Clinical Decision Support Using Waveform Pattern Matching
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Solution Overview
Problem
Inconclusive or ambiguous physiological waveforms from electrophysiological monitoring, such as ECG and EEG, often fail to provide clear diagnostic information, as clinicians struggle to identify specific criteria for cardiac or brain health conditions.
Innovation Solution
A system and method for clinical decision support that compares patient physiological waveform data to a database of previously-recorded waveform data using a pattern recognition algorithm, accounting for morphology and rhythm, to identify matches and generate a result set including clinical interpretations and potential diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional electrophysiological monitoring methods are used, then diagnostic information can be obtained, but inconclusive or ambiguous results occur when waveforms do not meet specific diagnostic criteria
Solution Approach 1:
The system pre-processes and stores waveform data from multiple patients in a database before clinical use. When a new patient's waveform is analyzed, the system compares it against this pre-existing database of waveform patterns and their associated outcomes, enabling reliable diagnosis even when traditional criteria are not met.
Solution Approach 2:
The system creates digital copies of waveform patterns from numerous patients and stores them in a database. By comparing a new patient's waveform against these stored copies, the system can identify matching patterns and provide diagnostic insights without requiring the waveform to meet rigid predetermined criteria.
2Productivity
If clinicians manually analyze physiological waveforms, then diagnostic interpretation can be performed, but time-consuming analysis and potential for inconclusive results occur
Solution Approach 1:
The system replaces the manual mechanical analysis process with an automated computer-based system. The processor automatically compares waveform data against the database using algorithmic pattern recognition, eliminating the time-consuming manual review process while maintaining or improving diagnostic accuracy.
Solution Approach 2:
The system enables self-service diagnostic support by automatically performing waveform analysis and pattern matching without requiring extensive manual intervention from clinicians. The computer system independently processes the waveform data, compares it against the database, and generates diagnostic suggestions, freeing clinicians from tedious manual analysis tasks.
Data Source
AI summary
A system for clinical decision support includes a database of previously-recorded waveform data from comparator-patients and a comparator module. The comparator module receives physiological waveform data for a patient and identifies a pattern in the patient's physiological waveform data, wherein the pattern accounts for a morphology and a rhythm of the patient's physiological waveform. The comparator module then compares the patient's physiological waveform data to the previously-recorded waveform data using a pattern recognition algorithm to detect the pattern in the previously-recorded waveform data to identify one or more matches. The comparator module further generates a result set based on the one or more matches.