Patient health monitoring and surveillance is crucial to recognizing and reacting to quick changes in status. Identification of cardiorespiratory insufficiency early or predicting its development beforehand is a challenge that leaves clinicians scrambling to respond to the rapid onset of life-threatening symptoms. Most pathological processes presenting as circulatory shock or respiratory insufficiency evolve over time, and early identification can markedly improve outcomes by improving processes before they become severe, induce remote organ injury, or become irreversible; but to date, such prediction ability has been inaccessible.
Description
Machine learning principles, coupled with human physiological data, can enable a data-driven prediction modeling approach to predict if and when patients are likely to develop future instability. Hemodynamic Monitoring Parsimony repurposes routinely acquired non-invasive hemodynamic data to predict cardiorespiratory insufficiency prior to the onset of severe symptoms, determine which additional biomarkers and measuring frequency will improve the accuracy and specificity of these predictions, assess whether the patient is responsive to process-specific interventions, determine if resuscitation has effectively restored tissue perfusion, and identify minimum criteria as to be clinically relevant. This data-driven prediction modeling approach will enable healthcare professionals to predict future instability in patients both at the bedside and in remote settings, yielding immense improvements in patient safety, surveillance and care.
Applications
• Patient safety monitoring to predict instability before it happens
• Focused resource allocation for triage of those who need little monitoring versus those at risk
• Personalized monitoring and resuscitation independent of disease type
Advantages
• Improved patient safety
• Higher quality of patient care
• Provides decision support for frequency of monitoring, case load mixture, and staff workload and allocation
• Prevents organ injury and other avoidable consequences of cardiorespiratory instability
Invention Readiness
Prototype
IP Status
https://patents.google.com/patent/US20140107437A1
