Dynamic Treatment Plan Evaluation via Knowledge Graph Analysis
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Solution Overview
Problem
Current methods for determining the efficacy of medical therapies are time-consuming, expensive, and biased, with healthcare providers struggling to keep up with the rapid pace of new treatments due to the overwhelming volume of published literature, leading to outdated guidelines and potential suboptimal patient outcomes.
Innovation Solution
A system that generates and evaluates treatment plans based on predefined templates, using a knowledge graph to analyze real-world evidence and published clinical studies, allowing for the identification and scoring of potential new therapies, thereby enabling healthcare providers to make informed decisions about up-to-date treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of published literature is performed by subject-matter experts, then treatment guidelines can be established, but the process becomes time-consuming and expensive
Solution Approach 1:
An automated system acts as an intermediary between published literature and healthcare providers, performing the manual review function. The system ingests, processes, and evaluates treatment plans using algorithms and knowledge graphs, replacing the need for human experts to manually review each document while maintaining evaluation accuracy.
Solution Approach 2:
The manual mechanical process of expert review is replaced with an automated computational system. The system uses natural language processing, knowledge graphs, and algorithms to automatically ingest literature, evaluate treatment plans, and generate recommendations, eliminating the time-consuming manual review process.
2Adaptability or versatility
If comprehensive literature review is conducted to identify new therapies, then treatment options can be updated, but the cost increases significantly
Solution Approach 1:
The system performs self-service by automatically ingesting and processing literature without requiring human expert intervention for each review. The automated algorithms continuously scan, parse, and evaluate new publications, enabling the system to identify new therapies independently while reducing the resource cost of comprehensive literature review.
Solution Approach 2:
The system changes the parameters of literature review by using automated computational methods instead of manual human review. This transformation from human-centric to algorithm-centric processing dramatically reduces the cost and resource requirements while maintaining or improving the ability to identify new therapies.
3Ease of operation
If accepted treatment plans are used without modification, then implementation is simple, but potential superior treatments are missed
Solution Approach 1:
The system introduces dynamics to previously static treatment plans by automatically generating and evaluating modifications. The system takes accepted treatment plans and dynamically creates alternative versions by adding, removing, or modifying treatment stages and options, then evaluates these modifications to identify superior treatments while maintaining ease of implementation through automated scoring.
Solution Approach 2:
The system performs preliminary action by pre-evaluating multiple modified treatment plans before clinical implementation. By automatically generating modifications and scoring them in advance using knowledge graphs and literature analysis, the system identifies superior treatments ahead of time, allowing healthcare providers to select optimized plans without compromising implementation simplicity.
4Measurement precision
If manual evaluation of treatment plans is performed, then institutional criteria can be applied, but the process cannot keep pace with new publications
Solution Approach 1:
The manual mechanical evaluation process is replaced with automated computational evaluation. The system uses algorithms to automatically apply institutional criteria to treatment plans, ingesting and processing publications at machine speed while maintaining the precision of criteria-based evaluation that would be difficult to achieve manually at scale.
Data Source
AI summary
Techniques for evaluating dynamically modified plans are provided. A selection of a treatment plan template is received, where the treatment plan template specifies a plurality of treatment stages, where each treatment stage defines a plurality of treatment options. A plurality of modifications to the treatment plan template is generated. It is determined, for each respective modification of the plurality of modifications, whether the respective modification is permissible, based on one or more predefined institutional criteria. Upon determining that a first modification of the plurality of modifications is permissible, a first treatment plan is generated based on the first modification to the treatment plan template. Further, a first predicted efficacy measure is generated for the first treatment plan based on analyzing a knowledge graph. Finally, the first treatment plan is provided, along with at least an indication of the first predicted efficacy measure.