Aesop Technology develops machine learning model to prevent wrong-site surgeries
AESOP Technology has created an innovative machine learning model to identify and prevent wrong-site surgeries. This Association Outlier Pattern (AOP) model analyses patterns between diagnoses and surgical procedures to flag inconsistencies, potentially reducing the occurrence of these critical “Never Events” that continue to challenge healthcare systems worldwide.
Addressing a persistent patient safety challenge
Wrong-site surgery (WSS) remains a significant patient safety concern despite established prevention protocols. The World Health Organisation’s 2024 Patient Safety Report indicates only 38% of countries have implemented reporting systems for never events, whilst the Joint Commission documented 112 surgical errors in the United States in 2023, with wrong-site surgeries constituting 62% of these incidents.
This underreporting creates substantial barriers for healthcare organisations attempting to gauge the magnitude of the problem and implement effective preventative measures. Inconsistent documentation has been identified as one of the major contributors to WSS occurrences.
Technical innovation through pattern recognition
AESOP’s approach differs significantly from conventional rule-based verification systems. The AOP model utilises data from the Centers for Medicare & Medicaid Services Limited Data Set (2017-2020) to examine discrepancies in surgical laterality documentation.
Rather than simply verifying consistency, the model analyses complex relationships between diagnoses and surgical procedures. Its sophisticated algorithm can process incomplete or ambiguous diagnostic information—a common challenge in clinical documentation—whilst maintaining an accuracy rate exceeding 80% in identifying potential surgical errors.
Dual functionality enhances surgical safety
The AOP model offers both retrospective analysis capabilities and real-time decision support during surgical planning:
Retrospective analysis: Healthcare organisations can identify inconsistencies in medical records, detect unreported surgical errors, and strengthen reporting mechanisms. This comprehensive approach not only improves patient safety but also enhances management systems for error prevention.
Real-time support: The model automatically flags incorrect associations between surgical codes and diagnoses during the planning phase, ensuring accurate and complete documentation. This real-time capability significantly reduces error risks during critical decision-making processes.
Future applications and development roadmap
Initially demonstrating efficacy in orthopaedics, the AOP model shows promise for application in other specialties where laterality is critical, such as ophthalmology and otolaryngology.
“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.”
This development aligns with AESOP’s broader commitment to advancing patient-centred AI solutions across diagnostics, medication safety, and surgical safety domains—potentially establishing new standards for reliability and safety in healthcare.
The integration of the AOP model into electronic health record systems could represent a significant advancement in surgical safety protocols, providing clinicians with valuable decision support whilst simultaneously improving documentation accuracy and completeness.
By addressing the fundamental challenge of documentation inconsistency, AESOP’s innovation targets a critical factor in wrong-site surgery occurrences, potentially reducing these preventable adverse events that continue to affect patient outcomes worldwide.
- For more information, visit: https://www.aesoptek.com
