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    Predictive analytics reduces chemotherapy-associated hospitalizations

    Risk stratification and predictive modeling can translate clinical oncology data into better decision making to protect chemotherapy patients and curb avoidable cancer care costs.

    That’s according to speakers at the AMCP Managed Care & Specialty Pharmacy Annual Meeting, in Denver, during the March 28 session, “Using Predictive Analytics to Improve Value-Based Cancer Care Delivery.”

    Cancer care quality and cost management “drive health plan actuaries crazy,” said Andrew Hertler, MD, FACP, chief medical officer of New Century Health. “It has the ‘perfect storm’ of challenges for predictability: high variability, high cost, and low volume.”

    Chemotherapy drugs represent 20% of cancer care costs but potentially-avoidable chemotherapy-related hospitalizations represent nearly as much, 18%, making it a major cost driver in clinical oncology, Hertler said.

    Hospitalizations among patients undergoing cancer treatment are frequent and “commonly related to chemotherapy toxicity,” he said. “Studies have shown that many chemotherapy-related hospitalization risks are predictable using routinely collected clinical data.”

    Data can reduce hospitalizations

    Clinical data can be employed in two ways to reduce chemotherapy-associated hospitalizations, Hertler said: risk stratification modeling and predictive analytics.

    Risk stratification uses prospective clinical data collected during treatment (prior to authorization), allowing timely use of that data.

    “While it is nowhere near as robust as claims data, claims data has a lag of up to 30 days—and that’s not going to do us any good,” he explained.

    Analyzing clinical data, New Century Health developed a proposed risk-stratification model to identify which patients are most likely to wind up in the emergency room—a major predictor of hospitalization and inpatient care. ER admissions risk factors in their risk stratification model include emetogenic or neutropenic chemotherapies, patient age, cancer stage and functional performance status, and line of cancer therapy.

    The model also includes patient body-mass index (BMI) or weight “and particularly changes in weight,” Hertler said. “When a patient begins losing weight, that’s a pretty global indicator that they are not doing well.”

    Each risk factor is weighted in the model, depending on how important a contributor it is to hospitalization risk. The result is a hospitalization risk-stratification score (high, moderate or low) that can be used to guide clinical decisionmaking, patient monitoring, and anticipation of interventions.

    The risk stratification model is easy to use, Hertler said. But it has limited data and lacks comorbidity data that can also influence hospitalization risk, he noted.

    Next: Predictive modeling capabilities

     

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