Expanding surgical measures to understand CV service lines

The very nature of cardiovascular service lines (CVSLs) has changed dramatically in the last several years, along with the changing environment in healthcare. I’m often charged with helping practices and healthcare organizations optimize their service lines.

Integrations, partnership and service line expansion are the order of business today for many healthcare organizations. We have quantifiable evidence that this approach is the direction needed to improve quality, outcomes and efficiency. It’s especially true, given the change in emphasis from volume to value.

That’s not the only change, however. As CVSLs have become more prevalent in integrated systems, they have begun to incorporate more specialists as the service line is realigned to cover an entire continuum of skills. The new approach requires a far greater granularity in the type of data collected, measured and analyzed—an effort that parallels the overall focus among many medical professionals today, with initiatives like Accountable Care Organizations, Meaningful Use, Appropriate Use Criteria and more. 

For instance, you can now separate out cardiovascular surgeons from cardiologists. We used to call them all cardiovascular physicians. Now, we are able to differentiate between a cardiologist and a cardiovascular surgeon.

We’re tracking our surgical counterparts, looking at the Relative Value Units (RVUs) they generate, and their compensation, both with and without benefits. Furthermore, filtering allows us to look at data by ownership model or by subspecialty, in order to obtain more accurate reporting.

As the focus homes in on delivering superior healthcare at lower costs, practices must be able to determine where they stand today, in order to seek out improvements for tomorrow. We are working to add cost data in the near future. By doing that, we can track what we think is a better representation of value. For instance, we would be able to track best practices for the cost per diagnosis related group, such as costs incurred when treating a patient who has a heart attack or a coronary bypass surgery.

The bottom line is all about helping doctors and hospitals work together to cut healthcare costs from their systems. In order to do that, systems must proactively look at patient status across the entire continuum, from the time they enter a hospital facility, through post-acute care, and even after they return home.

One trend they will have to figure out is population–predictive indicators around multiple disease states. The goal here, if they can do that, is to prevent readmissions or even unnecessary admissions in the first place. If doctors and hospitals have the proper data, and can analyze it correctly, they can structure their operational strategies accordingly.

It's not just a pipe dream; the proper use of trending data has already been used to predict growth and decline in several areas. We’ve been able to track declines in PCI, cath lab utilization and nuclear studies, while documenting growth in conditions like congestive heart failure (CHF) and atrial fibrillation.

Medicare is now penalizing hospitals based on CHF patients who are readmitted. Some systems are using this data to establish a growth area in ambulatory care, with transition clinics being set up. This way, if a CHF patient comes back with similar symptoms, they can be treated without having to enter the emergency department or being readmitted, which would result in a penalty being levied against the hospitals.

We already have an incredible amount of data that’s accessible. As we add more, the trends and insights they provides will be invaluable.

Jeff Ozmon is vice president, consulting, at MedAxiom Consulting in Neptune Beach, Fla., and former vice president at Sanger Heart and Vascular Institute in Charlotte, N.C. He will be among the speakers at MedAxiom’s Cardiovascular Service Line Symposium June 18-20 in Bachelor Gulch, Colo. Cardiovascular Business and the American College of Cardiology are co-sponsors of the event.