Probabilistic Medical Profile Generation Using Age-Correlated Patient Data
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Solution Overview
Problem
Current healthcare prediction systems face challenges in accurately forecasting future medical profiles and healthcare costs for patients, especially for longer-term projections, as they rely heavily on individual medical histories and struggle to account for age-related correlations with other patients' data.
Innovation Solution
A method involving computers that obtain current medical histories, match age-correlated characteristics with those of older patients, calculate similarity scores, and generate probabilistic future medical profiles, including non-matching characteristics, to estimate future healthcare values and costs by aggregating data from multiple patients' histories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prediction systems rely heavily on individual medical histories, then they can maintain patient-specific accuracy, but they fail to account for age-related correlations with other patients' data
Solution Approach 1:
The patent combines individual patient medical history data with aggregated population data from multiple patients of similar ages. The system merges these datasets to create a comprehensive prediction model that maintains patient-specific accuracy while incorporating age-related correlations from other patients, thereby resolving the contradiction between individual precision and population-level adaptability.
2Reliability
If systems aggregate data from multiple patients to improve statistical significance, then they can better predict population trends, but they lose individual patient specificity
Solution Approach 1:
The patent segments the aggregated patient population into distinct age groups, analyzing each segment separately. By dividing the population into age-specific cohorts and maintaining separate prediction models for each segment, the system preserves individual patient specificity within each age group while still benefiting from the statistical power of aggregated data across multiple patients in the same segment.
3Adaptability or versatility
If prediction models include non-matching characteristics from older patients, then they can provide more comprehensive future profiles, but they increase the complexity of data processing
Solution Approach 1:
The patent performs preliminary filtering and matching of patient characteristics before the main prediction process. By pre-identifying and selecting only those non-matching characteristics that are relevant to the specific patient's age group and medical history, the system reduces the volume of data requiring complex processing while still incorporating comprehensive future profile information, thereby resolving the contradiction between profile completeness and processing complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating a probabilistic healthcare value profile for a patient. One of the methods includes obtaining a current medical history including multiple current characteristics of a first patient having a first current age, obtaining multiple sets of age-correlated characteristics, each set corresponding to a different second patient, determining, for one or more sets of the age-correlated characteristics, that characteristics in at least a threshold portion of the respective age-correlated characteristics match one of the multiple current characteristics of the first patient, and generating a future probabilistic medical profile of the first patient, the future probabilistic medical profile including an aggregation of at least a portion of the age-correlated characteristics in the one or more sets, the aggregation including at least one non-matching age-correlated characteristic that does not match any of the current characteristics of the first patient.