||5R01CA214829-03 Interpret this number
||University Of Michigan At Ann Arbor
||The Lifecycle of Health Data: Polices and Practices
The proposed project seeks to identify concrete policies and practices that can better serve both people and
institutions in building local, state, and national health information systems necessary to maximize the potential
of data throughout its life cycle. We will focus on 5 intertwined and endemic issues to the life cycle of data that
arise in precision oncology: (1) informed consent, (2) duration of specimen storage; (3) storage of
germline DNA sequence data (4) disclosed commercialization; and (5) data sharing at local, state, and
national levels. Our proposed research will identify public preferences for specific policies and practices
governing these issues thus addressing a major gap in understanding how the public views data as it flows
across functional boundaries – clinical care, quality improvement, research and public health - and across
local, state, and national levels. Our interdisciplinary research team with expertise in policy, learning health
systems, ethics, precision oncology, and public health genomics has partnered with an expert advisory team
that spans these levels and boundaries. Specifically, at the local level we are engaging the University of
Michigan Health Systems Institutional Review Board (IRB), Comprehensive Cancer Center, Central
Biorepository, Compliance Office, and precision oncology researchers (MI-ONCOSEQ). At the state level, we
are interacting with the Michigan Health Information Network (MiHIN) that coordinates 10 health exchanges
with the Michigan Department of Health and Human Services. At the national level, we are engaging with the
multi-state PCORI-funded LHSnet, the American Society of Clinical Oncology’s CancerLINQ, which will
combine data across practices for quality improvement. Using an explanatory sequential design, we will
investigate the public’s knowledge, attitudes, and acceptance of data policies and practices using case studies
that illustrate the life cycle of data in precision oncology (n=3,500) (Aim 1) and conduct deliberative sessions to
identify recommendations for changes in institutional practices/policies (n=225) (Aim 2). We will then
quantitatively assess whether these recommendations ameliorate concerns and identify optimal policies
through conjoint analysis using a longitudinal follow-up survey conducted at the state and national scale
(n=2,500) (Aim 3).
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