Reference:
PharmStars

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.

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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We are delighted to announce that AESOP Technology has been awarded
the Silver Prize at the 2024 Taipei Biotech Awards.

​We are deeply grateful for this recognition from the Taipei City Government.

Medication errors are a critical problem in healthcare, and Look-Alike, Sound-Alike (LASA) medication errors pose a particularly daunting challenge. Studies show that LASA errors account for approximately one in four medication errors, making them a significant threat to patients. AESOP Technology is thus pleased to announce remarkable results from its recent clinical research, demonstrating its exceptional effectiveness in preventing LASA errors.

"Accurately identifying LASA errors can be challenging due to their complex origins. Surprisingly, only about 15% of intercepted wrong drug errors during our study could be clearly categorized as LASA errors, in the sense that the medications had similar names that contributed to the mix-up. The LASA errors also did not follow predictable patterns, with only 9 out of 71 cases repeating. These findings underscore the limitations of conventional human-defined rule systems, even when augmented with reinforcement learning. RxPrime (formerly MedGuard) not only detects errors that were not previously identified but also highlights the essential need for AI with advanced medical knowledge, demonstrating a breakthrough in patient safety in healthcare," said Jim Long, CEO of AESOP Technology, explaining the intricacies of detecting medication errors, specifically LASA errors.

AESOP also leverages its proprietary AI technology to overcome the limitations of traditional clinical decision support systems and address the issue of alert fatigue.

"AESOP has taken additional steps to improve problem list documentation to reduce alert fatigue from LASA error detection. Many alerts contributing to physician fatigue result from poor problem list documentation within electronic health record systems. Thus, AESOP intervened at the source by helping physicians complete clinical diagnoses and documentation more effectively while prescribing," Long added.
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More than half of all medication errors occur during the prescription phase, making it a critical focus for improving patient safety. AESOP's innovative approach offers a beacon of hope. Applying advanced AI technology to this complex problem has yielded impressive results and holds great promise for the healthcare industry. As we address patient safety challenges, AESOP Technology's contributions stand as a testament to the potential of innovative solutions in ensuring the well-being of patients worldwide.

Reference: PR Newswire

SOAP Health's conversational AI for medical encounters, risk and symptom assessment, and documentation integrates with AESOP Technology's electronic medical record data analysis for clinical decision optimization, coding, and productivity.
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BOCA RATON, Fla., Sept. 7, 2023 /PRNewswire/ -- SOAP Health and AESOP Technology are excited to announce a transformative partnership that will push the boundaries of AI-enhanced medical encounters. This collaboration combines AESOP's cutting-edge DxPrime and DeepDRG solutions with SOAP Health's patented and clinically validated Ideal Medical AI Assistant™ to fuse patient-reported data with Electronic Medical Record Systems (EMRs) data to create Precision Patient Profiles™ that give physicians the most comprehensive view and understanding of their patients to improve speed to diagnosis, workflow efficiency, and revenue.

Steven Charlap, MD, MBA, CEO of SOAP Health, believes this alliance marks a milestone in effectively integrating AI into clinical encounters. "By incorporating AESOP's DxPrime into our existing solutions, we are elevating the realm of data-driven clinical decision-making. DxPrime's DeepDRG and Natural Language Processing capabilities are revolutionary, enhancing the quality and efficiency of data analysis and diagnostic decisions," said Dr. Charlap. "Combined with SOAP's patented and clinically validated Ideal Medical AI Assistant™, this partnership promises to enhance medical encounters, providing a robust, integrated solution that is unparalleled in today's market."

Combined with SOAP's integration with Dolbey Fusion Narrate™, a previously announced partnership, the Precision Patient Profile™ will seamlessly integrate into over 100 EMRs, fortifying the power of these applications to deliver value to physicians. "The joint venture between AESOP Technology and SOAP Health promises to radically optimize physician data collection and medical coding," says Jeremiah Scholl, Co-founder and CPO of AESOP Technology. "Our shared mission is to harness the power of AI to improve patient outcomes and clinical efficiency, and this partnership is a giant leap toward fulfilling that commitment."

