Medicine is pattern recognition. Healthcare should be driven by pattern recognition.
macro-eyes began by solving clinical questions, built a multidimensional model of the patient and an approach for predicting outcomes, leveraged this technology to create a product for the predictive supply chain and is increasingly focused on the foundation of care, patient scheduling.
Providers see more patients without adding hours to the day
Cut empty slots while reducing overbooking
The schedule becomes predictable
Streamline purchasing to focus on value
identify the medical devices that deliver optimal outcomes at greatest value for each patient type
Identify cohort-specific risks and the most effective course of care, based in practice-based evidence from patients like your patient
See how similar scenarios were resolved
Underpinning each macro-eyes product is a computational engine that detects and understands patterns in routinely collected data: patterns that are meaningful for personalizing the delivery and practice of care.
Patterns that are expressed multi-dimensionally are more predictive. The greater the number of clinical dimensions that are measured, the richer and more actionable the results.
Multidimensional queries allow providers to pinpoint the interventions or patient characteristic that consistently and uniquely lead to a specific outcome.
The machine learning at the core of macro-eyes products detects and analyzes the degrees of multidimensional similarity between patients and between events.
This engine allows macro-eyes to deliver deep insight on care pathways and patient risk, to determine the different medical devices that will bring optimal outcomes for different carefully defined cohorts of patients (patient phenotypes) and to predict with high accuracy the specific patients that will no-show for scheduled medical appointments.
The structure and scope of macro-eyes AI is informed by years of work with clinicians, data-scientists and administrators at health systems and leading academic medical centers in New York City and California.