Innovative healthcare organizations around the country are using carefully-planned care management initiatives as a strategic tool to maximize the impact of each care manager, improve patient health outcomes, and reduce utilization. In short, patients with complicated diagnoses get the layered, holistic care they need, and hospitals are giving these patients a better prognosis.

Care management is an important component of population health management, but sometimes measuring and gauging these programs’ effectiveness is a struggle. Poor measurement strategies can result in organizations cutting their care management programs too early, before they have a chance to show results. Poor measurement can also cause a misalignment between the resources being invested and the organization’s ultimate population health goals.

The solution? Excellent data and predictive analytics, so healthcare systems can keep a constant finger on the pulse. Below, we outline a few key ways organizations can measure these programs and grow them into the future.

Data drives care management with impact

To effectively measure the impact and value of your care management program, think about it as having five distinct phases:

  1. Planning
  2. Program Launch
  3. Operational stability
  4. Mature program
  5. Transition

1. Planning 

This is the first phase of any chronic care management program, where initial scoping and sizing exercises take place. Here’s where a healthcare network plans activities required for program launch, and here’s where the following questions can be answered by good data: 

  • Is there an identifiable pool of patients that could be impacted with a program like this? 
  • What is the ROI target, and when will it be measured? 
  • Are there enough patients with enough medical cost to justify the program? 

As you evaluate the volumes of patients you may be able to impact, there are still more considerations to take into account. Think of engagement rate projections (who will respond?) as well as enrollment rate projections (who will volunteer for such a program?). Assume that you will need to engage substantially more patients than you will eventually enroll. For Medicaid programs, your engagement rate may need to be twice as high as your desired enrollment rate. 

2. Program launch

You’ve passed the planning milestone, and now the program is getting off the ground and starting to enroll patients. As your organization starts to roll out the care management program, your healthcare analytics focus should shift to these key activities: 

  • Set up regular meeting cadence: Start with biweekly first, then change to monthly meetings
  • Implement program reporting: Here’s where the data comes in (again)
  • Validate planning assumptions: Do the initial projections still seem correct? If not, recalibrate
  • Provide clinical program management dashboards: Care managers and other clinical leaders can use data to view issues as they arise

3. Operational stability 

Your program will hit this phase when the team feels that the program is running smoothly, you see that the process measures have stabilized, and patients are moving through the program at appropriate timelines. This is the most challenging stage of the program from an analytics perspective. 

Why so challenging? It’s unrealistic to expect immediate perfection, so you will want to adapt to feedback and make changes along the way. However, as you optimize your program, ensure you track these changes so your data and reporting are accurate. For example, clinical review may have excluded patients selected during initial stratification. Here are some of the challenges you may run into, and strategies for overcoming them: 

  • Data overload, it’s real: To solve this, make sure your focus is appropriately narrow, and only track the key measure you identified during your planning phase.
  • Jumping to conclusions: Set expectations and timelines for assessment, so you aren’t overanalyzing or overreacting to normal fluctuations in data.
  • Patient mismatch: Work with program leaders to continuously refine who’s enrolled.
  • Scaling woes: Make sure your care management team has plenty of support staff, preventing burnout and offering better services to patients.
  • ROI doubts: Sometimes, at this phase, it requires creativity to earn back ROI, but one way to do this is by educating a patient, so they continue to seek future care in-network.

4. Mature program

The timing of the transition to this phase will depend on the type and volume of the care management program, but typically you need at least a year of full claims run-out data before you can begin to assess program outcomes.

When you start evaluating program performance, it is critically important to have close collaboration between analysts, financial leaders, and care management leaders. Slight nuances can have major implications for reporting, so everyone needs to understand what is happening operationally.

The program you launched is not your current program. You will need to revisit your outcome measures to make sure that they accurately reflect program performance.

To do this, solicit input from care managers and health services managers, so analysts and providers are on the same page. Measure your program as it is, not as it was, and see whether you’ve been able to hit the intended outcomes you set out at the beginning.

Finally, resist comparing patients enrolled in the program to those outside it, as participants were selected due to the very intricate, costly nature of their diagnoses.

5. Transition

Once a program has been operating in a mature state and its impact can be evaluated, the organization should determine whether to cut, change, or extend the program.

At this stage, you have a fully operational program and will have made some assessments about the impact this program has had on the enrolled patient population. Now, it’s time to determine whether your care management program is viable in the long term.

Healthcare data analytics for complicated care management

To provide excellent care for patients with intricate diagnoses, data proves essential. Read the full white paper to learn more about how healthcare analytics can cut through the static to help organizations drive meaningful results, and why an analytics platform should be mission control for any care management program.