Utah's Unique Data Powerhouse
Utah's Unique Data Powerhouses
September 8, 2025
1 pm - 2:30 pm
Opening Remarks
Lightning Talks: 3 to 5 minutes
Lightning Talks
- Madsen, R. (2025). Real World Data to Evidence: An Alternative. (Presenting for Ram Gouripeddi)
- Amburgey, V. (2025). Identifying Fit-For-Purpose Data Resources for Translational Research.
- Alexander, K., Golden, M. (2025). The Hive: The University of Utah's Research Data Repository.
- Madsen, R. (2025). CDIS: Clinical Data Interoperability Services.
- Miller, M. (2025). Utah Cancer Registry: Utah's Population-Based Source of Data on Cancers Diagnosed in Utah.
- Ramshaw, M. (2025). Center for High Performance Computing.
- Willburn, J. (2025). Intelvia: Patient Blood Management (PBM) Insights Improving Patient Outcomes.
- Hollingshaus, M. (2025). Utah Caregiving Population Science (C-PopS).
- Jones, L. (2025). Health Research Opportunities in the WERDC.
- Deshmukh, V. (2025). Data Science Services and Enterprise Data Warehouse.
- Samore, M. (2025). What makes VA Data Uniquely Powerful.
Impact - HSR
Inetegrating Medicine and Policy to Achieve Healthcare Transformation
• New SOM program to support late translational (T3/T4), health services research (HSR), and outcomes research across the School of Medicine
Goals:
Build late translational research community across departments
Provide support, mentorship and collaboration
Strengthen recruitment/retention
Elevate visibility of HSR at The University of Utah SOM
SFESOM Impact HSR Kickoff Symposium
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Exposure Health Data & Methods
Ram Gouripeddi
Content
Integrating Exposure with Clinical Data
Exposure Health Informatics Ecosystem
Data Acquisition Pipeline
- Hardware and software wireless networking, and protocols to support easy system deployment for robust sensor data collection in homes, and monitoring of sensor deployments.
Participant Facing Tools
- Annotate participant generated data, display sensor data, and inform participants of their clinical and environmental status.
Computational Modeling & Uncertainty Characterization
- Generate high resolution spatio-temporal data in the absence of measurements as well as for recognition of activity signatures from sensor measurements.
- Characterize uncertainties associated with collected or computed data.
Central Big Data Integration Platform
- Standards-based, open-access infrastructure that integrates study-specific and open sensor and computationally modeled data with biomedical information along with characterizing uncertainties associated with these data
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