MDMai

On average each year, physicians miss out on $75,000 due to incorrect MDM levels

MDMai

Our advanced MDMai model is designed to analyze medical encounter notes with a high degree of accuracy, accurately determining the medical decision-making (MDM) level. It provides intelligent suggestions to elevate the MDM level if needed, ensuring that it meets the highest standards of complexity and comprehensiveness. This model leverages deep learning algorithms and natural language processing to enhance clinical documentation and accurate MDM levels.

The Feedback Loop

MDMai and ENCOUNTERai work in tandem to optimize clinical documentation and medical decision-making levels (MDM). MDMai first ingests and processes existing encounter notes to accurately determine the appropriate MDM level. These encounter notes and their corresponding MDM levels are then utilized to train and refine the ENCOUNTERai algorithms. This iterative feedback loop allows HAL to generate future encounter notes efficiently upon request. By leveraging this dynamic learning process, HAL significantly enhances its accuracy and efficiency in determining MDM levels, aiming to reduce the time required for creating physicians encounter notes to near zero.

How it Works

MDMai is an advanced AI-driven system designed to optimize the medical decision-making (MDM) process. It begins by ingesting and analyzing physicians' existing encounter notes, utilizing natural language processing (NLP) and machine learning algorithms to accurately assess the complexity and comprehensiveness of each note. MDMai determines the appropriate MDM level by evaluating key clinical factors, including the number and complexity of problems addressed, the amount and complexity of data reviewed, and the risk of complications, morbidity, or mortality associated with patient management.