The Penn Medicine Center for Health Care Innovation will fund three new initiatives in the second round of its Innovation Grant Program. The program encourages Penn employees and students to submit their ideas for advancing health and health care delivery. Winners receive funding and support from the Center for Health Care Innovation to facilitate the rapid translation of ideas into action and measurable outcomes over six months.
Fifty-six different ideas were submitted for review this spring. The winners include a cloud-based platform for ICU EEG monitoring and visualization of test results, a telemedicine effort to improve access to genetic testing and counseling services, and technology to improve prenatal services. Each winner will receive design support and between $5,000 and $75,000 in funding to further develop and test their idea.
“The innovation grant program allows us to help thought leaders across Penn Medicine accelerate programs and practices with the potential to make a meaningful difference in health care delivery,” said David Asch, MD, MBA, professor of medicine and executive director of the Penn Medicine Center for Health Care Innovation. “We were excited by the level of interest from our colleagues, and we are eager to begin work in June.”
Cloud-based platform for ICU EEG monitoring and visualizing results
A team led by Brian Litt, MD, a professor in Neurology & Bioengineering, will build an automated, cloud-based platform for Intensive Care Unit (ICU) electroencephalogram (EEG) interpretation.
Patients are monitored continuously with EEGs in ICUs worldwide. Recent studies show a large percentage of ICU patients have seizures, brain ischemia, encephalopathy, or other conditions that can be detected early on an EEG, allowing therapy to be initiated promptly.
However, continuous long-term EEG monitoring currently presents two major problems: it must be interpreted manually by physicians, delaying the delivery of results to the caregivers, and those caregivers rely on written reports from these studies, thus inhibiting the ability to view trends over time or forecast when a patient’s condition may deteriorate. The 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 improve patient outcomes.
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