Systems and Methods for Dynamic Health Appointment Scheduling Based on Real-Time Conditions
Summary
USPTO granted Patent US12603183B2 to Optum, Inc. covering machine learning systems for dynamic healthcare appointment scheduling. The patent discloses methods for inputting user health data to ML models that identify similar health profiles, generating risk scores from subsequent health datasets, and adjusting appointment frequency, duration, and type based on evaluated risk changes. The patent includes 17 claims and was filed on October 7, 2022.
What changed
USPTO granted Patent US12603183B2 to Optum, Inc. covering systems and methods for dynamic healthcare appointment scheduling using machine learning. The patented invention involves receiving initial health datasets, using ML models to identify users with similar health profiles, determining monitoring and appointment frequencies, and generating health, rule, and medication scores to evaluate risk. The system adjusts appointment schedules based on changes in risk scores and notifies users.
Healthcare providers and technology companies developing similar scheduling systems should monitor this patent grant for potential licensing implications or to assess whether their systems may fall within the scope of these claims. The patent's broad coverage of ML-based health profile matching and dynamic scheduling may affect competitive positioning in healthcare technology markets.
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Apr 15, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Systems and methods for a dynamic scheduling of appointments based on real-time health conditions of users
Grant US12603183B2 Kind: B2 Apr 14, 2026
Assignee
Optum, Inc.
Inventors
Saloni Kakkar, Surajit Das, Gregory J. Boss, Lo Fu Tan
Abstract
Systems and methods are disclosed for scheduling appointments based on changing health conditions of users. The method includes inputting the initial health dataset of a user to a machine learning model configured to identify other users with similar health profiles. A frequency of monitoring, a frequency of appointments, a duration between the appointments, or a type and length of the appointments is determined to generate a schedule of appointments. A subsequent health dataset of the user is received, and health scores, rule scores, or medication scores for the user are determined based on the subsequent health dataset. A plurality of risk scores for the user is evaluated based on the health scores, rule scores, or medication scores. A recent risk score is determined based on a change in the plurality of risk scores. The schedule of appointments is adjusted based on the recent risk score and the user is notified.
CPC Classifications
G16H 50/30 G16H 80/00
Filing Date
2022-10-07
Application No.
18045116
Claims
17
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