AESOP Technology Unveils World's First Machine Learning Model to Combat Wrong-Site Surgery

Wrong-site surgery (WSS), a critical "Never Event," represents a failure that should never occur in healthcare. Yet, due to underreporting, the true prevalence of these incidents remains obscured, jeopardizing patient safety and healthcare management. AESOP Technology, a medical AI startup, has developed an innovative solution: the Association Outlier Pattern (AOP) machine learning model. This model offers real-time decision support and retrospective analysis, aimed at enhancing surgical safety and care quality.

According to the World Health Organization's (WHO) 2024 Patient Safety Report, a mere 38% of countries have established reporting systems for never events. In the United States, the Joint Commission documented 112 surgical errors in 2023, with wrong-site surgeries comprising 62% of these incidents. The absence of comprehensive reporting hinders the healthcare system's ability to gauge the issue's magnitude and implement effective preventative measures.

Inconsistent documentation is one of the major contributors to WSS. To address this, AESOP utilized data from the Centers for Medicare & Medicaid Services Limited Data Set (2017–2020), examining discrepancies in surgical laterality. This analysis informed the creation of the AOP model—the first of its kind dedicated to addressing WSS.

Unlike traditional rule-based systems that merely verify consistency, the AOP model analyzes intricate patterns between diagnoses and surgeries. It excels in handling incomplete or ambiguous diagnostic data, achieving an accuracy rate of over 80% in identifying surgical errors, outperforming existing methods.

The AOP model empowers healthcare organizations to detect inconsistencies in medical records, identify unreported surgical errors, and enhance reporting mechanisms. This not only improves patient safety but also strengthens management systems for error prevention.

Beyond retrospective analysis, the AOP model offers real-time decision support during surgical planning. It automatically flags incorrect associations between surgical codes and diagnoses, ensuring accurate and complete records. This real-time capability reduces error risks, making the AOP model an essential tool for future electronic health record (EHR) systems.

"We are thrilled with the preliminary outcomes of our research and look forward to integrating these insights into DxPrime's patient safety features this year," said Jim Long, CEO of AESOP Technology. "Our advancements in automating surgery coding show great potential for helping physicians deliver safer care, reduce documentation time, and enable medical coders to perform better concurrent surgery coding and review when patients are still hospitalized."

Having demonstrated its efficacy in orthopedics, the AOP model holds promise for other specialties reliant on laterality, such as ophthalmology and otolaryngology. This expansion aligns with AESOP's commitment to advancing patient-centered AI solutions across diagnostics, medication safety, and now surgical safety—ushering in a new era of reliable and safer healthcare.

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