Deep Learning Influence Attribution Model for Physician Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for measuring peer influence in networks, such as those in the medical industry, are incomplete and unable to accurately quantify the influence of thought leaders on the adoption of new therapies, relying on assumptions rather than evidence and failing to capture leader-follower connections and sales data effectively.
Innovation Solution
The use of artificial intelligence (AI) and machine learning (ML) to integrate social network analytics and big data, creating a multi-relational network that decomposes treatment decisions into peer influence and control factor influences, allowing for the identification of thought leaders and their impact on prescribing behaviors within healthcare provider networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional communication and marketing strategies are used to identify influential physicians, then early adoption and self-prescribing metrics are easily observable, but peer influence cannot be directly observed or measured
Solution Approach 1:
The patent uses an influence attribution model as an intermediary to connect observable prescribing behaviors with unobservable peer influence. The model acts as a mediator that translates visible sales data and network structures into hidden influence relationships, allowing indirect measurement of peer influence through the lens of treatment decisions and network positions.
Solution Approach 2:
The patent replaces traditional mechanical measurement approaches (direct observation of peer interactions) with a computational system using machine learning models. The influence attribution model substitutes physical observation with algorithmic inference, using trained models to predict and quantify peer influence from pattern recognition in prescribing data and network structures.
2Adaptability or versatility
If multiple methodologies are combined to get a complete picture of influence, then more comprehensive data is obtained, but the approaches remain disjointed and not aligned
Solution Approach 1:
The patent merges multiple previously separate methodologies (network analysis, prescribing behavior analysis, sales data analysis) into a single unified influence attribution model. The model integrates these different data sources and analytical approaches into one coherent framework, eliminating the disjointed nature of separate methods while maintaining their individual strengths.
Solution Approach 2:
The influence attribution model serves multiple functions simultaneously: it identifies influential physicians, measures peer influence magnitude, analyzes network structures, and predicts prescribing behaviors. This multi-functional approach replaces the need for multiple separate methodologies, providing comprehensive influence measurement through a single versatile system.
3Measurement precision
If assumption-based quantification methodologies are used, then simple models are maintained, but evidence-based measurement of peer influence is not achieved
Solution Approach 1:
The patent incorporates feedback mechanisms where the influence attribution model is trained on observed prescribing behaviors and network data, then uses its predictions to identify influential physicians, whose identified influence is fed back into the model for refinement. This iterative feedback loop transforms assumption-based modeling into evidence-based measurement, continuously improving accuracy through real-world data validation.
Solution Approach 2:
The patent changes the fundamental parameters of the analysis model from static assumptions to dynamic, data-driven parameters. The model adjusts influence weights, network connection strengths, and prescribing behavior patterns based on actual observed data rather than fixed assumptions, enabling evidence-based measurement while adapting model complexity to match data availability.
4Ease of operation
If network centrality is used as a proxy for peer influence, then measurement is simplified, but the approach is incomplete and misses actual influence measures
Solution Approach 1:
The patent segments the single metric of network centrality into multiple distinct components (degree centrality, betweenness centrality, closeness centrality) and further divides influence measurement into separate dimensions (peer influence, self-prescribing, network position). This segmentation allows the model to move beyond the incomplete single-proxy approach while maintaining operational manageability through structured analysis.
Solution Approach 2:
The patent adds new dimensions to the influence measurement by incorporating temporal dynamics (changes in prescribing behavior over time), multiple network layers (different types of professional connections), and quantitative influence magnitudes. This dimensional expansion transforms the two-dimensional network centrality proxy into a multi-dimensional evidence-based influence measurement system.
Data Source
AI summary
Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.