Clinical Decision Support Interface for Context-Aware Patient Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing healthcare systems face challenges in providing timely and contextually relevant clinical decision support across diverse health records systems with varying nomenclatures, especially for managing single and multi-conditions, and there is a need for adaptive and intelligent decision support services that can handle overlapping conditions and multi-diagnoses.
Innovation Solution
A system utilizing software agents and adaptive learning algorithms operates across a distributed cloud-computing platform to provide contextually intelligent decision support, integrating disparate health data sources, and dynamically generating assessments based on caregiver specialty, role, and patient conditions, with capabilities for predictive, preventative, and diagnostic services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If healthcare systems integrate diverse health records systems with varying nomenclatures, then the comprehensiveness of clinical information is improved, but the system complexity increases
Solution Approach 1:
The patent employs software agents as intermediary components that mediate between diverse health records systems with varying nomenclatures and the clinical decision support system. These agents translate and normalize data from different sources, enabling comprehensive information integration without directly coupling the heterogeneous systems, thus managing complexity while maintaining adaptability.
Solution Approach 2:
The system is segmented into autonomous software agents, each responsible for specific functions such as data collection, normalization, analysis, and recommendation generation. This modular architecture allows the system to handle diverse health records systems independently through specialized agents, improving comprehensiveness while managing overall system complexity through functional decomposition.
2Loss of information
If the system provides contextually intelligent decision support tailored to caregiver specialty and role, then the relevance of clinical information is improved, but the processing requirements and system complexity increase
Solution Approach 1:
The patent implements local quality by tailoring the information presented to each caregiver based on their specific specialty, role, and the patient's condition. Software agents analyze the caregiver's profile and deliver customized clinical decision support, ensuring that each user receives only the relevant information needed for their specific context, thereby improving relevance without unnecessarily processing all possible data for every user.
Solution Approach 2:
The system dynamically adapts the information provided to caregivers based on real-time context, including their specialty, role, and the specific clinical situation. This dynamic customization allows the system to adjust processing requirements according to actual needs, providing contextually intelligent support that improves relevance while optimizing processing efficiency by avoiding static, one-size-fits-all approaches.
3Measurement precision
If the system dynamically generates assessments and recommendations based on patient conditions, then the accuracy of clinical decision support is improved, but the computational time and resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and normalizing health data as it is collected from various sources, organizing it into standardized formats before clinical analysis is needed. Software agents perform initial data validation, normalization, and structuring in advance, so that when clinical decisions need to be made, the system can quickly generate accurate assessments and recommendations without performing extensive data preparation at the moment of decision-making.
Data Source
AI summary
Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.