Designed to scale into an end-to-end solution STABILITY can be used across a variety of healthcare settings and will include early warning systems for patient deterioration across several areas of care, including our existing STABILITY UO solution for Acute Kidney Injury, and our forthcoming Atrial Fibrillation solution.
Developed in collaboration with hospital researchers and clinical teams, STABILITY provides real-time monitoring and risk prediction analysis.Contact Us
Current early warning score models are limited by their use of generic thresholds. This often results in the subtle signs of patient deterioration being missed until it is too late to react. STABILITY anticipates patient deterioration before it occurs, improving patient safety and the timeliness of interventions.
STABILITY has the potential to make significant cost savings by reducing hospital length of stay and limiting unnecessary resource use.
STABILITY uses a patient’s physiological data and, therefore, each risk prediction score is entirely personalised.
STABILITY provides an easy to understand scoring system, promoting efficient assessment adoption. With automatic retrieval of data, the opportunities for incorrect data entry and subsequent calculations are significantly reduced.
The latest addition to the STABILITY platform is STABILITY AF, which provides clinical risk prediction for patients developing atrial fibrillation (AF) after cardiac surgery.
AF is the most common arrhythmia complicating cardiac surgery, and puts patients at increased risk of stroke, congestive heart failure, and haemodynamic instability. Post-operative AF usually occurs 2-3 days after surgery, and is associated with longer stays in hospital stays and increased treatment costs.
STABILITY AF uses physiological data inputted pre-operation and post-operation to provide clinicians with a risk score for how likely a patient is to develop atrial fibrillation. Each patient receives a pre-operative risk score, and a post-operative risk score, allowing clinicians to quickly and easily identify those patients most at risk.