Controlled Substance Monitoring Visualization

Project Summary

Over ten weeks, Biology major Jacob Sumner and Neuroscience major Julianna Zhang joined forces with Biostatistics Masters student Jing Lyu to analyze potential drug diversion in the Duke Medical Center. Early detection of drug diversion assists health care providers in helping patients recover from their condition, as well as mitigate the effects on any patients under their care.

Themes and Categories
Year
2017
Contact
Ashlee Valente
Center for Applied Genomics and Precision Medicine
ashlee.valente@duke.edu

Project Results: The team did an extensive analysis of 300,000 Omnicell transactions obtained by the Duke Medical Center pharmacy. They created interactive dashboards of drug usage, which can be broken down by practice type, drug type, time granularity (day, week, month), and/or specific user. They had the opportunity to present their findings to Duke's Provost and to senior leadership within Duke Hospital and the Duke Clinical Research Institute

Partially funded by Duke Anesthesiology

Click here for the Executive Summary

Faculty Lead: Rebecca Schroeder

Project Manager: Willem van den Boom

"It changed my perception of how a large variety of skill sets can work together to solve a data science problem." — Willem van den Boom, Project Manager and PhD student, Duke Department of Statistical Science

Related People

Related Projects

A large and growing trove of patient, clinical, and organizational data is collected as a part of the “Help Desk” program at Durham’s Lincoln Community Health Center. Help Desk is a group of student volunteers who connect with patients over the phone and help them navigate to community resources (like food assistance programs, legal aid, or employment centers). Data-driven approaches to identifying service gaps, understanding the patient population, and uncovering unseen trends are important for improving patient health and advocating for the necessity of these resources. Disparities in food security, economic stability, education, neighborhood and physical environment, community and social context, and access to the healthcare system are crucial social determinants of health, which studies indicate account for nearly 70% of all health outcomes.

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here: