AI Biomarker Trend Analysis for Surgical Risk Notification
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Solution Overview
Problem
Healthcare providers are overwhelmed by the vast amount of data and biomarkers produced by sensing systems, making it difficult to diagnose surgical complications effectively.
Innovation Solution
A computing system and method that uses risk assessment to provide notifications by receiving biomarker data from sensing systems, determining patient outcomes, and adjusting calculated probabilities to escalate notifications based on predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensing systems continuously monitor and report all biomarkers, then comprehensive patient data is available, but healthcare providers become overwhelmed and distracted by irrelevant notifications
Solution Approach 1:
The patent introduces an artificial intelligence intermediary that processes raw biomarker data and generates synthesized patient summaries. This intermediary filters and translates complex sensor data into clinically relevant insights, allowing providers to access comprehensive monitoring data without being overwhelmed by raw data volume or irrelevant notifications.
Solution Approach 2:
The system implements feedback mechanisms where the AI continuously monitors biomarker trends and adjusts notifications based on clinical significance. The system learns from provider interactions and patient outcomes to refine which notifications are generated, creating a feedback loop that reduces irrelevant alerts while maintaining comprehensive monitoring coverage.
2Measurement precision
If multiple biomarkers are monitored simultaneously, then diagnostic accuracy improves, but data complexity and processing burden increase
Solution Approach 1:
The patent merges multiple biomarker data streams into unified patient summaries generated by AI. Instead of presenting separate biomarker readings that require individual interpretation, the system combines them into synthesized clinical narratives that maintain diagnostic accuracy while reducing processing complexity for providers.
Solution Approach 2:
The system transforms raw biomarker parameters into clinically meaningful metrics through AI processing. By changing the parameter representation from raw sensor values to synthesized clinical insights, the system maintains measurement precision while reducing the complexity of data interpretation and provider workload.
3Loss of information
If all biomarker data is presented to providers, then complete information is available, but notification quality decreases due to irrelevant alerts
Solution Approach 1:
The patent extracts only the clinically relevant information from complete biomarker datasets using AI analysis. The system takes out and highlights only those data points and trends that have genuine clinical significance, separating signal from noise while maintaining information completeness for those extracted elements.
Solution Approach 2:
The AI performs preliminary analysis and filtering of biomarker data before presenting it to providers. By pre-processing the data to identify and flag only clinically significant findings, the system maintains complete information availability while improving notification reliability through advance filtering of irrelevant alerts.
Data Source
AI summary
A computing system and/or a method may be provided for using a risk assessment to provide a notification. The computing system may comprise a processor. The processor may be configured to perform the method. A biomarker may be received for a patient from a sensing system. A data collection that includes pre-surgical data may be received for a patient. A probability of a patient outcome due to a surgery performed on the patient may be determined using the biomarker and the data collection. A notification may be sent to a user. The notification may indicate that the probability of the surgical complication may exceed a threshold.