Editor: What To Know
- Health at Scale’s novel approach uses advanced machine learning to simultaneously construct and relate information across multiple models that evaluate the best providers for each individual based on a vast number of factors related to the patient, provider, the time and setting.
- Health at Scale’s invention uses new AI technologies to score patient-provider matches based on thousands of health factors about a patient and an understanding of which providers have historically done well with similar kinds of patients.
- This patent advances Health at Scale’s mission to re-invent care delivery as a precise and personalized process that is deeply aware of each patient’s unique health characteristics and circumstances at all times.
Health at Scale, the market leader in smart AI-powered technologies to optimize care management, announced today that it has been awarded a patent by the U.S. Patent and Trademark Office for its breakthrough advances in personalized patient-provider matching using machine learning.
Choosing a provider is often the most important decision a patient makes. Health at Scale’s patented technology transforms provider search by empowering patients to find optimal primary and specialist care provider matches specific to their unique individualized health conditions and needs. This represents a significant advancement over current navigation technologies that consider providers in isolation of the patient – and are typically based on ad-hoc reputation scores or process-based metrics that are not correlated with better outcomes.
Health at Scale’s invention uses new AI technologies to score patient-provider matches based on thousands of health factors about a patient and an understanding of which providers have historically done well with similar kinds of patients. This proprietary approach, offering smart hyper-personalized provider navigation to payers, employers, providers and patients, evaluates the best facility, practice and physician choices for each individual’s unique health situation, and enables the creation of deeply personalized networks for the N-of-1. Health at Scale’s novel approach uses advanced machine learning to simultaneously construct and relate information across multiple models that evaluate the best providers for each individual based on a vast number of factors related to the patient, provider, the time and setting.
This patent advances Health at Scale’s mission to re-invent care delivery as a precise and personalized process that is deeply aware of each patient’s unique health characteristics and circumstances at all times. By rethinking the patient-provider matching process, Health at Scale drives improved outcomes, costs, satisfaction, quality and equity, while reducing unnecessary waste and cost in the system.
“Putting each patient’s needs at the center of care management is a core element of our work at Health at Scale. We’re excited to lead the way in reimagining the how patients find providers: as a process that is built around each patient and shows a nuanced understanding and deep empathy for their unique needs in the moment,” said Zeeshan Syed, Health at Scale CEO and formerly a Clinical Associate Professor at Stanford Medicine and an Associate Professor with Tenure in Computer Science at the University of Michigan. “We’re proud of the recognition from the USPTO for our team’s thought-leadership and pioneering contributions in personalized navigation and are committed to continued innovation for the populations we serve.”
