This proposal envisions a comprehensive plan for the career development of Dr. Ethan Basch in cancer outcomes research. Through mentored research, coursework, and independent study, Dr. Basch will develop collaborative relationships and methodologic skills to support his research agenda in clinical informatics and patient symptom management. This work focuses on the use of novel technologies to improve symptom monitoring during cancer care and trials, specifically through the evaluation of patient- centered treatment models which integrate patient reported outcomes (PROs). Currently, it is not standard to use patients' own self-reported symptom information to monitor cancer care. Rather, clinicians are relied upon to elicit, filter and report this information - an approach which is mandated in NCI and industry-sponsored trials. This stands in contrast to quality of life or symptom research, in which PROs are standard. The overall objective of this research program is to explore the application of patient self-reporting to symptom monitoring in oncology treatment, as a possible new paradigm.
The specific aims are: 1) to assess the feasibility of routinely collecting symptom PROs via the web during cancer care and trials; 2) to measure the relationship between patient and clinician symptom reporting using the standard U.S. instrument for this purpose, the NCI's Common Terminology Criteria for Adverse Events (CTCAE); and 3) to evaluate the impact of patient self-reporting on clinical and administrative outcomes. This agenda will be accomplished through a series of IRB-approved studies Dr. Basch has designed and is systematically conducting at Memorial Sloan-Kettering Cancer Center, as well as nested in multicenter NCI and industry-sponsored clinical trials.
Patient self-reporting may increase the efficiency of clinical operations, foster early detection of serious adverse events, and improve patient satisfaction. Engaging patients as active participants in research and in their own care conveys the message that treatment tolerance is an important endpoint. This work will help determine how new technologies may be deployed to improve traditional models of care.
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