DxPro utilizes a Clinical Deep Reasoning Network trained on billions of health records to analyze structured and unstructured clinical data. This platform provides physicians with real-time diagnostic insights and automated concurrent coding assistance. For coders, it offers code-ready suggestions, complexity detection, and DRG optimization for improved compliance.
Funding
$3.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
HEFounders
Product
Problem
Physicians face challenges in accurately capturing and documenting diagnoses due to the complexity of patient medical histories and the increasing volume of clinical data. Incomplete or inaccurate diagnostic information can lead to errors, unnecessary confirmations, and delays in effective patient care, impacting both patient outcomes and hospital performance.
Solution
AESOP's DxPrime platform leverages Clinical Deep Reasoning Networks and machine learning to analyze patient data in real-time, enhancing the accuracy and efficiency of the diagnostic decision-making process. By conducting gap analysis between diagnoses and clinical evidence, DxPrime provides real-time feedback on potentially missed, incomplete, or unclear diagnostic items. The system integrates directly into clinical workflows via the electronic health record system, offering physicians evidence-based analysis and individualized insights tailored to different medical departments and patient genders. This approach reduces diagnostic errors, promotes timely intervention of comorbidities, and improves the overall quality and continuity of care.
Target Audience
The primary target audience includes physicians, hospitals, and healthcare systems seeking to improve diagnostic accuracy, reduce errors, and enhance the efficiency of clinical documentation.
Features
- Clinical Deep Reasoning Networks analyze patient data in real-time based on specific conditions.
- Proactive gap analysis identifies potentially missed, incomplete, or unclear diagnostic items.
- Explainable AI provides dynamic displays of network relationships between suggested items and clinical evidence.
- Individualized analysis tailors insights to different medical departments and patient genders.
- Seamless integration into existing clinical workflows via electronic health record systems.
- Supports early prediction and timely intervention of comorbidities with real-time feedback on primary and secondary diagnoses.
- Identifies possible diagnoses for rare or complex cases.