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X-WR-CALDESC:Events for Stanford - Department of Biomedical Data Science
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DTSTART;TZID=America/Los_Angeles:20241115T110000
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SUMMARY:CCSB Seminar Series
DESCRIPTION:Third Friday of the month\nRegular Time (Feb-Dec):  11:00 AM – 12:00 PM\nLocation:  Clark Center\, S360 (behind the coffee shop)
URL:https://dbds.stanford.edu/event/ccsb-seminar-series-8/
LOCATION:Clark Center S360
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DTSTART;TZID=America/Los_Angeles:20241115T110000
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SUMMARY:Stanford CCSB Seminar Series: Aaron M. Newman\, 11/15
DESCRIPTION:
URL:https://dbds.stanford.edu/event/stanford-ccsb-seminar-series-aaron-m-newman-11-15/
LOCATION:Clark Center\, S360\, 3rd floor
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DTSTART;TZID=America/Los_Angeles:20241118T121500
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SUMMARY:Monday Student Talks: Speakers Bryan Bunning and Yixing Jiang
DESCRIPTION:DBDS Student Talks (BIOMEDIN 201) Autumn Quarter 2024-25  \n11/18/24 \n12:15-12:45: Bryan Bunning \n12:45-1:15: Yixing Jiang \nLK 120/or Zoom Link: https://stanford.zoom.us/j/97747187137?pwd=QJqAPX1yRpGOwTTGPsIIwEYp7W2Aaz.1&from=addon\nPassword: 180750 \nTitle: A Micro-Randomized Trial Design of Remote Patient Monitoring in Pediatric Type 1 Diabetes \nAbstract: At Stanford Children’s Hospital\, the standard-of-care for Type 1 Diabetes (T1D) in children now includes continuous glucose monitors (CGMs) and AI-enabled remote patient monitoring (RPM) to track blood sugar levels in real time. This initiative\, known as the Teamwork\, Targets\, Technology\, and Tight Control (4T) Program\, aggregates patient glucose data\, sends it to the cloud for analysis\, and presents it to the care team via a dashboard to support timely insulin adjustments and patient triage. \nIn this study\, we designed a micro-randomized trial embedded within the 4T Program for newly diagnosed pediatric T1D patients. The intervention involves increasing the RPM frequency for a subpopulation of patients who are not meeting their glucose targets. Through simulations\, we assessed the impact of factors such as study duration\, sample size\, and clinic capacity. Clinic capacity\, ie the working capacity for the staff to act and provide care from the remote data\, is highlighted. This simulation-based approach provides a practical framework for designing effective RPM studies under real-world constraints. This trial may offer evidence to inform RPM billing policy standards in diabetes. \n  \nYixing Jiang\, 3rd Year PhD Student \n(12:45pm-1:15pm) \nTitle: SmartAlert: Integrating Machine Learning and Alert Triggers into Live Electronic Medical Record Systems\, Targeting Low-Yield Inpatient Lab Tests \nAbstract: This study explores integrating machine learning into electronic medical record systems to predict stability of inpatient lab tests. A ‘smart alerts’ system was developed and tested at Stanford Hospital. The system identifies stable lab results\, advising clinicians on test ordering. Live deployment showed desired precision at good recall in predicting test result stability\, with suggestions for system optimization identified. This approach may significantly decrease low-yield testing and enhance personalized clinical decision-making. \n 
URL:https://dbds.stanford.edu/event/monday-student-talks-speakers-bryan-bunning-and-yixing-jiang/
LOCATION:LK 120
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