University of Notre Dame researchers, in collaboration with the Hospital Infantil de México Federico Gómez (HIMFG), are utilizing artificial intelligence (AI) to monitor chemotherapy complications in pediatric cancer treatment. This project aims to address the critical gap in post-treatment care for children battling cancer in Mexico, where it is the second leading cause of death among this demographic. The goal is to develop an AI model that can track chemotherapy complications and provide insights for caregivers and medical professionals. By digitizing clinical data and incorporating social determinants of health, physicians can have more effective patient management.
The AI model focuses on assessing pediatric patients who develop neutropenia after chemotherapy, which is characterized by a lower-than-normal white blood cell count. This condition increases the risk of infection in approximately half of pediatric patients. The AI model predicts the likelihood of adverse outcomes such as infection, internal bleeding, and mortality for patients with neutropenia. This predictive capability optimizes resource allocation in hospitals, especially considering space and bed constraints.
To ensure accurate predictions and actionable insights, the model needs comprehensive and accurate data. However, the Mexican healthcare system still relies on paper records, posing a challenge. The Lucy Family Institute developed a data collection app to bridge this gap. As the project progresses, researchers gather a comprehensive overview of patients’ oncology journeys, including background information, healthcare access determinants, oncology service utilization, emergencies, hospitalizations, and infection history. Despite the learning curve for hospital staff and caregivers, 264 families have already been enrolled in the pilot study.
The ultimate goal of the project is to provide a holistic understanding of the risks and variables post-chemotherapy, considering not only the patients but also their family and community context. This approach guides discharge decisions and improves patient outcomes. Additionally, the collected data serves as a surrogate electronic medical records system, benefitting patients in tangible ways.
The researchers are embarking on a prospective study with HIMFG to integrate social determinants of health data. This expansion aims to refine data collection processes and develop more comprehensive AI models to offer further insights for physicians’ decision-making. The findings of the project were published in the Journal of the Pediatric Infectious Diseases Society.
This project highlights the potential of technology-driven solutions to enhance healthcare delivery, particularly for underserved populations. As the researchers continue their work, they contribute to improving pediatric cancer treatment and post-treatment care, ultimately saving lives.
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