Systems and methods for a dynamic scheduling of appointments based on real-time health conditions of users

A system for scheduling appointments based on changing health conditions of users, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing processor-executable instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving, via one or more sensors at a first time, an initial health dataset of a user, wherein the initial health dataset indicates a health condition of the user;
• applying a trained machine learning model to the initial health dataset to determine i) a second time to schedule an appointment for the user with a service provider, and ii) a frequency for monitoring a plurality of subsequent health datasets between the first time and the second time, wherein the trained machine learning model is configured to identify one or more other users with similar health profiles to the user and determine the second time and the frequency for monitoring based on the similar health profiles;
• scheduling the appointment for the user at the second time;
• receiving, via the one or more sensors after the first time and before the second time, the plurality of subsequent health datasets of the user, wherein the plurality of subsequent health datasets is received at an interval based on the frequency of monitoring;
• determining at least one of health scores, rule scores, or medication scores for the user based on a processing of the plurality of subsequent health datasets;
• determining a plurality of risk scores corresponding to the plurality of subsequent health datasets for the user based on the at least one of the health scores, the rule scores, or the medication scores, wherein the plurality of risk scores are compared to a risk score threshold to determine a risk associated with one or more health conditions of the user, including at least the health condition indicated by the initial health dataset, and wherein the risk associated with the one or more health conditions indicates a low-risk condition, a moderate-risk condition, a high-risk condition, a severe-risk condition, or a critical-risk condition;
• determining a recent risk score, from among the plurality of risk scores for the user, has changed by a threshold level within a predefined time period, wherein the change in the recent risk score is indicative of a recent health condition trend of the user associated with a deterioration of the health condition indicated by the initial health dataset;
• adjusting the scheduling of the appointment for the user from the second time to a third time, the third time being prior to the second time based on the risk associated with the recent risk score;
• determining a treatment to respond to the deterioration of the health condition based on the initial health dataset and the plurality of subsequent health datasets;
• and
• transmitting a notification to a device associated with the user, the transmitting causing the device to display the notification about the adjusted scheduling of the appointment, wherein the determined treatment to respond to the deterioration of the health condition is administered to the user at the third time of the adjusted appointment.

One or more non-transitory computer-readable media for scheduling appointments based on changing health conditions of users, the one or more non-transitory computer-readable media storing processor-executable instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving, via one or more sensors at a first time, an initial health dataset of a user, wherein the initial health dataset indicates a health condition of the user;
• applying a trained machine learning model to the initial health dataset to determine i) a second time to schedule an appointment for the user with a service provider, and ii) a frequency for monitoring a plurality of subsequent health datasets between the first time and the second time, wherein the trained machine learning model is configured to identify one or more other users with similar health profiles to the user and determine the second time and the frequency for monitoring based on the similar health profiles;
• scheduling the appointment for the user at the second time;
• receiving, via the one or more sensors after the first time and before the second time, the plurality of subsequent health datasets of the user, wherein the plurality of subsequent health datasets is received at an interval based on the frequency of monitoring;
• determining at least one of health scores, rule scores, or medication scores for the user based on a processing of the plurality of subsequent health datasets;
• determining a plurality of risk scores corresponding to the plurality of subsequent health datasets for the user based on the at least one of the health scores, the rule scores, or the medication scores, wherein the plurality of risk scores are compared to a risk score threshold to determine a risk associated with one or more health conditions of the user, including at least the health condition indicated by the initial health dataset, and wherein the risk associated with the one or more health conditions indicates a low-risk condition, a moderate-risk condition, a high-risk condition, a severe-risk condition, or a critical-risk condition;
• determining a recent risk score, from among the plurality of risk scores for the user, has changed by a threshold level within a predefined time period, wherein the change in the recent risk score is indicative of a recent health condition trend of the user associated with a deterioration of the health condition indicated by the initial health dataset;
• adjusting the scheduling of the appointment for the user from the second time to a third time, the third time being prior to the second time based on the risk associated with the recent risk score;
• determining a treatment to respond to the deterioration of the health condition based on the initial health dataset and the plurality of subsequent health datasets;
• and
• transmitting a notification to a device associated with the user, the transmitting causing the device to display the notification about the adjusted scheduling of the appointment, wherein the determined treatment to respond to the deterioration of the health condition is administered to the user at the third time of the adjusted appointment.