Acute Care Dashboard with Preorganized Diagnostic Pathways
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Solution Overview
Problem
Existing automated differential diagnosis systems have not been accepted in emergency medical settings due to their interference with critical care processes and the inaccessibility of patient medical history during acute conditions, leading to potential delays in treatment.
Innovation Solution
A system integrating sensors to monitor patient physiology, a user interface, and a processor to present customizable input elements and physiological data, allowing caregivers to efficiently perform differential diagnosis during emergencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated differential diagnosis systems are implemented in emergency medical settings, then diagnostic accuracy is improved, but treatment process efficiency deteriorates due to interference with critical care processes
Solution Approach 1:
The system performs preliminary actions by pre-organizing diagnostic pathways, treatment protocols, and decision support information before emergencies occur. During acute care, the system simply retrieves and presents pre-prepared diagnostic options and treatment pathways, eliminating the need for complex real-time analysis that would interfere with critical care processes.
Solution Approach 2:
The system acts as an intermediary between the caregiver and the complex diagnostic decision-making process. It translates patient data into pre-organized diagnostic pathways and treatment recommendations, allowing caregivers to maintain focus on critical care while receiving structured diagnostic support without direct interference in the treatment workflow.
2Measurement precision
If comprehensive patient medical history is accessed during acute conditions, then diagnostic accuracy is improved, but treatment time increases due to data retrieval delays
Solution Approach 1:
The system performs preliminary action by pre-fetching and organizing patient medical history, lab results, and clinical data into structured formats before they are needed during acute care. This allows immediate access to relevant historical information without retrieval delays when time-critical decisions must be made.
Solution Approach 2:
The system applies local quality by providing only the specific portions of patient medical history and data that are relevant to the current acute condition, rather than presenting all available data. This targeted approach improves diagnostic accuracy with the necessary historical context while minimizing time loss by excluding irrelevant information.
3Adaptability or versatility
If multiple simultaneous symptoms are analyzed using Bayesian networks, then diagnostic comprehensiveness is improved, but system complexity increases making it overwhelming for users
Solution Approach 1:
The system segments the complex analysis of multiple simultaneous symptoms into distinct diagnostic pathways and modular components. Each symptom and clinical finding is processed through organized segments of the diagnostic algorithm, allowing comprehensive analysis to be divided into manageable steps that reduce perceived system complexity while maintaining diagnostic thoroughness.
Solution Approach 2:
The system serves as an intermediary that handles the computational complexity of analyzing multiple simultaneous symptoms through Bayesian networks and other algorithms. It translates complex probabilistic calculations into simplified diagnostic recommendations and pathway suggestions, maintaining diagnostic comprehensiveness while shielding users from the underlying system complexity.
Data Source
AI summary
A medical system according to embodiments of the present invention includes at least one sensor configured to monitor physiological status of a patient and to generate sensor data based on the physiological status, a user interface device, a processor communicably coupled to the user interface device, the processor configured to: present via the user interface device an array of two or more possible input elements, the input elements each comprising a class of patients or a diagnosis and treatment pathway; receive a selected input element based on a user selection among the two or more possible input elements; acquire the sensor data and process the sensor data to generate physiological data; and present via the user interface screen the physiological data according to a template that is customized for the selected input element.