Patient Data Sorting System for Cardiologist Prioritization
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Solution Overview
Problem
Cardiologists face challenges in efficiently prioritizing and identifying critical patient data among multiple patients due to scattered health-related information across different data fields and images, leading to time-consuming, unreliable, and error-prone selection processes, which may result in delayed attention to critical health situations.
Innovation Solution
A computer-implemented method and system that loads patient data records from a health data processing system, determines deviations from reference parameter values, and sorts patient data records based on criticality metrics to generate a sorted patient list, enabling immediate focus on critical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cardiologist manually evaluates patient data records by going through entire details, then comprehensive information can be reviewed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent extracts critical health information from scattered data fields and images by using AI-based natural language processing and computer vision. The system automatically identifies and extracts vital parameters (temperature, blood pressure, heart rate, imaging findings) from unstructured data sources, separating essential information from redundant data to enable rapid cardiologist review.
Solution Approach 2:
The patent introduces an AI-based information processing system as an intermediary between data collection devices and the cardiologist. This intermediary automatically processes, consolidates, and prioritizes patient data from multiple sources (medical devices, imaging systems, electronic health records), presenting only critical information to the cardiologist in a structured format.
2Quantity of substance
If health relevant information is spread across various images and data fields, then complete patient data is available, but the cardiologist cannot quickly identify critical patients
Solution Approach 1:
The patent performs preliminary action by having the AI system continuously monitor and pre-process patient data in real-time. The system proactively identifies critical changes in health parameters and pre-ranks patients by urgency before the cardiologist needs to review them, eliminating the need for manual searching through complete datasets.
Solution Approach 2:
The patent replaces the mechanical manual review process with automated AI-based information extraction and prioritization systems. Natural language processing algorithms analyze medical notes, computer vision processes imaging data, and machine learning models prioritize patients based on criticality, substituting human cognitive processing with automated computational systems.
3Ease of operation
If the cardiologist uses heuristic approach to sort patients, then some prioritization is achieved, but the process becomes error-prone and unreliable
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously learns from cardiologist interactions and clinical outcomes. The system provides feedback by adjusting its prioritization algorithms based on actual patient presentations and outcomes, improving accuracy over time while maintaining ease of use through automated decision support.
Solution Approach 2:
The patent changes the parameters for patient prioritization from subjective heuristic judgments to objective, multi-parameter clinical criteria. The AI system automatically evaluates multiple health parameters (vital signs, lab values, imaging findings) simultaneously, weighting them according to clinical guidelines and patient-specific factors to produce reliable prioritization rankings.
4Duration of action of moving object
If critical patients are not identified immediately, then more time is available for comprehensive review, but patient outcomes deteriorate due to delays
Solution Approach 1:
The patent performs preliminary identification of critical patients through continuous AI monitoring that detects critical changes in real-time. The system proactively alerts the cardiologist to urgent cases before they are reviewed comprehensively, enabling immediate intervention while still allowing time for thorough assessment of critical findings.
Data Source
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AI summary
A computer-implemented method and system for analysing efficiently patient data of patients comprising the steps of: loading (S1) by a user-client device(5) of a health investigating user (U) at least one patient data record, PDREC, of each patient (P) forming part of a selected group of patients (P) assigned to the client device (5) of the respective health investigating user (U) for medical investigation of the assigned group of patients (P) from a transitionary database (2) of a health data processing system (1), wherein each loaded patient data record, PDREC, of a patient (P) of said assigned group of patients comprises measured parameter values of a selected group of predefined vital health related parameters, vp, measured for the respective patient (P) by means of medical devices (3) during previously performed medical investigations of the respective patient (P); determining (S2) by a processor of the user-client device (5) of the health investigating user (U) an extent of deviations of parameter values of the predefined vital health related parameters, vp, within each loaded patient data record, PDREC, of a patient from reference parameter values; and sorting (S3) by the processor of the user-client device (5) of the health investigating user (U) the loaded patient data records, PDRECs, of the group of patients (P) assigned to the health investigating user (U) according to patient list indices to generate automatically a sorted patient record list, SPRL, of the assigned patients (P) depending on the determined extent of deviations of the predefined vital health related parameters, vp, and/or depending on a determined number of vital health related parameters falling out of range for performing a data analysis of patient data included in the patient data records, PDRECs, of the generated sorted patient record list, SPRL.