Until now, managing risk in health care has largely been an exercise that was left to the payers. Provider organizations haven’t needed to accurately calculate the clinical and financial risks associated with caring for patients, because they didn’t bear the liability.
But the rise of value-based care and big data is changing the face of health care risk. Providers’ ability to take on risk has increased as clinical and claims data have become more widely available, making it possible for providers to approach managing patient health with a forward-looking, predictive model and not just a rear-view mirror, historical view.
Analyzing a cross-section of data, including clinical, social and demographic information, provides a full picture of an organization’s population and enables them to identify the risk factors for each of their patients.
By understanding population risk factors, providers can then target those patients who are most likely to benefit from engagement and interventions, and implement evidence-based care and disease management programs geared at reducing hospitalizations and cutting costs before they accrue.
Analytics give providers the ability to create a predictive model of patients who need extra clinical attention. The predictive model generally highlights which opportunities are available for intervention, and evaluates the most effective clinical path to address the problem.
Check out this video below to learn more about tapping into the power of analytics.
In our next post, we’ll look at how analytics can help organizations segment their population by risk and more effectively strengthen care management programs.