Population Analytics Detailed

Augmented Care Effectiveness (ACE) 

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 three 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.

Risk Stratification

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.

Rapid Hypothesis Testing

HELP Experimental Learning

Rapid Hypothesis Testing (Experimental Learning) facilitates the goal of actionable information. Hypothesis Testing allows for the dynamic creation of cohorts that can be broken into control and study groups for the delivery of new and existing protocols to care. It then tracks the ongoing success of a change in care and automatically defines the success of a new protocol with statistic accuracy.

Through Hypothesis Testing, rapid experiments can be run in parallel with small clusters of a given cohort to demonstrate promising (or not) interventions to care. While not intended to serve as a clinical study, it does provide a rapid quick view into the feasibility of a given change in care, and the cost/benefit likely derived from such change.

While Hypothesis Testing  can be quite granular and clinical in its approach (i.e. therapies for a targeted condition, or the impact of changes in diet plans or sleep on targeted vitals), it can also be focused on the impact of non-critical protocols such as the introduction of dog walking to reduce the number of falls in an aging in place community. In fact, it can even be used to validate the effectiveness of larger initiatives such as introduction of Remote Monitoring and the subsequent reduction in admissions, readmissions, or other critical events.