The centrality of health IT to ACO success

Achieving the goals of accountable care—better coordination, improved outcomes, lower costs—requires a robust health information technology infrastructure and the ability to analyze large volumes of patient data.

Modern Healthcare recently hosted a webinar that featured a leadership panel willing to share their views on the importance of health IT systems, the challenges associated with implementation and their advice for other accountable care organizations.

Modern Healthcare New York Bureau Chief Melanie Evans moderated a discussion with Jason Dinger, CEO of MissionPoint Health Partners, a Nashville-based subsidiary of Ascension that focuses on accountable care; Kim Kauffman, vice president of value-based care at Summit Medical Group, a 220-physician group based in Knoxville, Tenn.; and Katie White, assistant professor of health policy and management at the University of Minnesota School of Public Health. This is an edited transcript of that discussion.

Katie White: We are living in exciting times for health IT. It's clear that incentives for expanding IT systems are in place, but there's a lot of learning to be had before we see systems used to their full potential.

In our study of the ACOs in the Medicare Shared Savings Program and the CMS Innovation Center's Pioneer ACO program, we found that more advanced information technology and data analytics did not necessarily ensure success. For example, we had an integrated delivery system with a single electronic health-record system, sophisticated value-analytics capabilities, with years of performance in risk-based managed-care contracting that could not achieve shared savings.

On the other hand, we had a sizable physician group partnered with a hospital system with little risk-based or performance-based contracting experience that had multiple EHR systems but achieved shared savings in the first year with little IT support. They tracked patients with Excel spreadsheet-based registries and did extensive health coaching and care coordination throughout their practices.

The bottom line seems to be that truly adding value in health IT will come from developing the ability to analyze big data, to understand patterns in those data, and to act on trends where it matters most. We are just beginning to understand what exactly fully functioning health IT for value-based payment systems means.

Modern Healthcare: What are the key lessons you identified as you looked at the role of IT among Medicare Shared Savings ACOs?

White: When you enter programs like this, you come in with a set of situations and a context and you try to adapt based on whatever you have today and whatever your experience is. Some of these ACOs, frankly, were just pretty lucky. They've been on this journey for some period of time. Other ACOs found that they weren't prepared and they couldn't achieve those savings. Much of that had to do with not having the data at their fingertips to make really good decisions to put them into the right trajectory so that they could succeed.

Kim Kauffman: Summit Medical Group formed 20 years ago and is currently home to 220 physicians and about 140 advanced practitioners. It's physician-owned and primary-care driven, and 99.9% of our eligible sites have achieved Patient-Centered Medical Home Level 3 recognition. A full 35% of our patients are under a value-based contract, including one Medicare Advantage contract with upside and downside risk and contracts with our two largest commercial health plans.

Our strategy includes aligned incentives, full transparency and various tracking tools. We also use dashboards and side-by-side reports to demonstrate the provider's progress on quality measures, expense management measures and other key performance indicators such as admissions per thousand, emergency department visits per thousand, generic prescribing rates and readmission rates.

MH: For organizations that are new to data analytics, what do you see as the first steps?

Kauffman: A low-cost way to begin—and most organizations have the capability to do this in-house—is to start with a simple creation of disease registries and look for those historically high-utilizing, high-cost patients, or those patients who have problems but perhaps haven't seen their primary-care physician in the year to date, and engage those patients.

Then there's the opportunity to move on to hindsight. Again, it's the easiest information to wrap your hands around. That is the historically high-cost utilization. This presumes that past behavior is the best indication of future behavior. And then as your organization matures, endeavor to identify your rising-risk patients.

MH: It sounds as if transparency really played a role in achieving the outcomes you were seeking. How did you approach communication with physicians?

Kauffman: Years ago, when we first started down this path and multiple payers were approaching us with a list of 24 and 36 and 47 different quality measures they wanted us to track, we decided that we needed to pick a finite number of measures, develop workflows and processes and point-of-care reminder tools within the EHR for a subset of those measures and focus on those measures. And now, instead of a health plan approaching us with a myriad of measures, we approach them and say, “These are the measures we are prepared to knock out of the ballpark. Let's focus our work around these, and then we'll add a couple more next year and a couple more the year after that.”

The dashboard is fully transparent. Any of our providers can go on to our Internet site and look at their aggregate stars on these Healthcare Effectiveness Data and Information Set measures. Similarly, on a quarterly basis we send out a side-by-side report that shows each individual primary-care physician the number of patients they have in a particular contract, their medical-expense ratio, their risk score and their key performance indicators.

The newest addition to that stable of transparency tools relates to the distribution of bonus or pay-as-you-go performance dollars. Any time any of those dollars are distributed, every provider sees the same report with the provider name right there and the amount and the reason for which they were receiving incentive dollars.

Jason Dinger: Mission Point Health Partners started with 10,000 members in 2012. We're now managing the needs of over 250,000 members, and we've clinically integrated with more than 7,200 providers. We've learned a lot by being in different geographies and seeing quite a bit of variation.

The first step is getting a historical view of your population. As you know, a small percentage of people generate most of the cost, and that's one of the big challenges for ACOs. We have found, as we go on our IT journey, is that being able to stratify patients to make sure we're allocating the right amount of time to each person and engaging them in the right setting to really help them and their families is so important.

We are starting to do a lot of work around predictive modeling and machine learning, putting more and more data into kind of our data repository and letting that data get smarter and smarter about which interventions are working and which ones aren't. For example, recently we were looking at some data and found that our second call with the member is by far the most predictive of improving outcomes and lowering costs.

MH: Could you give us an example of how data stratification allows you to allocate resources efficiently and in an appropriate setting?

Dinger: The one that comes to mind is depression. Historically, we would have looked at folks with depression as asthma patients or active cancer patients and relate to them as such. But we now know that unless we can really help them through their depression, all the other conversations are just not going to have the same impact. By doing some scoring directly with members and working through our providers, we can get a little bit closer to the root cause.

MH: What would you recommend as first steps for organizations that are new to data analytics?

Dinger: I would find a partner to just help clean and standardize your data. There are a number of lower-cost solutions now on the market, and finding a partner can take a whole bunch of things off your plate as well as kind of reduce the number of potential errors. Then I'd listen and watch that data and really kind of soak yourself in what it can tell you about the people you're serving. And then I'd customize and slowly add to that data set and just let it get richer and richer for you and your partnering providers.