Study published by The Joint Commission Journal of Quality and Patient Safety finds ~70% of MedAware’s AI-enabled clinical interventions would not have been generated by existing tools
MedAware (www.medaware.com), a developer of AI-based patient safety solutions, today announced the publication of a study by The Joint Commission Journal on Quality and Patient Safety, validating both the significant clinical impact and anticipated ROI of MedAware’s machine learning-enabled clinical decision support platform designed to prevent medication-related errors and risks.
The study analyzed MedAware’s clinical relevance and accuracy and estimated the platform’s direct cost savings for adverse events potentially prevented in Massachusetts General and Brigham and Women’s Hospitals’ outpatient clinics. If the system had been operational, the estimated direct cost savings of the avoidable adverse events would have been more than $1.3 million when extrapolating the study’s findings to the full patient population.
“This study shows that MedAware’s system performed well in identifying important medication-related errors in the ambulatory setting, and that implementing it could result in substantial cost savings. MedAware’s application enables systems to catch errors they didn’t know they had and which would not have been caught using existing systems—these can be very serious and have major consequences,” explained Dr. David Bates, MD, study co-author, Professor at Harvard Medical School, and Director of the Center for Patient Safety Research & Practice at Brigham and Women’s Hospital. “Because it is not rule-based, MedAware represents a paradigm shift in medication-related risk mitigation and an innovative approach to improving patient safety.”
The study analyzed outpatient data from two academic medical centers, and over the course of the study, MedAware flagged 10,668 potential errors and adverse drug events in retrospect on 373,992 patients used for this study. A random sample of 300 warnings were selected for medical record review. From this sample, the research team found that:
- 92% of the warnings generated were accurate based on data available
- 79.7% of the warnings were considered clinically valid
- 68.2% of MedAware’s warnings would not have been flagged by existing decision support systems
- $1.3 million would have been saved in direct healthcare-related costs when extrapolating study findings to the full patient population
“We have entered an era of real-world application of machine learning in our health system. However, many technologies have yet to prove ROI for hospital systems,” said Dr. Ronen Rozenblum, PhD, MPH, Assistant Professor, Harvard Medical School, Director of Business Development for Patient Safety Research and Practice at Brigham and Women’s Hospital and the study’s lead author. “MedAware offers both measurable improvement in patient safety and significant potential cost savings to hospitals at a time when healthcare systems must find every opportunity to drive efficiencies from a financial perspective.”
The cost associated with medical error are a major issue for the U.S. health care system. A leading source of harm, according to study authors, are prescription drug errors, which result in “substantial morbidity, mortality and excess health care costs estimated at more than $20 billion annually in the United States.”
“Our mission has always been simple: Improve patient safety by preventing avoidable medication-related errors and adverse drug events. We provide an essential layer of safety for providers and their patients,” said Dr. Gidi Stein, co-founder and CEO of MedAware. “This study not only further validates our platform’s efficacy and impact on patient safety, but also proves our considerable benefit to a hospital’s bottom line as well.”