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At first, it was Pac-Man. Literally.
Pac-Man that gobbled up antibiotic complications.
When Madhu Nzerem (GSAS ’26) and Dr. Khanh Pham first pitched their idea at a Cornell hackathon, they called it PACMAN-EHR, short for Preventing Antibiotic Complications in Electronic Health Records. Clips of the arcade game played in the background, but the problem they wanted to solve was serious: antibiotic prescribing errors are common in hospitals, even among experienced physicians, and can put patients at risk.
“During my daily work treating patients with infectious diseases, I realized there was a need for an AI-powered assistant to act as a safety net for antibiotic prescribing,” said Pham, the company’s CEO, a physician at NewYork-Presbyterian, and an assistant professor at Weill Cornell Medicine.
Now rebranded as StewardGuard, the tool has evolved into an EHR-integrated clinical decision support system designed to reduce errors in antibiotic use. With AI assistance, it automates the manual review process by extracting key patient data from electronic health records, cross-referencing it with treatment guidelines, and providing real-time recommendations for safe, empiric antibiotic options.
“Physicians are strained for time and pulled in every direction,” said Nzerem, the company’s CTO and a PhD candidate in computational biology at NYU Langone. “StewardGuard eliminates the need to dig through patient records, especially in those first critical moments of care.”
Because antibiotic decisions can directly affect patient safety, StewardGuard is also designed to limit the risks associated with AI through what the founders call “a two-tiered architecture”. Most of its logic is hard-coded based on clinical guidelines, while large language models are employed selectively to validate inputs.
“Hallucination risk is real, especially in a clinical setting,” said Nzerem.
In early tests with more than a dozen simulated inpatient cases, StewardGuard made antibiotic decisions 33 times faster than clinicians — with 100% adherence to established guidelines.
However, running these large models locally requires GPU infrastructure that many hospitals lack.
To address this, the team is testing smaller models and exploring partnerships with OpenAI and other cloud providers to find a balance between performance and data security.
Alongside all these technical development, StewardGuard has taken steps to protect its intellectual property. Before joining NYU’s Summer Launchpad accelerator, the team drafted and submitted a provisional utility patent through Cornell’s technology transfer office to secure its proprietary code and clinical workflow. They hope to finalize the filing by the end of the summer, marking an important step toward commercialization.
With support from Leslie eLab, StewardGuard is now in discussions with NYU Langone and Weill Cornell to prepare for potential in-hospital trials — a critical phase in their broader goal of deploying StewardGuard in community hospitals, where physicians often lack access to infectious disease specialists.
“They have the bigger ‘hair-on-fire’ problem,” Pham said. “We’re building this for the places that need it most.”
