The Pitt Challenge
Pharmacy is a critical component of patient safety within health care—it is often the first line of defense against a dangerous medication error. For this reason, we were especially excited that the Pitt Challenge 2022 focused on the intersection between pharmacy and technology. The Pitt Challenge is a student-led hackathon at the University of Pittsburgh that is open to all college students. The hackathon tasked students with solving the most pressing issues facing pharmacy and healthcare today.
The Pitt Challenge 2022 had 320 registered participants and 23 final pitches on solutions to pharmacy challenge that spanned the spectrum of health care. Students picked from among 10 tracks, including topics such as health equity, big data and machine learning, increasing health literacy, virtual medical simulation, and patient safety. The Patient Safety Technology Challenge sponsored the Patient Safety Tech track, which received submissions from 10 teams. Teams were asked to create an application with a minimal viable product (a simplified product with just enough features to be usable and provide feedback for future product development) and create a video that highlights their projects. Tyler Hoffman, PharmD, a University of Pittsburgh Medical Center (UPMC) pharmacist, served as a mentor to help students think through the challenges of patient safety innovation. Hoffman had participated in the Pitt Challenge in previous years and was thrilled to be invited back as a mentor. Jieshi Chen, senior research analyst at Carnegie Mellon’s Robotic Institute, participated as a judge.
The Patient Safety Technology Challenge selected one winning team in the Patient Safety Tech track, who received a $500 prize. The winning team developed Pill Identifier, an application that allows users to upload an image of a pill and receive information on what medication the pill is most likely to be based on its physical characteristics, such as shape, size, and color. The team implemented a machine learning algorithm and a neural network model to allow the images to be compared against a dataset. The output includes information on the most likely drug and the predicted accuracy of the identification. This idea is a pioneering step toward ending medication error, a leading cause of adverse events in patient safety. The Pitt Challenge 2022 highlighted the essential role that pharmacy can play in spurring innovation in tech-enabled patient safety solutions.