SOAP Health's vision to reduce diagnostic errors aligns perfectly with AESOP's focus on improving the quality and efficiency of clinical decision-support and medical coding. This partnership is poised to bring significant advancements in physician practice, optimizing patient care and safety.
For more details on how SOAP Health and AESOP Technology are collaborating, please visit the SOAP Health Partner Page at www.soap.health/aesop

About AESOP Technology
AESOP Technology harnesses advanced AI to improve the clinical decision-making process, enhance medical coding quality, and prioritize patient safety. AESOP's flagship products, DxPrime and DxCode, boast 99% accuracy in identifying and analyzing data and integrate directly into physicians' clinical workflows. DeepDRG is the unique AI approach they developed to unlock understanding of how diagnoses are typically associated with structured clinical data like lab results, medications, and procedures. To learn more about AESOP Technology, visit aesoptek.com.

About SOAP Health
SOAP Health is a trailblazer in medical practice AI. Through patented conversational and generative AI, SOAP's mission is to save lives by reducing diagnostic errors, accounting for over 800,000 preventable deaths and permanent disabilities each year. The company leverages the collective expertise of seasoned medical entrepreneurs, scientists, and technologists to deliver clinically validated solutions. To learn more about SOAP Health, visit soap.health. 

AESOP Technology, in collaboration with AstraZeneca Taiwan, has unveiled Medigator, an innovative AI software designed to manage immune-related adverse events (irAEs) and enhance the effectiveness of immunotherapy. With cancer being the second leading cause of death and immunotherapy offering improved survival rates, Medigator is specially designed to address the potential challenges associated with irAEs that may deter some patients from choosing this treatment.

The American Cancer Society estimates that of the 2 million new cancer cases in the U.S. in 2023, about half could be eligible for immunotherapy treatment. While this treatment stimulates the patient's immune system to fight cancer, it can potentially trigger an overactive immune response, leading to irAEs.
Managing irAEs is challenging due to the unique immune responses and variability in the reaction to immunotherapy. The intensified immune response that fights cancer cells can inadvertently harm normal tissues. Effective communication is crucial to educate patients about possible side effects, readiness for assistance, and timely medical intervention. Mild irAEs can often be managed symptomatically with topical treatments, while severe cases may require discontinuing immunotherapy and administering immune-suppressing medications.

To address the complexities of managing irAEs, Medigator offers real-time assistance to physicians. This tool manages the risk of irAEs and is seamlessly integrated into the Computerized Physician Order Entry system.
"By harnessing the power of big data analytics, Medigator analyzes real-world patient experiences with irAEs, physicians' management strategies, and patient responses to treatment based on a dataset of 197,921 claim-based prescriptions. Using these analytical parameters, Medigator goes a step further by predicting the risk levels of different irAEs in individual patients. It provides personalized medication options by considering factors such as patient age, gender, race, chronic medical conditions, and genetic history, empowering physicians to enhance their risk management strategies in the care plan," explained Jim Long, CEO of AESOP Technology.

"Medigator is an immunotherapy medication navigator designed for physicians. It aims to minimize interruptions in immunotherapy, preserve valuable treatment time and resources, and alleviate the treatment burden," said Jim Long.

"The origin of the partnership between AESOP Technology and AstraZeneca Taiwan traces back to 2019 at an international biomedical accelerator co-hosted by AstraZeneca A.Catalyst Network and the National Biotechnology Research Park in Taiwan, which is dedicated to exploring new possibilities to change patients' lives. Medigator has set a promising example demonstrating patient-centric innovation by advancing shared decision-making in precision medicine," said Ben Chen, Medical Director, AstraZeneca Taiwan.

The research findings of Medigator, which were recently presented at the annual meeting of the American Society of Clinical Oncology, received notable attention. AESOP Technology continues its unwavering commitment to delivering physicians with precise and personalized solutions for irAEs as immunotherapy advances. Its ultimate goal is to enhance immunotherapy treatment outcomes and improve patients' overall quality of life.

Reference
Yahoo Finance