The Augmented Care Effectiveness (ACE) is a state of the art solution that uses machine learning (ML) to identify risk and predict the occurrence of critical medical events.
It is comprised of two unique elements:
Key to any predictive model, segmentation (also called clustering) allows for records to be placed in groups of similar records based on advance analytic capabilities.
Segmentation is based first on analyzing the population as a whole, with the goal of ascertaining preceding occurrences that may lead to a critical event. This segmentation maximizes informative correlations from the demographic and medical variables that were selected for analysis. This allows our predictive models to capture nuances of a specific segment to more accurately project how risk levels for a particular event progress over time.
These models are “trained” by past data, which identifies the strength of the correlation of factors and conditions to predict the likelihood of the event occurring when highly similar activities and conditions occur in the future. This analysis produces a risk score which allows the process of risk stratification, which identifies the importance and degree of risk.
Time to Event Analysis
In addition to risk stratification during our predictive modeling process, time to event analysis allows us to quantify the urgency of an intervention. Time-to-event analysis, also referred to as survival analysis, strives to predict the time horizon for a given event to occur. While often focused on mortality, our model identifies time to event analysis for a wide range of critical events, over varying observation windows. Ultimately, this approach quantifies risk in a way that allows stratification that leads to the identification of high-risk individuals in need of urgent attention, or at-risk individuals requiring ongoing observation.