Clover Health Investments has patented a system for medication fillings management using machine learning. The system predicts users likely to miss refills based on health indicators and refill schedules, generating targeted reminders to prevent missed refills. The system also adapts reminders based on user input. 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 February 2024 was 31%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting medication refills and sending targeted reminders to users
A recently granted patent (Publication Number: US11908558B2) discloses a system that utilizes machine learning models to predict and prevent missed medication refills. The system comprises processors and computer-readable media storing instructions for receiving user information, identifying health indicators, determining refill probabilities, generating reminders, and training machine learning models based on user interactions. The system can adaptively send reminders to users based on their refill schedules, refill histories, and user input data, enhancing medication adherence. The reminders include interactive links for users to schedule refills, arrange deliveries, or prepare for in-person pickups, improving user engagement and convenience. Additionally, the system can predict missed refills for multiple users, generate personalized reminders, and analyze health indicators to optimize reminder effectiveness.
Furthermore, the patent includes methods for generating machine learning models, training datasets, and predicting missed refills based on user data and interactions with the system. By analyzing health-related data, refill schedules, and user input, the system can determine the probability of missed refills and generate tailored reminders for users. The methods also involve comparing health indicators, identifying associations between indicators and refill probabilities, and considering device types for reminder generation. Overall, the patented system and methods aim to enhance medication adherence through personalized reminders, predictive analytics, and machine learning techniques, ultimately improving health outcomes for users by ensuring timely medication refills and reducing the risk of missed doses.
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