AI-Powered CDSS Enhances Patient Safety with Real-World Data

The growing demand for personalized medicine has accelerated the adoption of Real-World Data (RWD) in healthcare. The Tungs' Taichung MetroHarbor Hospital in Taiwan, in collaboration with AESOP Technology, conducted research on an AI-driven Clinical Decision Support System (CDSS) that leverages RWD to enable safer and more effective clinical decision-making, significantly reducing the risk of potentially inappropriate medications. The findings were published in the Journal of Medical Internet Research recently.

RWD encompasses various data types, including electronic health records (EHR), insurance claims, wearable devices, environmental factors, and social determinants of health. It offers a comprehensive view of patient conditions and treatment outcomes. However, effectively utilizing RWD remains a significant challenge.

As a key component of RWD, the EHR system is often constrained by the poor design of traditional CDSS. These systems frequently fire irrelevant or low-priority alerts and fail to provide specific recommendations for complex scenarios, such as off-label drug use, multimorbidity, and polypharmacy. This results in alert fatigue among physicians, causing critical alerts and reminders to be overlooked. Consequently, the completeness and accuracy of medical records are compromised, increasing the risk of inappropriate diagnoses or treatments and potentially threatening patient safety.

This study addresses these challenges with an integrated AI-powered CDSS that combines MedGuard (now called RxPrime) for prescription appropriateness and DxPrime for diagnostic recommendations. By analyzing 438,558 prescriptions during a year-long trial, the system delivered 10,006 actionable recommendations, achieving a nearly 60% acceptance rate by physicians. Compared to traditional systems, this AI-enhanced approach demonstrated superior precision and practical applicability in real-world clinical settings.

The results also revealed high acceptance rates in specialties such as ophthalmology (96.59%) and obstetrics/gynecology (90.01%), indicating strong applicability. In contrast, lower acceptance rates in neurology (38.54%) and hematology-oncology (10.94%) underscore the need for specialty-specific customization to address diverse clinical demands.

This research highlights the transformative potential of RWD-driven AI systems with actionable recommendations to improve patient safety and support complex treatment decisions. These advancements foster greater trust and adoption of CDSS by physicians. Furthermore, by enhancing the completeness and accuracy of medical records, these systems elevate the quality of RWD, fostering a positive feedback loop that drives future medical advancements and consistently provides a reliable foundation for data-driven healthcare.

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