AI Medical Coding for Accurate Patient Event Records
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Solution Overview
Problem
Inaccurate and inefficient medical coding in healthcare settings due to the large number of tasks and classifications, leading to significant inaccuracies and missed items in patient records, which can impact billing and diagnosis.
Innovation Solution
Utilizing machine learning models trained on patient data, event records, and motion data to automatically generate accurate medical codes, such as ICD codes, by iteratively refining the models through labeled training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual medical coding is performed by caregivers, then coding can be done with human judgment and context understanding, but the process is time-consuming and prone to inaccuracies due to the large number of tasks and classifications
Solution Approach 1:
The patent replaces the manual mechanical process of code assignment with an automated machine learning system. The ML model processes patient data, event records, and motion data to automatically generate medical codes, eliminating the need for manual code selection by caregivers while improving accuracy and reducing time loss.
Solution Approach 2:
The system enables self-service coding where the machine learning model autonomously performs medical code generation without requiring human intervention. The model learns from training data and independently classifies patient events into appropriate medical codes, making the system self-sufficient for the coding task.
2Reliability
If all tasks and codes are recorded immediately after performing them, then complete and accurate records can be maintained, but this is difficult or impossible given the large number of tasks and vast number of classifications
Solution Approach 1:
The system performs preliminary coding actions during or immediately after patient events occur, using the ML model to generate codes in real-time. This preliminary automation ensures codes are captured at the source without requiring complex post-processing or manual intervention later, maintaining reliability while simplifying the recording process.
Solution Approach 2:
The machine learning model acts as an intermediary between patient events and medical codes. Instead of directly mapping complex events to codes through manual processes, the ML model serves as a mediator that automatically translates clinical data into appropriate medical classifications, reducing the complexity of the recording system.
3Ease of manufacture
If conventional manual coding methods are used, then simplicity in implementation is maintained, but significant inaccuracies and missed items are present in the records
Solution Approach 1:
The patent segments the coding process into distinct components: data collection from multiple sources (patient data, event records, motion data), feature extraction, model inference, and code generation. This segmentation allows the complex accuracy improvement to be achieved through modular processing while maintaining ease of implementation through standardized interfaces between components.
Data Source
AI summary
Techniques for improved machine learning are provided. Patient data for a patient is received, the patient data relating to an action performed by a caregiver. A medical code is generated for the action by processing the patient data using a machine learning model, and the action is associated with the medical code. An event record, including the medical code, is generated for the patient.