Variability in hospital treatment costs: A time-driven activity-based costing approach for early-stage invasive breast cancer patients

Abstract

Objectives: Using a generic treatment path for breast cancer, and the molecular subtype perspective, we aim to measure the impact of several patient and disease characteristics on the overall treatment cost for patients. We aim to generate insights into the drivers of cost variability within one medical domain. Methods - A generic treatment pathway was developed, process maps were constructed identifying all relevant activities, medical personnel, direct medical materials and facilities used for treating patients. Through face-to-face interviews with the medical staff and direct observations, time estimates were captured for each activity. The cost of resources were obtained from the financial database of the hospital. The per unit cost of supplying the resources were calculated by dividing the financial cost and the practical capacity rate. The per unit cost was then multiplied by the time spent per activity to obtain the full cost for each step in the treatment process. Results - Significant cost variations within each molecular subtype and across molecular subtypes were found. Typically for luminal A the cost differential amounts to roughly 166%, with the greatest treatment cost amounting to $29,780 relative to $11,208 for a patient requiring less medical activities. The major driver for these cost variations relate to disease characteristics. For the luminal B classification a cost difference of roughly 242% exists due to both disease and patient related factors. The average treatment cost for triple negative patients amounted to $26,923, this is considered to be a more aggressive type of cancer. The overall cost for HER2-enriched is driven by the inclusion of Herceptin, thus this subtype is impacted by disease characteristics. Cost variability across molecular classifications is impacted by the severity of the disease, thus disease related factors are the major drivers of cost. Conclusions - Given the cost challenge in health care, the need for greater cost transparency has become imperative. Through our analysis we generate initial insights into the drivers of cost variability for breast cancer. We found evidence that disease characteristics such as severity and more aggressive cancer forms like HER2-enriched and triple negative have a significant impact on treatment cost across the different subtypes. Similarly, patient factors such as age and presence of gene mutation contribute to differences in treatment cost variability within molecular subtypes.