Medication Error is Common
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A right start
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| The Medication-Use Process |
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Better Decision Support for Unexplained MedicationRxPrime helps to detect potential errors in prescriptions. It provides alternative recommendations based on the patient's diagnoses, age, and gender, considering multiple perspectives, including the department and hospital-wise. It offers real-time decision support without disrupting the clinical consultation process. In this way, RxPrime enhances prescriptions' accuracy and safety, reducing the risk of medication errors and improving patient outcomes.
Dual AI EnginePowered by a dual AI engine with customized algorithms for medication safety, RxPrime continuously self-improves and reduces alert fatigue. It analyzes large data sets to identify patterns and trends not immediately apparent to humans, thereby providing more accurate and relevant support during prescribing. |
U.S., Taiwan Partner to Improve Patient SafetyAccording to our clinical study conducted in partnership with Harvard Medical School and Taipei Medical University, RxPrime’s accuracy ranged from 79% to 85%. The study employed an international federated learning approach, a method of training machine learning models on decentralized data to ensure the transferability of results. The findings demonstrated that RxPrime effectively identifies potential prescription errors and provides accurate recommendations when applied across different countries.
 3.2B Prescription DatabaseRxPrime is built on a vast database of well-coded prescriptions, comprising over 3.2 billion records and 2.4 billion association rules. This data supports medication decision-making by analyzing both explicit and tacit knowledge, as well as prescribing behaviors from doctors. |
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In the light of increasing complexity of healthcare and the aging population, there is a very clear trend that there are more medical errors happening every day. Tools that our offer by companies like AESOP provide us a way to tackle this serious worldwide problem with innovative AI tools. We are very hopeful that in the future AI is going to help us reduce error and improve patient safety significantly.
Dr. Yu-Chuan Jack Li
Distinguished Professor College of Medical Science and Technology Taipei Medical University Chief & Dermatologist Taipei Municipal Wanfang Hospital President International Medical Informatics Association |
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We’re living in an exciting time. We now have more data because of the EHRs that we’ve installed across the country in the world. Yet we’ve not built tools that allow us to harvest that data. AI and machine learning provide new opportunities to improve medication safety. Approaches like AESOP will allow us to understand multiple drug interactions as well as age and drug interactions, and diagnosis and age interactions, so that we can improve medication safety for all of our citizens.
Prof. Charles Safran
Chief Harvard Medical School Division of Clinical Informatics Beth Israel Deaconess Medical Center |
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