Three new initiatives at the Penn Medicine Center for Health Care Innovation were recently funded in the second round of their Innovation Grant Program. The program is designed to advance promising ideas and to promote exploration in the areas of connected and employee health. Each winner either an employee or a student at the Penn Medical Center received $5,000 and $75,000 to further develop and test their ideas.
In one research study, Angela R. Bradbury, MD Assistant Professor of Hematology-Oncology in the Abramson Cancer Center, will use telemedicine to increase access to genetic testing and counseling services. Today, genetic testing for cancer susceptibility requires the increasing need for genetic counseling specialists to assist in caring for patients and their families.
Genetic testing needs to be conducted along with proper pre- and post-test counseling to contexualize the test and outline what the results mean. As genomic applications in oncology expand, the demand for genetic expertise will increase and gaps in delivery are expected to worsen.
Through an NIH-funded study, Bradbury and her team showed how telemedicine can be used to effectively expand genetic services to populations with limited or no access to care. The award for the new project will help transition the team’s research-supported telemedicine program to a sustainable clinical model.
A second award went to a team led by Brian Litt, MD, Professor in Neurology & Bioengineering to build an automated cloud-based platform for Intensive Care Unit (ICU) Electroencephalogram (EEG) interpretation.
Today, patients are monitored continuously with EEGs in ICUs worldwide. Recent studies show that a large percentage of ICU patients have seizures, brain ischemia, encephalopathy, or other conditions that can be detected early on an EEG to allow therapy to be initiated promptly.
However, continuous long term EEG monitoring currently must be interpreted manually by physicians thereby delaying the delivery of results to the caregivers that have to rely on written reports. This method for delivering the information inhibits the ability to view trends over time or forecast when a patient’s condition may deteriorate.
This research project aims to build an automated cloud-based system for interpreting long term ICU EEG data to speed response to changes in patients’ conditions and to improve patient outcomes.
Ian Bennett, MD, PhD, Associate Professor of Family Medicine & Community Health is the team leader for the third research project. This initiative will use text messages to engage and educate patients thereby enabling early interventions to reduce poor pregnancy outcomes.
Low income women have high rates of poor pregnancy outcomes, including low birth weight, preterm birth, and preeclampsia. Delays in identifying these disorders can result in poor perinatal outcomes. This research project will work on creating an app to deliver information regarding signs and symptoms of adverse pregnancy conditions to at risk women via text messages.