Healthcare Analytics Management System with Closed-Loop Feedback
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Solution Overview
Problem
Current healthcare analytics solutions lack a scalable and traceable mechanism for managing analytics assets, leading to manual and ad hoc processes for deriving insights, with no organized way to reuse, share, or adapt analytics models based on new evidence or data sources, resulting in inefficiencies and a lack of continuous improvement.
Innovation Solution
A cloud-based healthcare analytics management system with a closed-loop mechanism that includes a development sub-system for creating analytics pipelines and models, a deployment module for model deployment, a monitoring module for performance tracking, and a feedback module for continuous improvement, enabling traceability, reusability, and adaptability of analytics assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual and ad hoc processes are used for deriving healthcare insights, then flexibility in analysis is maintained, but productivity and scalability deteriorate
Solution Approach 1:
The system segments the healthcare analytics process into distinct modular components including data collection modules, data processing modules, analytics model development modules, deployment modules, and monitoring modules. Each module handles specific tasks independently, enabling parallel processing and improving productivity while maintaining flexibility through modular architecture that allows easy reconfiguration for different analysis needs.
Solution Approach 2:
The analytics platform is designed as a universal system capable of performing multiple healthcare analytics functions through a common infrastructure. The system can handle various types of healthcare data (clinical, financial, operational), support different analytics models (predictive, descriptive, prescriptive), and serve multiple healthcare consumers simultaneously, achieving both scalability and adaptability through multi-functionality.
2Speed
If analytics models are developed and deployed without organized management, then rapid deployment is achieved, but traceability and continuous improvement deteriorate
Solution Approach 1:
The system implements comprehensive feedback mechanisms through monitoring modules that continuously track analytics model performance after deployment. The monitoring modules collect performance data, compare it against expected outcomes, and feed this information back to the development modules. This enables traceability of model performance over time and facilitates continuous improvement while maintaining rapid deployment capabilities through automated feedback loops.
Solution Approach 2:
The system performs preliminary actions by establishing organized management frameworks, version control systems, and performance baseline definitions before analytics models are deployed. Analytics assets are registered, documented, and configured with performance metrics in advance, ensuring traceability is built-in from the start rather than added afterward, which enables both rapid deployment and comprehensive tracking.
3Adaptability or versatility
If analytics assets are not designed for reusability, then customization for each healthcare consumer is achieved, but device complexity and development time worsen
Solution Approach 1:
The system applies local quality by allowing analytics assets to have both standardized core components and customizable local adaptations. The modular architecture enables different levels of customization - from fully reusable standard analytics models to highly customized local implementations - depending on the specific needs of each healthcare consumer. This reduces overall system complexity by avoiding the need to build entirely custom solutions for each consumer while maintaining necessary adaptability.
Data Source
AI summary
A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to a model monitoring module of the healthcare analytics operation sub-system detecting a performance deviation of the deployed healthcare analytics model for performance deviation from the set of business needs for the healthcare analytics client, a model feedback module of the healthcare analytics operation sub-system determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.