Hierarchical Healthcare Data Analysis for Performance Driver Detection

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Solution Overview

Problem

Healthcare cost analysis is hindered by the complexity and sparsity of healthcare data, making it difficult to identify performance drivers in a timely manner, which are often hidden and influenced by biases and non-standard terminologies, leading to delayed detection and increased costs.

Innovation Solution

A computer-implemented method using a hierarchical data structure to analyze healthcare data, identify performance indicators, and rank performance drivers based on their impact and contributing factors, allowing for optimization of healthcare systems by adjusting utilization trends and migration patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional manual analysis methods are used to identify performance drivers in healthcare data, then analysts can detect cost patterns, but the process is overwhelmed by millions of potential drivers and influenced by personal bias, leading to delayed detection and increased costs

Engineering Contradiction:

Improvedetection accuracy of performance driversVSAvoidtime to detect performance drivers

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis by analysts with an automated computer-based system that uses algorithms to process healthcare data. The system automatically identifies performance drivers by analyzing claims data against a database of known cost drivers, eliminating human bias and significantly reducing detection time while maintaining or improving accuracy through systematic computational methods.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service analysis where the computer automatically performs the entire performance driver detection process without requiring analyst intervention. The system self-manages data processing, pattern recognition, and driver identification, allowing continuous automated monitoring of healthcare cost patterns without manual overhead.

Inventive Principle:

Principle #25Self-service

2Reliability

If comprehensive healthcare data is analyzed to identify all potential performance drivers, then complete cost patterns can be detected, but the complexity and sparsity of data make the analysis overwhelming and difficult to complete in a timely manner

Engineering Contradiction:

Improvecompleteness of performance driver detectionVSAvoidcomplexity of data analysis system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-processing healthcare claims data and pre-identifying potential performance drivers before comprehensive analysis. The system prepares the data structure in advance, organizing claims information and known cost driver patterns, which simplifies the subsequent detection process and reduces the computational complexity of analyzing all potential drivers comprehensively.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the complex healthcare data analysis into manageable components: data preprocessing, pattern matching against known drivers, statistical analysis, and result validation. This segmentation breaks down the overwhelming task of analyzing all potential performance drivers into systematic steps, reducing overall system complexity while maintaining detection completeness.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If manual analysis of healthcare data is performed, then performance drivers can be identified, but personal bias and nonstandard terminologies confuse analysts, reducing detection accuracy

Engineering Contradiction:

Improveaccuracy of performance driver identificationVSAvoidease of data analysis operation

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system applies parameter changes by transforming nonstandard healthcare terminologies into standardized formats through automated mapping and normalization processes. The computer system converts varied clinical terminologies and coding schemes into consistent parameters that can be systematically analyzed, eliminating the confusion that plagues manual analysis and improving identification accuracy.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11250948B2Searching and detecting interpretable changes within a hierarchical healthcare data structure in a systematic automated manner

Publication Date: 2022.02.15 MERATIVE US LP

AI summary

Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined. The identified performance drivers may be ranked and use of the performance drivers may be adjusted based on the ranking.