Predictive Server for Medical Claim Misdiagnosis Detection
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Solution Overview
Problem
Critical illnesses often face misdiagnosis due to their rarity, complexity, and the unavailability of advanced diagnostic tools, leading to adverse outcomes, ineffective treatments, and resource wastage.
Innovation Solution
A trained predictive server system that uses prior authorization data to identify potential misdiagnoses by applying machine learning models to determine the accuracy of diagnosis and treatment plans, generating requests for consulting reviews when inaccuracies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If healthcare providers diagnose critical illnesses using conventional methods, then the diagnostic process remains simple and quick, but misdiagnosis rates increase due to illness rarity and complexity
Solution Approach 1:
A predictive server acts as an intermediary between healthcare providers and diagnostic decision-making. The server receives PA data, applies trained predictive models to assess misdiagnosis risk, and generates consulting review requests when inaccuracies are detected, thereby improving diagnostic reliability without directly complicating the provider's workflow
Solution Approach 2:
The patent replaces manual diagnostic assessment with automated machine learning models. The trained predictive models automatically analyze PA data patterns to identify potential misdiagnoses, substituting human cognitive evaluation with computational analysis that can process complex patterns without fatigue or bias
2Reliability
If advanced diagnostic tools are made available to all healthcare providers, then diagnostic accuracy improves, but the cost and complexity of the healthcare system increases
Solution Approach 1:
The patent creates a virtual copy of specialized diagnostic capabilities through machine learning models trained on extensive PA data. Instead of requiring every provider to have access to expensive specialized tools or expertise, the predictive server replicates the diagnostic intelligence of specialists through automated analysis, making advanced diagnostic capability universally accessible
Solution Approach 2:
The predictive server provides universal diagnostic support across diverse healthcare settings. A single system handles multiple functions: analyzing PA data, applying multiple predictive models for different critical illnesses, generating consulting review requests, and providing second opinions, thereby replacing the need for multiple specialized tools and experts
3Reliability
If misdiagnoses are detected and corrected, then patient outcomes improve and resource waste is reduced, but the diagnostic process takes longer and requires additional resources
Solution Approach 1:
The system performs preliminary analysis of PA data immediately when claims are submitted, identifying potential misdiagnoses before treatment begins. By detecting inaccuracies in advance and generating consulting review requests proactively, the system prevents delayed diagnosis and inappropriate treatment without adding time to the overall diagnostic process
Solution Approach 2:
The predictive server implements continuous feedback by monitoring PA data patterns, comparing predicted misdiagnosis risks against actual outcomes, and refining predictive models over time. This feedback loop improves detection accuracy and reduces false positives, ensuring that consulting review requests are generated only when genuinely needed, thereby minimizing unnecessary delays
4Reliability
If consulting reviews are generated for all potential misdiagnoses, then diagnostic accuracy improves, but resource consumption and system cost increase
Solution Approach 1:
The system dynamically adjusts the threshold for generating consulting review requests based on predicted misdiagnosis risk levels. For high-risk cases with strong predictive signals, consulting reviews are generated immediately. For lower-risk cases, the system may monitor without immediate review or adjust thresholds based on illness type, provider expertise, and resource availability, thereby optimizing resource allocation while maintaining diagnostic accuracy
Data Source
AI summary
A trained predictive server is provided for determining that a diagnosis and treatment plan is inaccurate. The trained predictive server includes a processor configured to receive a set of prior authorization (PA) data associated with a medical claim for a patient, and determine that the set of PA data indicates that the medical claim is associated with a qualifying critical illness. The processor is further configured to extract component data from the set of PA data, and apply the extracted component data to a trained predictive model associated with the qualifying critical illness to determine whether the medical claim is associated with an inaccurate diagnosis and treatment plan. Upon determining that the medical claim is associated with an inaccurate diagnosis and treatment plan, the processor is configured to generate a request for a consulting review of the diagnosis and treatment plan using the set of PA data.