Global Digi-Health Monitor: Calendar Week 4, 2025
Healthcare Expert: EHRs Are Not the Root Cause of Medical Practice Problems
In an October 2024 Forbes article, Dr. Spencer Dorn argues that electronic health records (EHRs) have become an oversimplified scapegoat for healthcare’s deeper issues. While acknowledging that EHRs contribute to clinician burnout (requiring nearly 6 hours of use per 8-hour shift), he points out that the real challenges stem from increasing healthcare complexity, including patient needs (40% of American adults have multiple chronic conditions), complex clinical protocols, and administrative requirements. Dorn emphasizes that organizations using the same EHR system can have vastly different experiences based on their workflow design and implementation. Rather than blaming technology, he advocates for focusing on systemic improvements like care system redesign, workflow streamlining, and team optimization.
Case Western Receives $4M to Develop AI Model for Predicting Heart Disease from CT Scans
Case Western Reserve University researchers have received $4 million in NIH grants to develop an AI model that analyzes calcium-scoring CT scans to predict cardiovascular events and heart failure. The project, in collaboration with University Hospitals and Houston Methodist, will examine multiple factors from CT scans, including plaque buildup, aorta condition, heart shape, and body composition, along with clinical and demographic data. The goal is to create a non-invasive, cost-effective way to identify high-risk patients earlier and more accurately, potentially transforming cardiovascular disease prevention. Currently, cardiovascular disease causes over 17 million deaths annually worldwide.
Global Study Identifies 300 New Genetic Risk Factors for Depression Through Diverse Population Sample
A groundbreaking international study led by the University of Edinburgh and King’s College London has discovered 300 previously unknown genetic risk factors for depression by analyzing data from over 5 million people across 29 countries, with 25% being of non-European ancestry. The research, published in Cell, identified a total of 700 genetic variations linked to depression, with 100 of the new variants specifically found due to the inclusion of African, East Asian, Hispanic, and South Asian participants. The study also revealed potential new treatment options, identifying existing drugs like Pregabalin and Modafinil that affect depression-linked genes. This more diverse approach to genetic research could lead to better prediction of depression risk across ethnicities and more inclusive treatment options.
AI Model Detects Brain Cancer Spread with 85% Accuracy Through MRI Analysis
McGill University researchers have developed an AI model that can detect metastatic brain cancer spread using only MRI scans, potentially eliminating the need for invasive surgery in some cases. The model was tested on over 130 patients at The Neuro (Montreal Neurological Institute-Hospital) and achieved 85% accuracy in detecting cancer cells in surrounding brain tissue. The AI can identify subtle patterns that traditional imaging methods might miss, offering a promising non-invasive alternative for patients who may not be candidates for surgery. The research team plans to expand their study with larger datasets to refine the model for clinical use.
Cerebras Systems and Mayo Clinic Develop AI Model to Predict Arthritis Treatment Success
Cerebras Systems and Mayo Clinic have collaborated to create a genomic foundation model that can predict which rheumatoid arthritis treatments will work best for individual patients, with 87% accuracy. The model, which is 10 times larger than AlphaFold and trained on a trillion tokens, uses multiple nucleotide analysis to improve prediction accuracy. The system also shows promise for cancer (96% accuracy) and cardiovascular disease (83% accuracy) predictions. The model was trained using Mayo Clinic’s patient exome data combined with public genome data, demonstrating how AI can potentially transform personalized medicine by reducing the trial-and-error approach to treatment selection. Additionally, Mayo Clinic is working with Microsoft Research on a separate AI initiative to improve chest X-ray analysis.
