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Title page for ETD etd-11112016-144712

Type of Document Master's Thesis
Author Cheng, Alex Chih-Ray
URN etd-11112016-144712
Title Quantifying Burden of Treatment for Breast Cancer Patients from Clinical Encounter Data
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Mia Levy Committee Chair
Daniel Fabbri Committee Member
Laurie Novak Committee Member
  • treatment burden
  • electronic medical record
Date of Defense 2016-11-07
Availability unrestricted
Breast cancer patients suffer from the symptoms of their illness as well as from burden of treatment imposed by their care. Patients with high levels of burden tend to be less compliant with treatment plans resulting in worsening outcomes. To address the problem of overburden, some providers have proposed practicing minimally disruptive medicine, where treatment plans are tailored to the patients’ capacity to handle the care. While some researchers have developed surveys that identify and quantify factors that contribute to treatment burden, no studies have used the electronic health record to assess patient burden. We developed measures derived from outpatient and inpatient encounter data that included time spent in appointments, waiting time, unique appointment days, and total inpatient length of stay. We used these measures to differentiate burden of treatment in early stage breast cancer patients in the first eighteen months after diagnosis. This method allowed us to identify outliers and to characterize the pattern of treatment over time. Our measures could also be used to evaluate new therapeutic and operational interventions for their effect on treatment burden. In patients receiving chemotherapy at Vanderbilt, a non-inferior change in protocol successfully reduced treatment burden while a therapeutically superior treatment may have imposed an increase of burden on patients. As the complexity of healthcare increases and patients take on more responsibility to manage their care, understanding treatment burden is critical to helping providers prescribe care right-sized for the patient to improve compliance and clinical outcomes.
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