An Introduction to Risk Stratification Analytics
If you work in the health care industry, you’ve likely heard of risk stratification. There is a good chance that risk stratification, whether used for negotiating payment rates or improving population health, will be one of the most important elements in a value-based health care system. Citing several indicators of industry disruption, there is an urgent need to focus efforts on optimizing care delivery processes. Clinicians and leaders may be well versed with in-the-moment problem solving to address patient needs but when it comes to system improvements, the ability to understand morbidity trends and to proactively target subpopulations of risk can be very challenging.
What risk stratification means
Risk stratification helps clinicians and teams understand the health of the patients they serve and customize care to match the needs. Stratification is designed to segment the population into meaningful subpopulations, where those in the same subpopulation are more like each other than those in another. Analytics takes the value a step further by mining the data on your behalf to find meaningful patterns that aid in effective care planning.
“We believe that every clinician should have the most accurate, relevant patient information in order to provide high quality, personalized medicine.”
Stratification matters because it is ineffective to treat an entire population the same, yet incredibly inefficient to perform customized, detailed analyses at a patient level. Accordingly, risk stratification analytics provides an excellent foundation for system transformation by using advanced technology to gain deep insights required to deliver highly effective medicine without the need for more resources.
If the stratification truly reflects the needs, then the methodology to create the groupings ought to be robust. Anyone can create stratifications based on factors believed to be influential and we find many examples in practice (i.e. if a patient has more than two hospitalizations in six months, they are at high risk), but risk stratification can be dramatic to achieving outcomes when the methodology is based on morbidity patterns in a way that truly captures patient complexity. In an industry that is fragmented, using data-agnostic risk stratification analytics overcomes some of the biggest hurdles to improving the cost and quality of healthcare.
How risk stratification works
Risk stratification analytics improves the ability to understand your population, identify opportunities and improve care delivery. One example of its utility is the impact on readmission rates. Readmissions is a systemic problem that impacts 7.5 million citizens and costs our nation nearly $564 million dollars. Risk stratification analytics evaluate clinically and statistically relevant factors to identify patients that have a greater probability of seeking out care in an inpatient setting, thus a powerful predictor in identifying those at highest risk of returning to the hospital after a discharge.
Patients with conditions that are associated
with a greater probability of hospitalization
Do I really need it?
So many times, data analysis is seen as a burden, just one more thing we must do while we try to care for patients. Then again, if you are committed to “Do No Harm”, here are five simple reasons why these analytics should be a part of your daily workflow.
Risk stratification analytics helps you achieve your goals by:
- Flagging care delivery and pharmaceutical opportunities to better manage your population
- Identifying health risk factors to provide personalized, preventative medicine
- Predicting patients that are likely to experience adverse health outcomes and high costs of care
- Evaluating if variations are correlated to patient complexity or the way medicine is being practiced
- Supporting equitable rate setting in value-based payment structures.
With so many different advancements in health care, it can be difficult to keep track of exactly what is happening with analytics and what all of it may mean. So, the summary of risk stratification analytics is that it allows the opportunity to deliver individualized care in an efficient way, which can meaningfully impact the health care industry to reduce costs and improve quality.
Reference
Psotka, M., Fonarow, G., Allen, L., Maddox, K., Fiuzat, M., Heidenreich, A., . . . O’Conner, C. (2020). The hospital readmissions reduction program nationwide perspectives and recommendations. JACC: Heart Failure, 8, 1-11.
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.