Utilization of Clinical Pathways Can Reduce Drug Spend Within the Oncology Care Model.

Purpose: Reducing drug spend is one of the greatest challenges for practices participating in the Oncology Care Model (OCM). Evidence-based clinical pathways have the potential to decrease drug spend while maintaining clinical outcomes consistent with published evidence. The goal of this study was to determine whether voluntary use of clinical pathways by a practice can maximize OCM episodic cost savings.

Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology.

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology.

High-dose busulfan, melphalan, thiotepa and peripheral blood stem cell infusion for the treatment of metastatic breast cancer.

The purpose of this study was to determine the outcome of patients with metastatic breast cancer treated with high-dose busulfan (Bu), melphalan (Mel) and thiotepa (TT) followed by peripheral blood stem cell (PBSC) infusion. Fifty-one patients with chemotherapy refractory (n = 32) or responsive (n = 19) metastatic breast cancer received Bu (12 mg/kg), Mel (100 mg/m2) and TT (500 mg/m2) followed by PBSC collected after chemotherapy and growth factor (n = 43) or growth factor alone (n = 8). The 100 day treatment-related mortality was 8% including one death from cytomegalovirus pneumonia, one from aspiration pneumonia and two from regimen-related toxicity (RRT).