Clinical Note Segmentation for Early DRG Prediction
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Solution Overview
Problem
Hospitals face challenges in optimizing cost and quality of care due to the manual and time-consuming process of calculating Diagnosis-Related Groups (DRGs) post-discharge, which hinders timely resource allocation and reimbursement management.
Innovation Solution
Applying a machine-learning model to pre-discharge medical notation data to predict medical billing classification codes, such as DRGs, by partitioning data into sequences, generating probability values, and selecting the most probable code based on cumulative or mean probability values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation of DRGs is performed post-discharge, then coding accuracy is maintained, but time consumption increases and resource allocation becomes delayed
Solution Approach 1:
The machine learning model performs DRG prediction during the inpatient stay (pre-discharge) rather than waiting for post-discharge coding. The system continuously processes medical notation data, procedure codes, and patient characteristics to predict DRGs in real-time, enabling proactive resource allocation and reimbursement management before discharge occurs.
Solution Approach 2:
The patent replaces the manual mechanical coding process with an automated machine learning system. The model uses neural networks to process unstructured medical notes and structured data, automatically generating DRG predictions without human intervention, thereby eliminating time-consuming manual calculations while maintaining coding accuracy.
2Reliability
If manual DRG calculation is performed, then coding thoroughness is maintained, but productivity decreases
Solution Approach 1:
The machine learning system performs self-service by automatically processing medical data, generating DRG predictions, and providing probability distributions without requiring manual review or expert intervention. The model continuously learns from new data patterns and refines its predictions autonomously, maintaining thoroughness while significantly improving productivity.
Solution Approach 2:
The patent transforms the coding process from manual parameter entry to automated multi-parameter processing. The system simultaneously analyzes numerous parameters including medical notation data, procedure codes, patient demographics, and historical data patterns, processing them through machine learning algorithms to generate comprehensive DRG predictions that would be impossible to manually review in real-time.
3Measurement precision
If DRGs are calculated post-discharge, then accurate billing classification is achieved, but financial management responsiveness is reduced
Solution Approach 1:
The system performs DRG prediction and billing classification before discharge occurs, enabling hospitals to proactively manage finances during the inpatient stay. The model generates predicted DRGs and associated reimbursement estimates in real-time, allowing financial managers to respond immediately to changing patient conditions and optimize resource allocation before the discharge event.
Solution Approach 2:
The machine learning system incorporates feedback loops that continuously compare predicted DRGs with actual discharged codes, refining predictions over time. The model learns from discrepancies between predicted and actual classifications, improving accuracy while maintaining real-time responsiveness for financial management decisions during the admission period.
Data Source
AI summary
Techniques for early prediction of medical billing classification codes and associated medical billing costs using routine clinical text are disclosed. The system predicts the medical billing codes within defined hours of admission by generating vector embeddings from a set of medical notation data, bypassing the need for post-discharge medical codes. Using a novel segmentation technique, the system processes lengthy medical notation data by dividing them into smaller subsequences. These subsequences are input to a large language model (LLM) to generate a plurality of sets of probability values for a set of medical billing classifications. The system selects a particular predicted medical billing classification for the patient based on the sets of probability values. Additionally, the system estimates medical billing costs early in the admission process. The system ensures comprehensive context utilization from clinical notes, enabling hospitals to manage treatment expenses proactively and improve operational efficiency.