Multi-Dimensional Claim Evaluation for Medical Encounter Classification
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Solution Overview
Problem
Existing classification methods for medical encounters, such as emergency visits, rely on single-dimensional evaluations, leading to high false negative rates and improper classification, resulting in increased financial costs and scrutiny, and are often manual and labor-intensive.
Innovation Solution
A multi-dimensional evaluation system that considers patient and provider perspectives, including external cause, primary diagnosis, secondary diagnoses, and procedures, to accurately classify medical encounters, implemented in a cloud-based architecture with payer-specific configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-dimensional evaluation methods are used for classifying medical encounters, then the classification process is simple and fast, but the classification accuracy deteriorates leading to high false negative rates
Solution Approach 1:
The classification system segments the evaluation process into multiple independent dimensional assessments (patient perspective dimensions and provider perspective dimensions). Each dimension evaluates specific aspects such as external cause, primary diagnosis, secondary diagnoses, and procedures separately, then integrates the results. This segmentation allows comprehensive multi-dimensional evaluation while maintaining organizational simplicity through modular architecture.
Solution Approach 2:
The patent transitions from single-dimensional to multi-dimensional evaluation by introducing additional evaluation dimensions. Specifically, it evaluates encounters from both patient perspective (external cause, reason for visit) and provider perspective (primary diagnosis, secondary diagnoses, procedures), adding dimensional depth to the classification process to improve accuracy without overwhelming complexity.
2Productivity
If manual classification methods are used, then the system complexity is low, but the processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated computer-based system. The system uses software applications that automatically extract, evaluate, and classify encounter data according to the multi-dimensional framework, eliminating manual labor while introducing computational complexity that enables high-speed processing.
Solution Approach 2:
The classification system performs self-service through automated query processing and decision-making. The system autonomously queries multiple datastores, evaluates code character strings against classification criteria, and generates classifications without requiring human intervention, thereby achieving high productivity with manageable system complexity through self-contained processing logic.
3Measurement precision
If comprehensive multi-dimensional evaluation is implemented, then the classification accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing classification criteria, reference code tables, and evaluation dimensions before actual classification tasks. The multi-dimensional evaluation framework and query structures are prepared in advance, enabling rapid execution during classification without time-consuming on-the-fly decisions, thus reducing processing time while maintaining comprehensive accuracy.
Solution Approach 2:
The patent implements continuous evaluation across multiple dimensions without interruption. The system sequentially queries different datastores and evaluates different dimensions in a continuous workflow, maintaining productive action throughout the classification process. This continuous approach prevents idle time and ensures that comprehensive evaluation is performed efficiently through uninterrupted processing.
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.