Patent Publication Number: US-2022238228-A1

Title: Intelligent telehealth platform

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application Ser. No. 63/140,689 entitled “INTELLIGENT TELEHEALTH PLATFORM,” filed Jan. 22, 2021, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to software technology, and more particularly, to systems and methods for using an intelligent telehealth platform to generate a recommended treatment plan to treat a malady of a user. 
     BACKGROUND 
     Machine learning is a type of artificial intelligence when computers are programmed to learn information without human intervention. In machine learning, the development of the underlying algorithms rely on computational statistics. Computers are provided data and then the computers learn from that data. The data actually teaches the computer by revealing its complex patterns and underlying algorithms. The larger the sample of data the machine is provided, the more precise the machine&#39;s output becomes. Machine learning in healthcare is becoming more widely used and is helping patients and clinicians in many different ways. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The described embodiments and the advantages thereof may best be understood by reference to the following description taken in conjunction with the accompanying drawings. These drawings in no way limit any changes in form and detail that may be made to the described embodiments by one skilled in the art without departing from the spirit and scope of the described embodiments. 
         FIG. 1  is a block diagram that illustrates an example computing architecture, in accordance with some embodiments of the present disclosure; 
         FIG. 2  is a block diagram of an example intelligent telehealth (IT) platform, in accordance with embodiments of the disclosure; 
         FIG. 3A  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a list of dispensaries stored in a storage of the IT platform  200 , according to some embodiments; 
         FIGS. 3B-3C  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a list of products stored in the IT platform  200 , as well as the details of each of the products, according to some embodiments; 
         FIGS. 3D-3E  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a list of patient profiles stored in the IT platform  200 , as well as the details of each of the patient profiles, according to some embodiments; 
         FIG. 3F  is an example graphical user interfaces of the telehealth application  160  depicting a method for displaying the model outputs from each of the models of the IT platform  200 , according to some embodiments; 
         FIGS. 3G-3H  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a draft careplan created by the models of the IT platform  200 , according to some embodiments; 
         FIG. 31  is an example graphical user interface of the telehealth application  160  depicting a method for displaying product specific instructions, according to some embodiments; 
         FIG. 3J  is an example graphical user interface of the telehealth application  160  depicting a method for displaying the model output of the why recommendation model  214  in  FIG. 2 , according to some embodiments; 
         FIG. 3K  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a user&#39;s shopping list, according to some embodiments; 
         FIG. 3L  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a draft careplan created by the models of the IT platform  200 , according to some embodiments; 
         FIG. 3M  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a user&#39;s progress in completing the careplan, according to some embodiments; 
         FIG. 3N  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a notes session to track any communication with the patient, survey data, or physician telemedicine (sometimes referred to as, “telemed”) notes, according to some embodiments; 
         FIG. 4  is a flow diagram depicting a method for using the intelligent telehealth platform  200  to generate a recommended treatment plan to treat a malady of a user, according to some embodiments; and 
         FIG. 5  is a block diagram of an example computing device that may perform one or more of the operations described herein, in accordance with some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     An intelligent telehealth platform is described. In one embodiment, the telehealth platform described herein provides intelligent recommendations for cannabis-based treatment to an individual. Worth noting, although many of the examples described herein refer to cannabis for clarity and brevity, any other suitable treatment methods are equally contemplated herein. In one embodiment, the platform described provides a complete, end-to-end solution for individuals seeking recommendations and guidance for healthcare treatment solutions. For example, the provided embodiments describe collecting information about an individual&#39;s ailments, connecting the individual with healthcare professionals, providing customized recommendations for treatment (e.g., including pharmaceuticals), an online marketplace for custom treatment solutions, personalized real-time treatment instructions and walkthroughs, advanced custom treatment optimizations, personalized reminders, etc. 
     Designing an intelligent telehealth platform/system for cannabis-based treatment to an individual presents several problems. For one, the platform must be able to make accurate predictions about the user even when there is no reliable source of data to use to build a model. Furthermore, the experience of cannabis effects by individuals is often highly idiosyncratic. Thus, the problem of idiosyncratic reactions to substances like cannabis requires a novel approach to both solving for utility and speeding up the system overall. 
