Using patient-generated health data to facilitate preoperative decision making for breast cancer patients

Approximately 1 in 8 U.S. women will be diagnosed with new invasive breast cancer over the course of her lifetime. An estimated 252,710 new cases of invasive breast cancer are expected to be diagnosed in women in the U.S. in 2017. Mastectomy is recommended in over a third of early-stage breast cancer patients. Those women who elect to undergo breast reconstruction are counseled on surgical risks and benefits of implant-based and autologous reconstruction. Currently, there is limited patient-centered information about course of recovery, which is a major consideration when deciding between types of reconstruction. Patient recovery estimates are often anecdotally related to the length or invasiveness of the surgical procedure rather than patient-centered, evidence-based data on implant versus autologous surgical recovery. This deficit in information on patient recovery comes at a time when real-time digital devices are used to track vitals, sleep-wake cycles, and steps taken for personal convenience without being used to guide treatment. The primary objective of this paper is to present a system framework for modeling the surgical recovery process. We also outline preliminary results from a pilot study with two breast cancer patients who each underwent one of the two reconstruction surgeries.