Clover Health Investments has been granted a patent for a system that analyzes patient data to generate and prioritize medical recommendations. The system utilizes machine learning to adapt recommendations based on historical usage and real-time inputs, enhancing decision-making during patient appointments. GlobalData’s report on Clover Health Investments gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Clover Health Investments, Treatment response prediction was a key innovation area identified from patents. Clover Health Investments's grant share as of July 2024 was 37%. Grant share is based on the ratio of number of grants to total number of patents.
Medical recommendation system using machine learning and prioritization
The patent US12051506B2 describes a system and method for generating and presenting medical recommendations to healthcare providers using machine learning. The system comprises one or more processors and non-transitory computer-readable media that store executable instructions. These instructions enable the identification of historical usage data from medical service providers, which is used to create a ranking of medical recommendations. The system also incorporates scheduling data related to patient appointments, allowing it to generate a machine learning model that predicts medical recommendations, including potential diagnoses, gaps in medical coverage, or medication suggestions. The model is trained using a dataset that combines historical usage and scheduling data, ultimately producing a graphical user interface (GUI) that displays the recommendations during patient appointments.
Additionally, the system is designed to adapt recommendations based on real-time feedback from healthcare providers. It can receive location data to ensure that the device displaying the recommendations is within a specified proximity to the patient's residence. The system allows for the approval or rejection of recommendations, which influences the generation of subsequent recommendations. This iterative process ensures that the recommendations presented are relevant and tailored to the patient's needs, with the ability to adjust priorities based on the severity of medical conditions or treatment plans. Overall, the patent outlines a comprehensive approach to enhancing the decision-making process in medical settings through the integration of machine learning and real-time data analysis.
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