     Aspects of the present disclosure address the above-noted and other deficiencies by implementing Bayesian and Decision Networks to best provide a knowledge-engineered platform that could eventually update itself and incorporate data when it becomes available. Advantageously, the combination of traditional and Bayesian approaches used produce a unique-to-each-user projection and prediction model consuming both discrete, observed quantitative data with continuous, subjective, and intuitive inputs regarding the real-time, experienced state of the individual. The Bayesian service generates (e.g., drives) a series of “tuning” questions that begins to self-learn. It also gives a method of observing and quantifying how trustworthy and accurate a given user&#39;s feedback is, as well as a certainty measure on the model&#39;s predictions and recommendations—an important insight for health- and medicine-related decision making—that is then incorporated into the unique-to-each-user prediction model for ever-increasing accuracy and temporal range. In one embodiment of the system, all this occurs in a runtime environment enabling nearly-immediate computation with no offline processing. In comparison, current approaches to state-change management combine online information retrieval of generic effects with incomplete asynchronous, “remembered” effects from the compound in a traditional medical interview that could take weeks or even months to fully cycle, and do not incorporate individualized subjective, real-time state-change experience data. 
     The overall intelligence model may include multiple pieces. At a high level are the inputs of the data used by the model. Such data may include user profiling inputs, product inventory, inputs from healthcare and pharmaceutical experts, inputs the user provides based on questions the platform asks, data indicative of physician expertise and experience associated with cannabis, physician approval of the recommended treatment plan, and/or data on how the user interacts with the system. 
     The embodiments described herein provide for a variety of novel features, including, but not limited to: the generation of a personalized, holistic, calendar-based treatment plan spanning medicine instructions (e.g., how much of what to take, when, where and how), diet, exercise and mindfulness; the enablement of real-time, structured patient feedback using established clinical measures, data sources from other user applications, and unstructured voice and text feedback all while the patient is experiencing the effects of the medication. the enablement of end-of-day, overall well-being and satisfaction check-ins using structured, established measures and unstructured voice and text inputs; and/or the synthesis of all patient inputs and third-party data into a clinical dashboard, through which the clinician can use a series of proprietary filters and search mechanisms to efficiently track individual patient progress and determine connections and insights related to medical substances ingested, patient health profile, patient feedback and patient actions/adherence to a care program (e.g., an eo 30-day care program). 
       FIG. 1  is a block diagram that illustrates an example computing architecture  100 , in accordance with some embodiments of the present disclosure. The computing architecture  100  may include host system  110 , third-party system  140 , and client device  150 . 
     As illustrated in  FIG. 1 , computing architecture  100  includes host system  110  that includes computing processing device  120  and data store  130 . The host system  110 , third-party system  140 , and client device  150  are coupled to each other (e.g., may be operatively coupled, communicatively coupled, may communicate data/messages with each other) via network  105 . Network  105  may be a public network (e.g., the internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. In one embodiment, network  105  may include a wired or a wireless infrastructure, which may be provided by one or more wireless communications systems, such as a WiFi′ hotspot connected with the network  105  and/or a wireless carrier system that can be implemented using various data processing equipment, communication towers (e.g. cell towers), etc. The network  105  may carry communications (e.g., data, message, packets, frames, etc.) between the various components of host system  110 . 
     The client device  150  includes a processing device  155  that executes a telehealth application  160  that the client device  150  displays on a computer screen (local or remote) allowing a user of the client device  150  to view and exchange data (e.g., user input data, recommendation requests, recommendations, model outputs, notifications, alerts, etc.) with any other computing devices (e.g., host system  110 , third party system  140 ) and/or data store (e.g., data store  130 ) connected to the network  105 . In some embodiments, an administrator may use the telehealth application (sometimes referred to as, “admin portal”) executing on a client device  150  in an administrator mode to manage and/or operate the host system  110   
     The telehealth application  160  may monitor interactions of a user (not shown in  FIG. 1 ) of the client device  150  with the telehealth application  160  and/or the client device  150  to intercept and/or collect data processed by processing device  155 , similar to a screen scraper, packet interceptor, application programming interface (API) hooking process, or other such application. The telehealth application  160  may intercept and/or receive data, such as mouse clicks, scroll wheel movements, gestures such as swipes, pinches, or touches, or any other such interactions. The telehealth application  160  may receive data via multiple choice, sliders, text entry, via open voice to text, etc. The telehealth application  160  may send the intercepted and/or received data to the host system  110  via network  105 . 
     In some embodiments, the telehealth application  160  may display a GUI screen (e.g., pop-up) asking for the user&#39;s permissions to have access to data (e.g., medical data, wellness data, personality data, user interaction data, ect.) about the user from external sources, and if access is granted, retrieve the data from the external sources. For example, the IT platform  200  may ask the user for permission to access their data that was gathered by their wearable device (e.g., smart watch, headset, wireless earbuds, fitness tracker, blood pressure monitor, etc.), and if approved, retrieve the data from the wearable device (or from the user&#39;s smart phone that is tethered to the wearable device). For example, a user that listens to a variety of music via an online music service may grant the IT platform  200  permission to connect to a public application programming interface (API) of the online music service and view the user&#39;s music choice. This music-related information may be a predictor or indicator of the user&#39;s state-change. The IT platform  200  may then use the data retrieved from the external sources to perform even deeper predictions about the user, which in turn, improves the IT platform&#39;s  200  capability to generate a recommended treatment plan that is best fit for the user. 
