Modular Clinical Analytics Architecture for Transparent Decision Support
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Solution Overview
Problem
Clinical decision support systems lack transparency and explainability, and existing systems fail to seamlessly integrate measurement and lab data for timely decision-making in both hospital and home settings, leading to mistrust among clinicians and inadequate patient monitoring.
Innovation Solution
A scalable modular architecture that allows clinicians to define analytics specifications and operations, enabling the creation of transparent and explainable decision support systems by measuring clinical data, aggregating it, selecting relevant measurements, and performing computations to trigger decision systems, with the option to learn from prior outcomes and integrate with electronic health records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional decision making systems are used, then automation extent is improved, but transparency and explainability deteriorate
Solution Approach 1:
The system segments the decision-making process into distinct components: data collection modules, analytics engine modules, decision triggering modules, and explanation generation modules. Each module operates independently and can be traced, allowing clinicians to understand exactly how automated decisions are reached while maintaining high automation levels.
Solution Approach 2:
The system implements feedback mechanisms that provide clinicians with explanations for automated decisions and allow them to adjust analytics specifications. This feedback loop maintains transparency by showing clinicians the reasoning behind automated actions while preserving automation extent.
2Reliability
If measurement and lab data integration is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The analytics engine is designed as a universal platform that can process multiple data types (measurements and lab data) through a single integrated architecture. This multi-functional design improves reliability by seamlessly integrating diverse data sources while avoiding the complexity of separate specialized systems.
Solution Approach 2:
The system introduces an intermediary data aggregation and processing layer that mediates between diverse data sources (sensors, lab systems) and the decision-making analytics engine. This intermediary layer standardizes data formats and handles integration complexity centrally, improving reliability without proportionally increasing overall system complexity.
3Adaptability or versatility
If clinician defined analytics are enabled, then adaptability is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides pre-configured analytics templates and default specifications that clinicians can accept without customization. This preliminary action maintains ease of operation for routine cases while allowing adaptability when clinicians choose to customize analytics specifications for complex scenarios.
Solution Approach 2:
The analytics specification interface is designed to be dynamic, allowing clinicians to adjust parameters interactively with real-time feedback. This dynamic design maintains ease of operation through intuitive controls while providing full adaptability for customized analytics definitions.
Data Source
AI summary
A method and system of a scalable modular architecture for enabling clinicians to define clinical inputs, operators, and notifications on a per patient and enterprise basis for screening any pathological condition per the clinical practice