Heart Failure Risk Score Differentiation via Temporal Lookback
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Solution Overview
Problem
Current medical devices struggle to accurately differentiate between high and medium heart failure risk levels, leading to inefficient prioritization and treatment of patients, as they often fail to distinguish between ongoing and new events, which can result in unnecessary hospitalizations and increased healthcare costs.
Innovation Solution
A method is introduced that differentiates heart failure risk scores by using a monitoring device to receive and analyze patient metrics, determining daily risk scores, and generating alerts based on the proximity of maximum risk scores within a lookback window, allowing for the differentiation of alerts as high/medium or high/medium ongoing alerts, and further categorizing risk status as high, high new, medium, and medium new.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional heart failure monitoring methods are used to generate risk level alerts, then clinicians can identify patients at risk of hospitalization, but clinicians cannot efficiently prioritize patients because they cannot distinguish between ongoing and new events
Solution Approach 1:
The patent segments the heart failure risk monitoring system into two distinct components: (1) a risk level determination module that calculates high/medium/low risk scores based on patient metrics, and (2) an event status determination module that classifies alerts as either 'ongoing' or 'new' based on temporal proximity of maximum risk scores. This segmentation enables clinicians to separately evaluate risk magnitude and event status, thereby improving both measurement precision and ease of patient prioritization.
2Loss of information
If conventional monitoring methods are used without event status differentiation, then the system can generate risk alerts, but the system cannot provide timely interventions because the nature of events (ongoing vs. new) is not distinguished
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing maximum heart failure risk scores within a defined lookback window (e.g., past 7 days) before clinician review. When a new alert is generated, the system has already determined whether it represents a new event or an ongoing event by comparing against these pre-computed maximums. This preliminary processing eliminates the need for clinicians to manually analyze historical data, thereby preventing information loss and reducing intervention response time.
3Loss of energy
If traditional risk scoring methods are used, then hospitalization costs can be reduced by identifying at-risk patients, but costs remain high because unnecessary hospitalizations occur due to inability to differentiate event severity
Solution Approach 1:
The patent applies parameter changes by introducing a temporal parameter (time since last maximum risk score) to the traditional risk assessment model. Instead of solely relying on risk magnitude thresholds, the system incorporates the duration and recency of elevated risk scores as additional parameters. This enables differentiation between transient fluctuations and sustained high-risk states, allowing clinicians to avoid unnecessary hospitalizations for transient events while maintaining appropriate intervention for sustained high-risk conditions, thereby reducing healthcare resource consumption.
Data Source
AI summary
A method and device for differentiating heart failure risk scores that includes determining receipt of a current data transmission and acquiring patient metrics from a remote monitoring device, determining a daily heart failure risk score for each day occurring during a time period from a previous received data transmission to the current received data transmission based on the acquired patient metrics, and determining a maximum daily heart failure risk score of the determined daily heart failure risk scores during a lookback window prior to the current received data transmission. A heart failure risk score is determined for the received data transmission based on the determined maximum daily heart failure risk score, and a heart failure risk score alert is determined for the received data transmission based on the proximity of the determined maximum heart failure risk score and the current received data transmission. A display of at least one of the determined heart failure risk score and the determined heart failure risk score alert is generated, and the determined daily heart failure risk score for each day occurring during the time period and the generated display are stored.