At a time when managing patients with chronic conditions has become increasingly vital, organizations can take various approaches to better understand their patient populations and manage their resources more effectively.
In 1996, when Montefiore Health System first began stratifying patients according to risk of utilization, the approach was fairly simple.
"We used very basic stratification models based on claims data that we received from payers," says Urvashi Patel, PhD, senior director and chief data scientist with Montefiore's care management organization.
That process in fact predated Patel's tenure at the New York City-based health system, which comprises 11 hospitals, a medical school, a school of nursing, and various primary and specialty care clinics.
That initial approach involved aggregating patient data from a variety of sources into several databases, then stratifying those patients into different categories based on their use of services and calculating a risk score, Patel says. For example, a patient visiting the emergency department (ED) multiple times for chronic conditions would be given a higher risk score than someone visiting the ED for appropriate care, such as a sprained ankle.
Today the health system takes a multipronged approach to risk stratification that includes rigorous analysis using statistical modeling, Patel says. But that basic method still plays a role.
"As we gained more experience, we started to bring in additional data elements, such as electronic medical record (EMR) data, when they became available," she says. The organization also takes into account nonclinical data, including health risk assessment information gathered internally, such as demographic data on financial and housing status, Patel says.
Managing patients with chronic conditions has become a central strategy in population health endeavors and other efforts to optimize the quality of care. Risk stratification enables providers to better understand their patient populations and manage their resources more effectively.
By: Karen Wagner