Learn more about our Machine Learning in Healthcare
Machine Learning is primarily used in Pieces Predict and is integral to Pieces for Hospital Systems.
Not just pre-programmed healthcare responses.
Machine Learning allows our Artificial Intelligence (AI) to generate healthcare recommendations and solutions based on analyzing the medical data. It doesn’t just present pre-programmed answers triggered by a piece of information. Machine learning also enables the processing of large amounts of healthcare data at speeds beyond what a human could do.
Machine Learning at Speed and Scale
Machine Learning enables the processing of huge amounts of healthcare data faster.
Predictions and Approaches from AI
Our AI analyzes the healthcare data to predict patient outcomes and give recommended plans of action.
Earlier Interventions with Machine Learning
With predicted outcomes through machine learning, clinicians can take the necessary steps earlier and prevent deterioration or escalation.
Our AI Accuracy Builds Confidence
With our AI we have a Positive Predictive Value or PPV of 90-95%, which means you can rely on the accuracy of our predicted healthcare outcomes.
Sensitivity in Machine Learning
Our AI accurately predicts 80 to 95% of actual positive cases. Conversely, we have five times fewer false positives.
Within Pieces Predict machine learning, all the health determinants of a patient are analyzed. Structured healthcare and medical data such as age, blood pressure, and other data points are combined with unstructured data from the clinician’s notes and with a patient’s social determinants from their life outside the hospital. Through analyzing all the relevant data, AI can predict, for example, that a patient needs ICU care before an adverse event happens. An earlier intervention through machine learning and AI can keep a patient’s condition from escalating or deteriorating.
Learn more about our Machine Learning in Healthcare
Machine Learning is primarily used in Pieces Predict and is integral to Pieces for Hospital Systems.
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