Sepsis Care Management System for Protocol Adherence
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Solution Overview
Problem
Current sepsis care lacks standardized protocols and is prone to delays in treatment due to the insidious onset of sepsis, leading to high mortality rates, as existing methods fail to provide effective early detection and continuous monitoring of patient conditions.
Innovation Solution
A system and method for sepsis care management that processes patient data to assess the likelihood of sepsis and compares clinician treatment data to a target treatment protocol, providing notifications for discrepancies and updating protocols based on evolving patient conditions, utilizing a statistical probability model and integrating data from various sources, including physiological monitors and treatment devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated sepsis diagnosis systems are implemented, then early detection capability is improved, but the system cannot account for the insidious onset and evolution of sepsis over time
Solution Approach 1:
The system implements continuous monitoring and re-assessment of patient conditions at scheduled intervals, ensuring that the diagnostic process continues throughout the patient's hospital stay rather than being a one-time event. This allows the system to track the insidious evolution of sepsis and update diagnoses as conditions change.
Solution Approach 2:
The system dynamically updates the sepsis diagnosis and treatment recommendations based on evolving patient conditions. The diagnostic model is re-executed at each assessment interval, allowing the system to adapt to changing clinical presentations and adjust the likelihood of sepsis accordingly.
2Loss of time
If general clinical orders are enforced to avoid treatment delays, then treatment timing is improved, but outcomes deteriorate when treatment is idiosyncratic and not reconsidered frequently
Solution Approach 1:
The system provides continuous feedback by comparing actual treatment administration against the dynamically updated target treatment protocol. At each re-assessment interval, the system determines whether previously recommended treatments should continue, be modified, or be discontinued based on the updated sepsis likelihood and current patient conditions.
Solution Approach 2:
The treatment protocol is not static but dynamically updated at each assessment interval. The system allows treatment plans to evolve as patient conditions change, ensuring that treatments remain appropriate and effective throughout the course of illness rather than being fixed at initialization.
3Measurement precision
If comprehensive patient data processing is implemented to assess treatment likelihood, then treatment accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses a unified diagnostic model that processes multiple types of patient data (vital signs, laboratory values, medical history) through a single integrated algorithm. This multi-functional approach consolidates what could be multiple separate analysis systems into one cohesive platform, managing complexity while maintaining comprehensive assessment capability.
Data Source
AI summary
Systems and methods are disclosed for sepsis care management. First data regarding a patient and second data regarding a clinician's treatment of a patient are received by at least one processor. The first data regarding the patient is processed to assess a likelihood that the patient would benefit from administration of each of one or more critical actions for treatment of sepsis, wherein at least one of the one or more critical actions relates to a request for at least one additional diagnostic action. A target treatment protocol, comprising a decision for each of the one or more critical actions, is determined based on the assessed likelihoods. The second data regarding a clinician's treatment of the patient is compared to the target treatment protocol and a notification is provided to the clinician if the second data is incompatible with the target treatment protocol.