ML Platform for Targeting Medical Professionals
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Solution Overview
Problem
Identifying and educating the right medical practitioners about new pharmaceuticals is a nebulous process, often relying on arbitrary or convoluted systems, as existing methods lack clear answers on which doctors to target and how to effectively communicate the information.
Innovation Solution
A machine learning platform that uses claims and lab data to identify relevant doctors and determine the most effective channels and content for education, tailoring communication to individual preferences and needs, with a focus on immediate versus ongoing needs based on data lag considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional human-based identification systems are used to target doctors for education, then the process can be manually controlled and flexible, but the system becomes arbitrary, convoluted, and lacks clear answers on which doctors to target
Solution Approach 1:
The patent replaces manual human-based identification systems with an automated machine learning system that uses claims data and lab data to automatically identify relevant doctors and determine effective communication channels, eliminating the need for arbitrary manual selection while reducing system complexity through algorithmic automation
2Measurement precision
If comprehensive data analysis is performed to identify the right doctors and communication channels, then the precision of doctor selection improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing claims data and lab data in a structured format, enabling rapid querying and analysis when needed. The machine learning models are pre-trained on historical data, allowing them to quickly identify relevant doctors without requiring time-consuming analysis during actual use
3Reliability
If tailored communication strategies are developed for each doctor based on detailed analysis, then the effectiveness of education improves, but the resource allocation becomes more complex
Solution Approach 1:
The system changes parameters by analyzing different aspects of doctor behavior and preferences (such as communication channel preferences, content preferences, and engagement patterns) to automatically generate tailored communication strategies. The machine learning models process multiple parameters simultaneously and output optimized communication recommendations, reducing the manual complexity of creating personalized strategies while maintaining high effectiveness
Data Source
AI summary
Introduced herein is a platform for determining machine learning communication/educating strategy. For example, the communication and educating strategy applies to introducing medical professionals to new, specialized medications. Medical professionals are categorized and/or characterized by their location, specialty, and therapy practice using claims data and labs data. Once a given medial professional is identified as one whom the specialized medication is useful to, an effectiveness model identifies a recommended channel and manner of educating the medical professional. The effectiveness model identifies one or more business metrics to be driven by a communication plan; generating one or more response functions of the business metrics by performing a machine learning process on a marketing dataset; and optimizing a spending subject of the marking plan subject to constraints to generate a marketing strategy based on multiple decision variables.