Predictive Monitoring: IMPact of Real-Time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT)

Hospitalized patients with cardiac disease are at risk for sudden deterioration from complicating subacute illnesses such as sepsis, hemorrhage, and respiratory decompensation, resulting in significant increases in length of stay and mortality. These subacute illnesses can be mitigated with early recognition and intervention. The use of continuous predictive analytics has the potential to warn providers of impending deterioration, allowing for an earlier window of treatment, when it is more effective and not too late to intervene effectively, thus allowing care to become proactive rather than reactive. By recognizing earlier in the clinical course that a patient is imminently at risk for respiratory failure, sepsis, or hemorrhage, appropriate care may be escalated resulting in better outcomes.

This research will evaluate the impact of CoMET—continuous monitoring of event trajectories—an artificial intelligence tool with visual analytics that displays risk estimates for multiple adverse outcomes. The tool integrates data streams of rapidly changing health information to predict and communicate risk of impending decompensation.

The specific aims of the research are as follows:

• Evaluate the impact of predictive analytics monitoring on patient outcomes.
• Evaluate the impact of predictive analytics monitoring on clinical action.
• Evaluate the impact of predictive analytics monitoring on costs to the health system.

The tool will use artificial intelligence models to incorporate cardiovascular data from patient monitors, as well as vital signs and lab test data from electronic health records. The vast quantity of these data is difficult for clinicians to absorb in aggregate, an issue compounded with the sickest patients who generate thousands of datapoints in short periods of time. The creation of visual tools allows for predictive analytics monitoring, while reducing the cognitive load for providers taking care of intensive care patients. The impact of the tool will be evaluated with a cluster randomized controlled trial—the Predictive Monitoring: IMPact of Real-time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT)—comparing patient outcomes, clinical action, and costs for patients in the intervention group versus usual care. It is anticipated that this tool will draw clinicians’ attention to those patients that warrant earlier interventions. Following the completion of this work, the researchers plan to expand their analysis by conducting a multi-centered randomized control trial testing the effectiveness of Co-MET.