prevent hospital acquired infections, prevent a readmissions, prevent the event from happening in the first place. Prevention necessitates two things: foresight to know who is at risk and the ability to effectively intervene. Emerging cognitive technologies are enabling these requirements faster and with greater precision than ever before. More specifically, Cognitive Clinical Success Machines (CCSMs) are entering the market that not only enable bundled payment models, they can help prevent events like heart attacks by providing a view into the exact individuals who will have an AMI within the next 12 months.
CCSMs work by delivering patient-level predictions, prioritizations, interventions, and recommendations directly to caregivers without disrupting the existing workflow. Think of these machines as a "plug-and-play" view into the future state of a patient. The high-definition picture that is rendered identifies areas of risk and provides the recommended care paths that will most effectively improve outcomes and quality. The core of the cognitive engine comprises self-learning Eigen Spheres. These spheres facilitate the consumption of disparate, incomplete, and inconsistent data-which are widespread in healthcare. They apply more than a quadrillion clinical and non-clinical considerations across hundreds of thousands of spheres. The result is a patient portrait that accounts for the clinical AND the exogenous factors that encompass more than 60% of a patient's risk. The resulting recommended actions are individualized, comprehensive, specific, and effective.
For the Cardiac Bundle, CCSMs provide a site agnostic solution that follows the patient across the care episode and even extends into the community. One of the great advantages of CCSMs is their ability to work outside of the hospital. They can identify those people at risk of a cardiac event while an individual is still within the outpatient setting. The University of Mississippi Medical Center (UMMC) is using CCSM technology to effectively identify patients at risk of AMI within the next 12 months to enable more effective and focused interventions (for more, please see the UMMC case study here. The ability to see beyond the four walls of the hospital drives preventative actions that may lessen the severity of an event and even prevent it.
Once a patient is in the hospital, CCSMs identify at-risk patients and provide suggested care paths to reduce readmissions, complications, hospital acquired infections, and mortality. They also provide recommendations on the best discharge disposition that will improve the patient's outcomes while reducing the risk of complications and readmissions during the 90-day post-discharge timeframe. This longitudinal view and the precision-prevention recommendations are embodied through clinical vectors. These vectors direct the machine's capabilities to the risks and events that are most important to driving quality outcomes. As new bundles and quality measures are added to CMS's program, CCSMs can quickly adapt by simply providing a different vector view into the high-definition patient portrait.
As we continue to march toward value-based care, prevention will inch closer to the center of our healthcare strategy. This is a good thing. Prevention is good for patients, good for providers, and good for our community. Concomitantly, CCSM technology will take a greater role as not just a technology enabler, but as a core clinical action-like a lab test or an MRI. These machines will drive highly precise views into risks along with the individualized recommended interventions that will improve lives and health. And that outcome is good for everyone.