Care management, no matter how it’s delivered, is expensive. Population health managers need to understand where to deploy their limited care management resources for the best results.
This research briefing explains how to establish each patient’s current and future risk level, find the root causes of the patient’s health risks, and identify which interventions would make the biggest impact.
Which patients should you prioritize?
When our researchers modeled out the impact of different care management approaches on a capitated contract with 25,000 Medicare patients, they found that if health systems managed only the high-cost patients, they would end up with a nearly 5% negative margin by the fifth year of operation. A positive margin required managing high-cost and moderate-risk patients.
That's because without intervention, nearly one-fifth of moderate-risk patients will move into the high-cost category each year.
How much data do you need to start?
Having comprehensive data or world-class analytics is ideal, but you can start making an impact on population health with information you already have.
When we profiled leading care management organizations around the country, we found that every single one was segmenting its patient population, but they were identifying and managing population risk using tools that were simple as well as complex—sometimes as minimal as a single spreadsheet for common disease states.
A staged approach to advancing population health analytics
Start with the data you have and incorporate new sources of information over time. Since every provider has clinical data in some form—from electronic medical records to laboratory and e-prescribing systems—we recommend using your disease registry as a first step to flag outliers and surface undiagnosed conditions. Throw in utilization data from insurance claims, and you now have a broader view of how patients are using health care outside of your network.
But to see the best return on population health efforts, you have to prioritize interventions based on expected benefit, not patient risk. This briefing explores how you can expand your population data over time:
Achieve baseline analytics
1. Start with clinical data to prioritize patients within key disease states
2. Build in claims data analysis to get holistic population view
Build the population data set
3. Use visits and partnerships to capture critical patient data
4. Deploy team to fill remaining data gaps on riskiest patients
Attain next-generation analytics
5. Incorporate social and behavioral risk factors into segmentation
6. Prioritize patients by greatest benefit potential
Patient Focused Care
Screening and Prevention
Electronic Medical Records Strategy
Next, Check Out
How to Use Analytics to Segment Your Patient Population