Understanding Utilization and Improving ROI Through Patient Cohort Analysis

Our member is a digital health management company that empowers people with chronic conditions to live better and healthier lives and prompt members to take action when it’s most likely to have clinical impact.

Download the Case Study as a PDF


Our member is a fast-growing healthcare technology firm. They are specifically focused on helping patients better manage their chronic conditions. This company has an innovative platform to assist patients in their healthcare journey, however they were struggling to understand what patients would benefit most from particular interventions. Some chronic conditions like hypertension, obesity, and diabetes are very prevalent and therefore difficult to identify subpopulations to act on.

Identifying these sub-populations was an issue because it was difficult for our member to get a large enough dataset to do such an analysis. Additionally, they found it to be very challenging to do the chronic condition assignment and patient segmentation. They lacked the insight into whether they were focusing on the most valuable benefits, or obtaining the best return-on-investment possible.

How CareJourney Helped

This member took advantage of CareJourney’s Cohort Atlas solution that offers the ability to segment patients into granular cohorts. Specifically, CareJourney was able to take ten popular chronic conditions and segment patients into hundreds of mutually exclusive multi-chronic condition cohorts.

The member leveraged cost, utilization, and outcomes measures calculated for each paitent cohort to pinpoint populations of interest. This level of segmentation and analysis made it clear that not all diabetes patients, for example, were utilizing healthcare the same way, and therefore should not be treated the same. Our analysis was able to show the cost and risk multiples that were created by additional comorbidities. This analysis brought to light that certain comorbidities show little to no additional effect, whereas others, such as behavioral health, more than doubled a patient’s expected utilization.


Using our patient cohort solution, our member was able to focus on certain subpopulations of patients to provide targeted benefits to improve outcomes and improve ROI. For example, they were able to dive into sub-segments of diabetes. As the table below shows, an additional condition of hypertension or behavioral health drastically changes a patient’s expected utilization. These insights helped them engage with select physician leads for feedback on utilization and facilitate performance improvement conversations.

Additionally, the member opened up the data for patients through a self-service, public website so that patients can make more informed decisions about their own care.

We are continuing to look at combinations of three or more conditions to accurately identify the patient segments of interest. Moving forward, we will continue to partner with them and find new innovative approaches to identify patient cohorts with unmet needs.

Diabetes  $3,012 1.00
Diabetes + Behavioral Health $6,721 2.23
Diabetes + Dyslipidemia $3,219 1.07
Diabetes + Hypertension $5,636 1.87
Diabetes + Obesity $3,046 1.01


Using our cohorting solution, our member was able to focus in on certain sub-populations of patients to provide targeted benefits to improve outcomes and improve ROI.