Moving Beyond Condition Cohorts – Risk Stratification Analytics Part 5

Moving Beyond Condition Cohorts – Risk Stratification Analytics Part 5

Patient-centered Morbidity Cohorts

Many providers and care teams head down population health paths by targeting conventional, condition-based cohorts.  Yet, research has proven that the clustering of morbidity is a better predictor of health care service resource use than the presence of specific diseases.

Condition cohorts are one approach. Patient-centered morbidity cohorts are another. When combined, the result is a powerful analytic tool that demonstrate the risk variability within condition categories and assures clinicians are working on the right opportunities.

Reinforcing Patient-Centered Population Health

Condition cohorts have many applications, particularly for those looking to understand the health of their population and identify populations for wellness, disease, or complex case management programs. But, condition cohorts do not account for differences in disease severity, chronicity, or the expected need for resources.

Solely relying on conditions is limited as we know that not all individuals with a certain condition may need intervention. To bridge this gap, KPI Ninja clients using the Johns Hopkins ACG® System can capture the overall morbidity profile of each patient by using the system’s ability to assign risk based on various dimensions of an individual’s co-morbidity such as duration of condition, severity of condition, need for specialty care to treat the condition, etc. This risk assigned allows clinicians to quickly identify those patients who are most likely to need higher levels of health care services within a condition category.

Taking a closer look at the below table, let’s focus on the rheumatologic condition category…

What we discover in this example is that rheumatologic conditions are the second most prevalent condition category within the population, with over 30k individuals with this condition. The table illustrates how targeting this specific type of condition may not be needed, as not all individuals with this condition are in need for intervention beyond the care they are already receiving (53% of rheumatologic patients fall into the moderate or lower risk stratifications). Rather, only those individuals in the high to very high risk categories may benefit from intervention or case management programs.

Teams looking to create care strategies using analytics can easily use reports like this one to ease the transition from traditional, condition-based approaches to more person-oriented strategies. When the population is stratified based on overall morbidity, teams don’t lose their condition specific expertise, but build on this knowledge and amplify it with new clinical information, concepts and perspectives that highlight the whole person rather than only their condition(s). Not only does risk stratification present information to understand a populations’ health but it also identifies new opportunities for teams to consider how health care needs to change and how to better match interventions and care strategies to populations that will benefit the most.

Renee Towne

About the Author
Renee Towne
VP of Population Health at KPI Ninja, Inc.
Renee provides operational leadership of quality initiatives at KPI Ninja. Towne has a background in occupational therapy, education and experience in operational excellence across a variety of healthcare domains. Based on prior experience as a clinician that drove outcomes patient by patient, she is leaving a larger footprint by improving health care more comprehensively, population by population.

About KPI Ninja
KPI Ninja is a data analytics company that helps healthcare organizations accelerate their quality, safety, and financial goals with a unique combination of software and service. We are differentiated by our signature mix of technology, performance management consulting and healthcare expertise. We don’t merely offer software solutions but work shoulder to shoulder with clients to help them draw on the power of analytics and continuous improvement methodologies to become more efficient. In harmony with our data-centered ethos, we truly believe that our success is strongly co-related with yours.

Tags: , , ,