Predictive Modeling for Patient True State Determination
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Solution Overview
Problem
Current medical information management systems face challenges in accurately identifying and documenting patient conditions, leading to issues such as incomplete or incorrect data, which affects MediCare compensation, diagnosis, and medical analytics, resulting in inefficiencies and potential health risks.
Innovation Solution
A system that utilizes predictive modeling and natural language processing to analyze medical records for patient 'true state', enabling personalized care, improved record keeping, and optimized MediCare reimbursement by determining the necessary medical resources and validating evidence for accurate condition identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of medical records is used to identify patient conditions, then diagnostic accuracy may be improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary automated analysis of medical records using NLP and predictive modeling to identify potential conditions and extract evidence before human review. This pre-processing step filters and organizes information, so that when clinicians do review records, they are examining pre-identified high-priority findings rather than manually scanning entire records, thus reducing time consumption while maintaining diagnostic accuracy
Solution Approach 2:
The patent introduces an intermediary automated system that sits between raw medical records and human clinicians. This intermediary uses NLP to parse unstructured data, predictive models to identify conditions, and evidence extraction to link findings to supporting documentation. The intermediary prepares structured, prioritized information for human review, reducing the time burden on clinicians while preserving diagnostic accuracy through multiple layers of validation
2Reliability
If comprehensive medical record analysis is performed to ensure accurate diagnosis, then patient care quality improves, but resource consumption and costs increase
Solution Approach 1:
The system applies partial action by focusing computational resources on the most critical aspects of medical record analysis. Rather than uniformly analyzing all records at maximum depth, the system uses predictive models to identify high-risk cases that require comprehensive analysis, while applying lighter analysis to low-risk cases. This selective approach maintains patient care quality for critical cases while reducing overall resource consumption
Solution Approach 2:
The patent dynamically adjusts analysis parameters such as confidence thresholds, review depth, and resource allocation based on patient risk profiles, condition severity, and available resources. By changing these parameters adaptively, the system optimizes the balance between care quality and resource consumption, intensifying analysis when quality is most critical and reducing analysis intensity when resources are constrained
3Productivity
If automated systems are used to analyze medical records rapidly, then productivity increases, but measurement precision and reliability of condition identification may deteriorate
Solution Approach 1:
The automated analysis system is segmented into multiple specialized modules: NLP preprocessing, predictive modeling, evidence extraction, and validation layers. Each segment handles specific aspects of analysis with optimized algorithms for its function. This segmentation allows parallel processing that maintains high productivity while each module contributes to overall accuracy through its specialized expertise, with results validated by subsequent segments
Solution Approach 2:
The system incorporates feedback mechanisms where automated analysis results are validated against established medical guidelines, cross-checked with multiple data sources, and subject to confidence threshold verification. Low-confidence findings trigger additional validation steps or human review, creating a feedback loop that maintains measurement precision while preserving the overall productivity benefits of automation
4Reliability
If detailed evidence verification is performed for each identified condition, then coding reliability improves, but the complexity of the process increases
Solution Approach 1:
The system performs preliminary evidence gathering and organization before the verification stage. NLP extracts relevant documentation snippets, predictive models pre-identify supporting evidence, and the system pre-structures this evidence in a standardized format. This preliminary action reduces the complexity of the subsequent verification process by presenting pre-organized evidence rather than requiring clinicians to search through entire medical records
Data Source
AI summary
Systems and methods for personalizing medicine utilizing the true state of the patient are provided. A number of medical records for a patient are subjected to predictive modeling for various conditions (known as patient ‘true state’). The patient personal information, previous care, and true state may be provided into a state machine in order to determine the resources needed for the patient. The medical resources may be any of laboratory services, diagnostics, therapies and medications. Using the true state information, and number of activities may be performed for the patient based upon the patient's needs. These activities include scheduling lab or diagnostic procedures in advance of an appointment, filling in documentation gaps, identifying items that require additional documentation using the true state, and tracking follow-up. It may also be beneficial to validate the true state.