Episode Classification System Using Probability Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current approaches to measuring episodes of care lack a mathematical model, rely on heuristic methods, require diagnosis classification into clusters, and focus on resource use rather than diagnosis intensity, limiting the accumulation of knowledge and accuracy in understanding severity.
Innovation Solution
A relation-based episode classification system that uses probability and severity analyzers to group diagnoses without pre-defined clusters, calculating probability and severity based on similarity and time between diagnoses, allowing for accurate classification and measurement of episode severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If diagnosis classification into clusters is used to identify episodes of care, then the complexity of episode identification is reduced, but important nuances and differences between types of diagnoses are lost
Solution Approach 1:
The patent segments the episode identification process into two independent components: (1) a mathematical model that calculates the probability that diagnoses belong to the same episode based on similarity and time factors, and (2) a separate classification step that groups diagnoses into episodes based on these probability scores. This segmentation allows the system to maintain detailed diagnosis information while systematically identifying episodes without requiring pre-defined clusters.
Solution Approach 2:
The patent changes the approach from using fixed diagnostic clusters to using dynamic probability scores calculated from multiple parameters including diagnosis similarity (based on ICD-9 code structure) and time between diagnoses. This parameter-based approach allows the system to flexibly determine episode membership based on quantitative measures rather than rigid predefined categories, preserving diagnosis nuances while maintaining systematic episode identification.
2Reliability
If heuristics are used to measure episode severity, then clinical sense is maintained, but mathematical theory and cumulative knowledge accumulation are limited
Solution Approach 1:
The patent replaces heuristic-based severity assessment with a mathematical model that uses probability theory to calculate the likelihood that diagnoses belong to the same episode. The model incorporates similarity measures (based on ICD-9 code hierarchical structure) and time decay functions to quantitatively assess episode membership. This substitution maintains clinical relevance while introducing rigorous mathematical foundations that enable cumulative knowledge accumulation and systematic improvement.
3Productivity
If resource use intensity is used to classify visits into episodes, then homogeneous resource use groups are created, but the nature of the diagnosis is ignored
Solution Approach 1:
The patent inverts the traditional approach by using diagnosis similarity and temporal relationships as the primary basis for episode classification, rather than using resource use patterns. The mathematical model first determines episode membership based on diagnosis characteristics (ICD-9 code similarity and time between visits), and only then can resource use be analyzed within these clinically meaningful episodes. This inversion ensures that episodes are defined by their clinical nature rather than by resource consumption patterns.
Data Source
AI summary
An episode classification system including a multitude of diagnosis records. Each of the diagnosis records includes diagnoses information, time of diagnoses information, and patient information. A patient grouper generates at least one patient group by grouping patient records having similar patient information. A diagnosis grouper generates at least one diagnosis group from a patient group by grouping patient records from a patient group that have similar diagnosis information. An episode analyzer includes a probability analyzer, an episode grouper, and a severity analyzer. The probability analyzer performs probability calculations capable of generating a probability value using at least two of the diagnosis records as input entries. The probability value represents the probability that the input entries belong to a single episode. The episode grouper groups diagnosis records determined to belong to a single episode. The severity analyzer performs episode severity calculations capable of generating an episode severity value.