We are excited about our current body of work and want to share project insights. For more information, questions about our work, or to share ideas, please contact us.
The Lab Projects
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Using Interventional Informatics to Address Social Determinants of Health During Clinical Care Visits to Promote Behavior Change and Prevent Type 2 Diabetes
This project aimed to integrate a diabetic clinical decision aid into the BJC electronic medical record system and deploy it into a single clinical area.
Project Lead: Maura Kepper
- Big Ideas
completed
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Machine Learning Risk Predictions at OR-ICU Handoffs
This project assessed post-operative risks to patients and incorporates those risks into nursing handoff reports all through the use of machine learning precision models.
Project Lead: Joanna Abraham
- Big Ideas
completed
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Closing the Loop on Unexpected Imaging Findings: Augmenting the Process with Artificial Intelligence and Deep Learning (CLU)
Using natural language processing, AI, and deep learning, this project aims to automatically identify follow-up recommendations on radiology reports to improve radiology workflows.
Project Lead: Andrew Bierhals and Aris Sotiras
- Big Ideas
completed
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Designing a Human-Centered Early-Warning System for Oncology Inpatients
Using clinician information and principles of Human-Centered Design, the Big Ideas team conducted rapid-cycle prototype testing to create a predictive model to act as an early-warning system for oncology inpatients.
Project Lead: Patrick Lyons
- Big Ideas
completed
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Enhancing Palliative Care with Machine Learning
To enhance palliative care, the Big Ideas team developed a machine learning algorithm using electronic medical records data to identify patients at a high risk of mortality within one to six months.
Project Lead: Nathan Moore
- Big Ideas
completed
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High-Resolution Trajectory Mapping of Post-Discharge Knee Replacement Patients (TRIPP: Trajectories in Predictive Methods and Alerts to Prevent Asthma)
The goal of this project was to determine if particulate size or exposure levels correlate with asthma symptoms in knee replacement patients, and if there is a lag time from exposure to onset of symptoms.
Project Lead: Joe Steensma
- Big Ideas
completed
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Biosensors and Machine Learning Algorithms to Detect Complications in Post-Pancreatectomy Patients
The goal of this project was to correlate surgical outcomes with patient pre-operative activity levels using a wearable activity monitor.
Project Lead: Chet Hammill
- Big Ideas
completed
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Programming the Future of Sepsis: Developing a Pragmatic, Real-Time Sepsis Prediction Model for Non-ICU Patients
This project worked to augment and optimize sepsis prediction algorithms and enable a sustainable and continuous EHR interface in order to develop a real-time prediction sepsis model.
Project Lead: Andrew Michelson
- Big Ideas
completed
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Voice User Interface Connecting Clinician with Supply Inventory Management via Alexa
In this project, the Big Ideas team piloted a voice assistant in two in-patient wards, which allows clinicians to order in-patient clinical supplies via voice command.
Project Lead: Marilyn Schallom, Po-Yin Yen
- Big Ideas
completed
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Conversational Agents for Managing Perioperative Psychological Stress
This project developed a smartwatch application to detect and help manage perioperative psychological stress through text-based mindfulness exercises.
Project Lead: Thomas Kannampallil
- Big Ideas
completed
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Sleep Preservation for the Inpatient Setting
With a goal to improve the patient experience and in-hospital sleep, the Lab is working to further understand the perceived patient experience of sleeping in a hospital.
Project Lead: Kayla Paynter
- Strategic Innovation
active
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Pressure Ulcer Prevention
The Lab is working to structure a pilot of a sensor system at BJC HealthCare, which uses a wearable device to measure turn frequency, turn angle, and tissue recovery time in an effort to prevent pressure ulcers.
Project Lead: Lindsay Lay
- Strategic Innovation
active
