High quality data with advanced analytics and AI companions enable healthcare providers to make smarter decisions:
Data-input: longitudinal data on patients, e.g. EHR, imaging, labs, genomics, behavioral data
both from the individual patient, and from the vast pool of comparable cases.
Analytics and AI used for: outcome predictions, diagnostic and therapy decision support or chronic disease management, episodic or longer-term.
In the future, a model, or “digital twin”, of the patient might be available and treatment alternatives could be explored in the virtual world before setting off on a care pathway in the real world.
Data-input: longitudinal data on patients and enterprise-wide operational data
Analytics and AI used for: assessment of patients’ needs
Algorithms could prompt or “nudge” lifestyle changes in the patient, or initiate conversation with the care provider in order to better manage—or even prevent—chronic conditions.
Data input: enterprise-wide data, real-time, operational data, e.g. workflows, patient flows, staff scheduling, productivity
Analytics and AI used for: operational decision support, e.g. asset and fleet management, workforce and workflow management.
The goal is to build the most complete digital model, a “digital twin” of the healthcare enterprise and then use predictive analytics to generate possible outcomes and improve operational efficiency.