SOAP Health and AESOP Technology Unite to Provide Advanced AI Solutions for Clinical Decision-Support and Medical Coding
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.
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.
New study paves the way for collaboration on artificial intelligence modelling and medication error reduction globally
Researchers at Harvard Medical School, Brigham and Women's Hospital, Taipei Medical University, and Aesop Technology, a Taiwan-based startup, announced today the results of a new joint study into the international transferability of machine learning (ML) models to detect medication errors. The results were recently published in the Journal of Medical Internet Research - Medical Informatics.
Working to Reduce Medication Errors
Medication errors are a growing financial and healthcare burden that results in economic costs of around US$ 20 billion and more than 250,000 deaths annually in the U.S. alone.
Medication errors can occur during any stage of the medication process, including prescribing, dispensing, administration, and monitoring, with errors in prescribing accounting for 50% of the total.
When medicating patients, physicians go through complex decision-making processes to accurately write a prescription. First, they must clearly define the patient's problem and list the therapeutic objective before selecting an appropriate drug therapy based on age, gender, and possible allergies. They must also consider dosing, drug-drug interaction, potential discontinuation of the drug, drug cost, and other therapies — and all of these need to be done instantly and simultaneously.
"Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr. David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham & Women's Hospital and Professor of Medicine at Harvard Medical School.
For technology to assist in solving these problems, it is critical that machine learning understands these variables. For this to be successful, data must be properly collected, organized, and maintained.