Abstract
e18515 Background: Many health systems seek the ability to share and learn from real-world evidence, or aggregated cancer patient data, to improve patient outcomes. Although precision medicine initiatives are becoming more commonplace at large health systems, there is limited data defining the attitudes of health system leaders on their views of precision medicine. To this end, the Health Management Academy conducted a survey of oncology leaders to explore their views on the application of real-world evidence in clinical oncology. Methods: In August 2017, the Health Management Academy surveyed leaders from 43 leading health systems regarding their awareness, integration, and operationalization of precision medicine. The survey included 48 questions that assessed the state of precision medicine, challenges with clinical decision making, and reimbursement policies. Four questions addressed data sharing and impact of real-world evidence on clinical decision making. Health system leaders were contacted via email. Results: We obtained a response rate of 49% (21/43). Respondents included Chief Medical Officers (CMOs) and oncology leaders representing 21 health systems, 296 hospitals, and approximately 2.9 million admissions annually. 80% of respondents expect real-world outcomes from aggregated, de-identified data to be “extremely important” in guiding physician decision making in complex cases. 63% of respondents considered providing oncologists with clinical guidance for molecular diagnostics and targeted therapies “extremely important”. 60% of responding health systems are involved in a cancer data sharing collaboration, most commonly the Oncology Precision Network (44%). Most (73%) respondents would find a tool that compared real-world outcomes across treatment regimens for patients with any combination of clinical and molecular characteristics “extremely valuable”. Conclusions: Real-world evidence to improve patient care remains a critical priority among health system leaders. Furthermore, these leaders recognize that decision support tools will be required to make this data actionable for clinicians at their individual points of care.
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