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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Remote Patient Monitoring (RPM) for Primary Care
The goal of RPM for Primary Care is to decrease inpatient admission and readmission rates for the high-risk population, and potentially allow us to discharge patients earlier or monitor more patients from home who would have otherwise been admitted.
Project Lead: Kayla Paynter
- Strategic Innovation
completed
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Atrial Fibrillation Dashboard
The goal of this project was to create a comprehensive dashboard for informed outpatient care for patients who are diagnosed with Atrial Fibrillation.
Project Lead: Krissy Mikhova, MD
- Innovation Trainee
completed
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Clinical Phenotypes of Viral Pneumonia: A Comparative Study Between SARS-CoV-2 and Influenza
This project, which is part of the COVID-19 Big Ideas cohort, aimed to study the differences in clinical characteristics between COVID-19 and Influenza-A-related pneumonia.
Project Lead: Pratik Sinha
- Big Ideas
completed
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Care Companion for Surgical Prehabilitation
The Lab is developing a program to help patients better prepare for surgery through the use of various tools available in the market, including wearable devices.
Project Lead: Julie Lenhardt
- Strategic Innovation
active
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Oncology Deterioration Early Warning Signs
This project focuses on preventing deterioration of hospitalized oncology patients. Providers will access an early warning system dashboard to detect those who are high risk.
Project Lead: Patrick Lyons, MD, MSc
- Epic Predictive Analytics
active
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Post-Operative Prediction
The goal of this project is to prevent death and post-operative complications, including acute kidney injury, after surgery using a prediction tool.
Project Lead: Bradley Fritz, MD, MSCI
- Epic Predictive Analytics
active
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Sepsis Prediction Tool
The Sepsis Prediction tool allows Epic to highlight patients who are at risk for sepsis, which leads to a more rapid clinical intervention and reduced adverse outcomes.
Project Lead: Andrew Michelson, MD
- Epic Predictive Analytics
active
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Machine Learning for Post-Operative Care
The goal of this project is to utilize machine-based learning in Epic to assist with the reduction of adverse outcomes in post-operative patients.
Project Lead: Christopher Ryan King, MD, PhD
- Epic Predictive Analytics
active
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Palliative Care Identification
The Lab aims to improve patient referrals for palliative care services using predictive models such as Epic Predictive Analytics and analyzing patient data while they are inpatient.
Project Lead: Nathan Moore, MD
- Epic Predictive Analytics
active
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Improving Diagnosis of Superimposed Preeclampsia in Pregnant Patients: A Wearable Device for Non-Invasive Continuous Monitoring of Fluid Dynamics
The team aims to use wearable devices to monitor and define the differences in third trimester fluid volume of patients with superimposed preeclampsia and those with chronic hypertension.
Project Lead: Valene Garr Barry and Ebony Carter
- Big Ideas
active
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Intelligent Clinical Decision Support for Perioperative Blood Management
By using an iterative, user-centered design and evaluation framework, the team is designing and assessing the usability and safety of the Surgical Personalized Anticipation of Transfusion Hazard (S-PATH) system.
Project Lead: Sunny Lou
- Big Ideas
active
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Remote Blood Pressure Monitoring
This project is designing a digital medicine program for remote blood pressure monitoring to increase patient engagement and trust in BJC. It is aiming for a 20% reduction in provider-reported administrative workload.
Project Lead: Jennifer Schmidt
- Big Ideas
active
