In the value-based world,  health plans and their provider partners share financial risk for the members’ healthcare expenditures as well as responsibility for quality and outcomes. Care management is an excellent solution to improve the members’ experience and outcomes as well as impact the costs. However, given the limited care management resources, it is critically important to have a thorough understanding of the population your care management programs will affect.

Most organizations use ‘blunt’ tools that either over-target or under-target. That’s why most care management programs are not showing a meaningful impact on the members whose health they are attempting to improve. One recent study showed a typical engagement rate of 13% or less. Often, these programs do not identify the right people to target, fail to reach these people successfully and do not engage  them in the way to influence change in their behavior.

High-need, high-cost (HNHC) members

“High-need, high-cost,” or HNHC, refers to members who have complex, costly health care needs and conditions, or whose risk of developing these conditions is imminent. These individuals are a small proportion of all members, but they account for a large percentage of health care spending. A common method to find HNHC members is the risk-based model. The population is stratified by risk score (mostly based on HCC models) and then the care management staff typically reaches out to the top 20% of people.  HCC models are highly weighed on chronic conditions and diagnosis coding,  and hence this approach can lead to suboptimal outcomes for people who have historically underused the primary care.

To maximize the impact of your care management programs, you should look beyond the chronic conditions, and explore opportunities in reduction of inappropriate procedures, optimized site-of-care choices, higher utilization of in-network and high-value providers, appropriate intervention choices, accurate coding of diagnoses, closure of care gaps, improved CAHPS performance and clinically appropriate use of medication, imaging and pathology. 

Data and Analytics

Before you select the eligible population for your care management programs, it is important to collect comprehensive data about the members. At the minimum, you should try to integrate medical, social and claims data. After the data collection and aggregation, you can use various techniques to identify and stratify members, including descriptive analytics and predictive modeling. Descriptive analytic approaches look at past data and predictive models, on the other hand, forecast future trends such as what paths members are likely to take.


Once eligible members have been identified, care management staff must begin enrolling members. A program’s enrollment strategy should be based on the program design, including when and where to enroll members, whether to use incentives, and how to retain enrollees. Careful planning during this step of the process will ensure higher and faster enrollments. Technology and analytics can can help you understand the right channels for each population cohort. For example, instead of calling all members or sending all of them text messages, analytics can measure and segment members  based on previous responses to various methods of delivery – to inform you of the best channel, the right time to send the messages as well as frequency of communication for each member.

Sprite Health digital care management provides advanced analytics and digital capabilities to help you improve the design and implementation of your care management programs, that promotes better health outcomes and an improved return on investment (ROI).