AESOP Technology Unveils World's First Machine Learning Model to Combat Wrong-Site Surgery2/19/2025
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. ![]()
PRNewswire
Medical AI start-up Aesop Technology announced a new partnership that made their new product, DxPrime, available in the Olive Library. DxPrime provides physicians and clinical documentation improvement (CDI) teams with information about missing and wrongly coded diagnoses and procedures to correct the patient's chart in just a few clicks. It makes completing discharge summaries, prioritizing work for CDI teams, and medical coding much easier, faster, and less error-prone.
If the patient record is incorrect, you cannot code correctly. Completeness, precision, and validity of medical documentation are critical for all healthcare stakeholders. Without correct patient records, patients could receive improper discharge instructions and a sub-optimal continuum of care. Providers also can struggle to estimate the length of stay and code insurance claims correctly, resulting in denials and loss of revenue. Approximately 10% of inpatient claims are denied, of which more than 85% (or about $35 billion) result in unnecessary losses. Many of these denials occur because of errors in the patient record that occur upstream from the claims process. Diagnosis input errors are difficult for physicians to avoid because the knowledge of coding systems is different from what they need to learn to provide great patient care. Modern medicine's complexity has caused 14,400 diseases to be included in ICD-10, further classified into 68,000 ICD-10-CM and 87,000 ICD-10-PCS codes. "Physicians, CDI team, and coders have to spend a lot of time poring through medical records to find the key clinical diagnoses among the vast amount of information available," said Jim Long, CEO of AESOP. "After that, they have to follow a series of inefficient steps on the computer to complete the input process, and search functionality for ICD codes often is not helpful. The whole process is complex, time-consuming, and error-prone. When the physicians input the improper diagnosis, it also has downstream implications. "When using DxPrime, we have helped physicians often notice they did not correctly code complications such as urinary tract infections and respiratory failure. By assisting them in inputting the proper diagnoses, our partners have seen an increase in revenue of 5-10% per inpatient." State-of-the-art machine learning assisted physician data entry. DxPrime provides high-quality suggestions to support physician data entry based on a machine learning model (published in the Healthcare journal) that has been run on top of data from 3.2 Billion patient visits, including vast amounts of structured information. It allows DxPrime to use items from the patient record like lab test results and medications ordered when predicting a diagnosis. This comprehensive model utilizes artificial intelligence to efficiently compensate for traditional CDSS and NLP weaknesses to find correct or missed diagnoses.
Referred from: PR Newswire
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