Healthcare Site Class-of-Trade Recommendations Using Fee-Schedule Analytics

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Solution Overview

Problem

Healthcare sites face challenges in identifying the appropriate class of trade (COT) that aligns with their operating state and interactions within the health provider ecosystem, which affects their operational efficiency and reimbursement strategies.

Innovation Solution

A computing system that analyzes historical operational data and fee schedules to determine performance metrics for different COTs (e.g., Hospital COT and Clinic COT), applying an analytic model to normalize fee schedules and generate recommendations for transitioning to the most suitable COT based on these metrics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If healthcare sites manually evaluate different classes of trade using complex fee schedule analysis, then measurement precision of performance metrics is improved, but loss of time and device complexity increase

Engineering Contradiction:

Improveperformance metric accuracyVSAvoidevaluation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automated self-evaluation of Class of Trade suitability by processing fee schedule data and operational characteristics automatically, eliminating the need for manual analysis while maintaining high measurement precision through consistent algorithmic evaluation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical analysis processes with automated computational systems that process fee schedule data and operational characteristics, substituting human evaluation with algorithmic determination to reduce time loss while preserving measurement accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If healthcare sites use simplified evaluation methods for class of trade selection, then loss of time is reduced, but measurement precision of performance metrics deteriorates

Engineering Contradiction:

Improveevaluation timeVSAvoidperformance metric accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The automated system performs comprehensive fee schedule analysis without requiring manual intervention, achieving both time efficiency and measurement precision through systematic processing of operational characteristics and fee data

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Complex manual evaluation processes are replaced with automated computational algorithms that efficiently process multiple fee schedule scenarios, maintaining high measurement precision while dramatically reducing the time required for Class of Trade determination

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If healthcare sites analyze multiple classes of trade with detailed fee schedules, then adaptability of reimbursement strategy is improved, but device complexity increases

Engineering Contradiction:

Improvereimbursement strategy flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically evaluates multiple Class of Trade scenarios and their impact on reimbursement strategies, enabling adaptable financial planning without requiring complex manual analysis frameworks

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The evaluation system handles multiple fee schedule types and Class of Trade scenarios through a unified automated process, providing versatile reimbursement strategy analysis while maintaining consistent system complexity through standardized computational approaches

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250308683A1Systems and methods for class of trade recommendations for a healthcare site

Publication Date: 2025.10.02 MCKESSON CORPORATION

AI summary

In some embodiments, a computing system can access data indicative of operating conditions of a healthcare site over a historical time period, and data indicative of fee schedules corresponding to respective classes of trade. The computing system, via a generative adversarial network, can generate a recommendation to configure the healthcare site according to a particular class of trade.