Decentralized Therapy Recommendation via Blockchain Efficacy Validation

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Solution Overview

Problem

Existing systems for evaluating therapy efficacy rely on inaccurate, inconsistent, and non-anonymous data, compromising user confidentiality and lacking meaningful metrics, which leads to inefficient processing and resource utilization.

Innovation Solution

A decentralized predictive recommendation system utilizing crowd-sourced therapy efficacy data and a blockchain/distributed ledger network to validate and store data anonymously, ensuring statistical significance and immutability, while training a therapy efficacy model to assign scores based on user attributes and refine recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If centralized data collection is used to evaluate therapy efficacy, then data processing efficiency is improved, but user confidentiality and data accuracy deteriorate due to inaccurate, inconsistent, and non-anonymous data

Engineering Contradiction:

Improvedata processing efficiencyVSAvoiduser confidentiality and data accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

A blockchain-based intermediary layer is introduced between users and the recommendation system. This intermediary enables anonymous data submission and verification without requiring centralized data collection, thus maintaining user confidentiality while ensuring data accuracy through cryptographic verification and consensus mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables users to self-verify and submit their therapy efficacy data anonymously to the blockchain network. Users independently validate and record their own data without requiring centralized verification, which improves both user confidentiality and data accuracy while reducing processing overhead.

Inventive Principle:

Principle #25Self-service

2Speed

If traditional data validation methods are used, then processing speed is improved, but data quality and statistical significance worsen due to noise and inconsistency

Engineering Contradiction:

Improveprocessing speedVSAvoiddata quality and statistical significance

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system implements a feedback mechanism where therapy efficacy data is continuously validated against the blockchain record and consensus rules. This feedback loop ensures that only statistically significant and consistent data is accepted, improving data quality without significantly impacting processing speed due to the efficient consensus algorithms.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Data validation and verification are performed preliminarily before data is added to the blockchain through consensus mechanisms. This preliminary action ensures that only high-quality, statistically significant data enters the system, preventing noise accumulation while maintaining processing efficiency.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If centralized recommendation systems are used, then resource utilization is improved, but system reliability and user trust deteriorate

Engineering Contradiction:

Improveresource utilizationVSAvoidsystem reliability and user trust

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The centralized recommendation system is segmented into a decentralized network of nodes that collectively maintain the blockchain and generate recommendations. This segmentation distributes trust across multiple independent entities, improving system reliability and user trust while maintaining efficient resource utilization through parallel processing capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The blockchain network serves multiple functions simultaneously: it stores therapy efficacy data, validates recommendations, generates new recommendations, and ensures user confidentiality. This multi-functionality eliminates the need for separate centralized systems, improving both resource utilization and system reliability through a unified decentralized architecture.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12002569B2Generating multi-dimensional recommendation data objects based on decentralized crowd sourcing

Publication Date: 2024.06.04 OPTUM TECH INC

AI summary

Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes. The predictive recommendation model is trained based at least in part on trusted efficacy blocks of a distributed ledger, where the trusted efficacy blocks are added to the distributed ledger when a number of therapy efficacy transaction blocks associated with a given therapy identifier and having a therapy efficacy score exceeding a therapy efficacy score threshold meets or exceeds a therapy efficacy transaction block threshold. The example apparatus may further transmit, to the external computing device, a recommendation data object configured for rendering for display via a display device of the external computing device, where the recommendation data object comprises one or more of the one or more therapy identifiers.