Multi-Dimensional Code Evaluation for Medical Encounter Classification
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Solution Overview
Problem
Existing classification methods for medical encounters, particularly emergency visits, suffer from high false negative rates and are labor-intensive, often misclassifying non-emergent visits as emergent, leading to financial abrasion and scrutiny.
Innovation Solution
A multi-dimensional evaluation system that assesses medical encounters based on patient and provider perspectives, utilizing dimensions such as external cause, primary diagnosis, secondary diagnoses, and procedures, implemented in a cloud-based architecture with payer-specific configurations to accurately classify encounters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing classification methods are used, then classification speed is maintained, but classification accuracy deteriorates with high false negative rates
Solution Approach 1:
The classification system is divided into multiple independent dimension evaluations (patient dimension, provider dimension, encounter dimension), each assessing specific code character strings separately. This segmentation allows parallel processing of different dimensions while maintaining comprehensive evaluation, thereby improving both accuracy and processing efficiency.
Solution Approach 2:
The patent introduces multi-dimensional evaluation beyond traditional single-criterion classification. By adding dimensions such as patient perspective, provider perspective, and encounter characteristics, the system achieves more accurate classification without significantly increasing processing time, as each dimension contributes independently to the final classification decision.
2Measurement precision
If manual classification review is performed, then classification accuracy improves, but labor intensity increases
Solution Approach 1:
The system performs automated multi-dimensional evaluation and self-classification of encounters using code character strings from claims data. The algorithm independently assesses multiple dimensions and determines classification without requiring manual intervention, thereby maintaining high accuracy while eliminating labor-intensive manual review processes.
Solution Approach 2:
The patent replaces manual mechanical classification processes with an automated computational system that evaluates code character strings across multiple dimensions. This substitution maintains or improves classification accuracy while dramatically reducing labor intensity by using algorithmic processing instead of human reviewers.
3Measurement precision
If multi-dimensional evaluation is implemented, then classification accuracy improves, but system complexity increases
Solution Approach 1:
The complex multi-dimensional evaluation system is segmented into distinct, manageable dimension modules (patient dimension, provider dimension, encounter dimension). Each module evaluates specific code character strings independently, making the overall complex system easier to implement, maintain, and debug while achieving high classification accuracy through comprehensive evaluation.
4Reliability
If comprehensive code character string evaluation is performed, then classification reliability improves, but processing time increases
Solution Approach 1:
The patent evaluates code character strings across multiple independent dimensions (patient, provider, encounter) rather than deeply analyzing a single dimension. This dimensional approach allows comprehensive evaluation that improves reliability while controlling processing time, as each dimension can be processed in parallel and contributes independently to the final classification.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatuses, systems, devices, computing entities for evaluating a medical encounter between a healthcare provider and a patient. Various embodiments evaluate a medical encounter to determine a classification of the medical encounter. An example method comprises receiving a claim data object comprising a plurality of code portions, each code portion corresponding to a dimension of the medical encounter; processing the claim data object to extract a plurality of code character strings, each code character string extracted from a corresponding code portion of the claim data object; generating a claim classification for the claim data object based at least in part on evaluating the plurality of code character strings with respect to at least one dimension relating to the provider's contribution to the encounter and at least one dimension relating to the patient's contribution to the encounter; and performing at least one classification-based action.