Predictive Program Matching for Faster Philanthropic Aid Applications

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Solution Overview

Problem

Patients face difficulties in identifying appropriate philanthropic programs for medical expense coverage and navigating cumbersome application processes, leading to unpaid out-of-pocket costs due to a lack of efficient mechanisms for accessing and understanding suitable funding options.

Innovation Solution

A system utilizing a cloud-based platform with a data mesh architecture that integrates machine learning algorithms to analyze program criteria and patient data, predicting the likelihood of program acceptance and providing task prioritization for efficient application processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If patients manually identify and apply for philanthropic programs, then they can access potential funding, but the process is time-consuming and complex

Engineering Contradiction:

Improveprogram identification efficiencyVSAvoidapplication processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and organizing patient data, pre-identifying suitable philanthropic programs, and preparing application materials before the patient actually applies. This includes retrieving patient information, matching it with program criteria, and generating pre-filled application forms, thereby significantly reducing the time and effort required during the actual application process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by allowing patients to automatically input their medical expense data, which is then automatically processed to identify suitable programs and generate applications. The system serves itself by autonomously matching patient needs with program requirements and managing the application workflow without requiring extensive manual intervention from patients or administrators.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive program data is collected and analyzed, then matching accuracy improves, but data processing complexity increases

Engineering Contradiction:

Improveprogram matching accuracyVSAvoiddata processing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The data processing system is segmented into distinct modular components: data collection modules, data cleaning modules, matching algorithms, and result generation modules. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while achieving high matching accuracy through coordinated operation of these specialized segments.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary components such as data normalization layers and standardized interface protocols that mediate between diverse data sources and the core matching algorithm. These intermediaries simplify data integration by converting various data formats into a unified structure, reducing processing complexity while preserving matching accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250363530A1Systems and methods for iterative predictive determinations

Publication Date: 2025.11.27 ATLAS LABS INC

AI summary

The subject disclosure relates to systems, devices, and methods for determining a match between program criteria of a philanthropic aide program and data via a data mesh by employing predictive determination tools.