Healthcare Triage System with Modular Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current healthcare systems lack efficient triage management capabilities, as they fail to accurately assign urgency levels and provide timely recommendations for patient treatment based on comprehensive patient data.
Innovation Solution
A healthcare system that retrieves multiple parameters from various databases, including EMR systems, IoT devices, and monitoring databases, to determine patient triage recommendations, probable diagnoses, and suitable treatments, and sends alerts to healthcare providers when pre-determined thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive patient data from multiple databases is retrieved and analyzed, then triage recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments patient data retrieval and analysis into distinct modules: a data retrieval module that collects parameters from multiple databases (EMR, claims, utilization data), a processing module that analyzes the retrieved data, and a recommendation module that generates triage suggestions. This segmentation allows comprehensive data analysis while managing system complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary processing layer between raw patient data and triage recommendations. This intermediary module retrieves, standardizes, and analyzes data from multiple heterogeneous databases before generating recommendations, thereby improving accuracy while abstracting the complexity of multi-source data integration from the core triage logic.
2Loss of time
If real-time patient data analysis is performed, then triage timeliness is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-retrieving and storing patient data from multiple databases before triage is needed. Historical patient data, claims information, and utilization patterns are pre-loaded into accessible storage, enabling rapid real-time analysis when triage recommendations are required without excessive computational resource consumption during critical moments.
Solution Approach 2:
The system applies local quality by analyzing only the specific patient parameters and data elements relevant to the current triage decision rather than processing all available patient data uniformly. The retrieval and analysis focus on locally relevant data quality needed for the specific clinical scenario, reducing unnecessary computational resource consumption while maintaining timeliness.
3Loss of information
If multiple data sources are integrated, then information completeness is improved, but data integration complexity increases
Solution Approach 1:
The system implements a universal data interface layer that can retrieve and standardize information from multiple heterogeneous data sources including EMR systems, claims databases, and utilization repositories. This multi-functional retrieval module handles diverse data formats and structures through a unified interface, ensuring information completeness while managing data integration complexity through standardization.
Data Source
AI summary
According to an aspect of the present disclosure, a healthcare system retrieves multiple parameters relating to a patient from different databases, the parameters including current and historical data relating to the patient. Upon receiving symptoms of a medical problem of the patient, the healthcare system determines based on the symptoms and the multiple retrieved parameters, recommendations to assist in the triage of the patient and provides the recommendations to a healthcare provider to assist in the triage of the patient. Accordingly, the healthcare system is configured to aid triage management by the healthcare system.