How to measure care management’s impact
Care management programs are critical tools for helping vulnerable populations, and data analytics are the key to measuring their success and efficiency.
Care management programs are one of a healthcare system’s most valuable tools when it comes to caring for patients with complicated or co-occurrent conditions. When the problem is multiple diseases that require monitoring and careful treatment, a team of experienced providers is an invaluable asset. But where human expertise goes a long way, data sharpens insights, giving you a window into performance, efficiency, and quality of care.
Here, we discuss how you can monitor a care management program’s success with data analytics, from taking evidence-based next steps to identifying an ideal pool of patients.
Key questions to evaluate a care management program
A huge pool of data can feel impenetrable and overwhelming, but these key questions will help you wade through a sea of information and come out with meaningful conclusions. Together, these questions will lead to metrics that give you a holistic sense of your care management program’s performance and highlight areas worth improving.
1. Who’s in the care management program?
The first key question in measuring your performance is getting a clear sense of who’s enrolled. Obviously, those are the numbers that will guide future decisions. But what’s less obvious is that participants can drop out, or enrollees can join after an initial count. Make sure that you’re keeping tabs of who’s in the program and checking in tandem with any reports you run.
2. Are the measures nuanced and program-specific?
It’s time to revisit a question that came up during the planning stages of the program. Check in now that you’ve got things up and running: are the measures for failure or success specific and quantifiable? Do they speak to your program, specifically, and not the general performance of your organization? Ensure you have a clear understanding of the specific impacts the program is intended to drive. For example, look at both readmissions and admissions. Have you been able to reduce readmissions but not admissions or ED visits? This clarity of purpose and measurement criteria will help you draw the most meaningful insights healthcare data analytics have to offer.
3. Do you have the right comparison group?
Ideally, metrics should tell you whether enrollment in your care management program is helping people with difficult-to-manage conditions more than if they’d opted out. To get there, you’ll need a comparison group. Make sure you select the right set of patients for comparison to your population under care management. One effective approach? Use the patients who were selected for the program but declined to enroll, so you’re working with a similar baseline disease profile but a different set of treatments and interventions.
4. Can you see trends graphically?
With so many data points in the care management constellation, you need a visual North Star. Synthesizing the information from your program in a graph is a great way to distill what’s important and share it with key stakeholders. This will give you insights or areas of inquiry you might not otherwise have. However, as always, tread carefully when exploring highly-volatile data.
5. Do you need more information on large variances?
If you see large increases or decreases in utilization or cost groups that are out of scope for the care management program, collaborate with care managers to assess if a variance is caused by the care management program or should be considered noise. Are diabetics in a care management program having more emergency department visits than anticipated, for example? Here’s where you tease out correlation from causation (and vice versa), and recalibrate your expectations around what the program will cost in the future.
Keep it real(-time)
“As It Was” might be a chart-topper, but you should measure your care management program as it is, in the present tense. As we’ve mentioned before, a good care management program relies on clearly defined objectives before it even launches.
The hardest time to measure results is, coincidentally, just as the program’s getting started! 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.
Typically, it will take up to a year before a care management program returns enough claims run-out data to mature and draw meaningful conclusions. Keep this in mind, stay in the present, and make sure your decisions aren’t hasty.
Meet us at the vanguard of value-based care
Arcadia’s mission centers on healthier lives for all, and well-run healthcare organizations are essential to that outcome. We’d love to help you gather, organize, and contextualize the data you amass so you can turn insights into action. Read more about launching and evaluating a care management program in our full white paper below.