Risk Metrics Platform for Drug Development Data Synthesis
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Solution Overview
Problem
Pharmaceutical and biotechnology companies face challenges in assessing composite risk for pharmaceutical drugs due to fragmented and unorganized data, leading to inefficient strategic decision-making and high failure rates in clinical trials.
Innovation Solution
A risk metrics information platform, including a reimbursement risk tracker application and clinical trial tracker, that aggregates, synthesizes, and presents data to facilitate better understanding of pricing and reimbursement market risk, converting unstructured data into structured formats for intuitive visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is gathered from multiple public domain sources to assess composite risk, then the completeness of risk information is improved, but the complexity of data organization and processing increases
Solution Approach 1:
The patent segments data from multiple public domain sources into distinct categories (clinical trial data, reimbursement data, regulatory data, patent data) and processes each category through specialized modules. This segmentation allows the system to manage complex multi-source data without overwhelming the overall processing architecture, as each data type is handled according to its specific requirements.
Solution Approach 2:
The patent introduces an intermediary data processing layer that sits between raw public domain data and the final risk assessment output. This intermediary layer includes data cleaning, normalization, and integration modules that transform fragmented information from various sources into a unified structured format, reducing the complexity burden on the final analysis system.
2Productivity
If qualitative expert opinion is used to assess risk, then the speed and cost of analysis is improved, but the accuracy and objectivity of risk prediction deteriorates
Solution Approach 1:
The patent implements self-service automation where the system automatically retrieves, processes, and analyzes risk data from multiple sources without requiring manual expert intervention for each assessment. The automated data processing pipelines, machine learning models, and algorithmic risk calculations enable the system to perform comprehensive risk assessments rapidly and objectively, eliminating the trade-off between speed and accuracy that plagues manual expert analysis.
3Quantity of substance
If clinical trial data is mined from unorganized sources, then the breadth of data coverage is improved, but the efficiency of data retrieval and analysis deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing clinical trial data from multiple sources into a standardized format before actual analysis needs to occur. The system establishes predetermined data collection protocols, pre-cleanses data from known sources, and maintains pre-organized databases of clinical trial information, thereby enabling rapid retrieval and analysis when risk assessments are needed without sacrificing comprehensive data coverage.
Data Source
AI summary
A platform for implementing a method of aggregating and contextualizing metrics relevant to developing, launching and marketing a target therapeutic or related therapy. The method of processing the metrics includes retrieving data related to the target from one or more data sources and synthesizing the retrieved target data in a contextual manner and creating synthesized target data by converting unstructured data into structured data including an event, wherein an event comprises a corpus of data from a single source on a single date. The synthesized target data is stored in a target specific database and a user may query the database regarding the target, where the query includes at least one search parameter. The user query is processed against the synthesized target data in the target specific database and the results are presented to the user.