     The data store  130  may be a persistent storage that is capable of storing data. A persistent storage may be a local storage unit or a remote storage unit. Persistent storage may be a magnetic storage unit, optical storage unit, solid state storage unit, electronic storage units (main memory), or similar storage unit. Persistent storage may also be a monolithic/single device or a distributed set of devices. In some embodiments, the data store  130  may correspond to a plurality of physically separate data stores (e.g., user data store, a collective user reviews and data store, a product inventory store, etc.) that are communicatively coupled to the network  105 . 
     Each component may include hardware such as processing devices (e.g., processors, central processing units (CPUs), memory (e.g., random access memory (RAM), storage devices (e.g., hard-disk drive (HDD), solid-state drive (SSD), etc.), and other hardware devices (e.g., sound card, video card, etc.). The host system  110 , third-party system  140 , and client device  150  may include any suitable type of computing device or machine that has a programmable processor including, for example, server computers, desktop computers, laptop computers, tablet computers, smartphones, set-top boxes, etc. In some examples, host system  110 , third-party system  140 , and client device  150  may include a single machine or may include multiple interconnected machines (e.g., multiple servers configured in a cluster). In some embodiments, host system  110  may be part of a cloud computing system. The host system  110 , third-party system  140 , and client device  150  may execute or include an operating system (OS), as discussed in more detail below. The OS of a server may manage the execution of other components (e.g., software, applications, etc.) and/or may manage access to the hardware (e.g., processors, memory, storage devices etc.) of the computing device. 
     In some embodiments, processing device  120  may execute an intelligent telehealth (IT) component  125 . In some embodiments, the IT component  125  may include at least part of a machine learning model for intelligently generating a recommended treatment plan (sometimes referred to as, “careplan” or “care plan”) to treat a malady of a user. The IT component  125  may obtain a user profile dataset associated with a user. The IT component  125  may further select, based on the user profile dataset, a first machine learning model from a set of machine learning models respectively trained to predict cannabis treatment responses. The IT component  125  may generate a recommended treatment plan for using one or more cannabis products to treat a malady of the user based on the user profile dataset and the first machine learning model. The IT component  125  may transmit the recommended treatment plan to a client device to cause the client device to display the recommended treatment plan. The IT component  125  may further transmit a request to a physician (e.g., third-party system  140 ) for approval of the recommended treatment plan. The IT component  125  may further receive physician approval of the recommended treatment plan prior to transmitting the recommended treatment plan to the client device  150 . Further details regarding the IT component  125  are discussed at  FIGS. 2-5  below. 
     In some embodiments, the data store  130  may include a repository of one or more pieces of user profile data, feedback data, product inventory data, collective user reviews, telemed session data, and careplans. 
     Although  FIG. 1  shows only a select number of computing devices (e.g., client devices  150 , third party system  140 ) and data store (e.g., data store  13 ), the computing architecture  100  may include any number of computing devices that are interconnected in any arrangement to facilitate the exchange of data between the computing devices. 
       FIG. 2  is a block diagram of an example intelligent telehealth (IT) platform  200 , in accordance with embodiments of the disclosure. In some embodiments, at least a portion of the IT platform  200  may be implemented by the IT component  125  in  FIG. 1 . The IT platform  200  (sometimes referred to as, “platform” hereafter) may include and/or execute a user profile collector  202 , a nature language processing (NLP)  204 , a diagnosis model  206 , one or more regimen recommendation models  208 , an inventory model  210 , an instructions model  212 , a why recommended model  214 , a feedback collector  216 , a telemed session data collector  218 , a feedback processing model  220 , and a final careplan generator  224 . In some embodiments, the diagnosis model  206 , the regimen recommendation models  208 , and/or the feedback processing model  220  may be combined into a single model that performs the combined functionality of each the models. In some embodiments, each of the models (e.g., diagnosis model  206 , the regimen recommendation models  208 , and/or the feedback processing model  220 ) may be split into a plurality of models that collectively perform the functionality of the model that was split. 
     The IT platform  200  may include a product inventory store  222 , a user data store  226 , and a collective user reviews and data store  228 . In some embodiments, the one or more of the product inventory store  222 , a user data store  226 , and a collective user reviews and data store  228  may correspond to the data store  130  in  FIG. 1 . 
     At an initial recommendation stage, the user profile collector  202  may collect (e.g., gather, retrieve, receive) a series of inputs (sometimes referred to as, “user input data” or “user profile data”) from the user responsive to the user creating their account. In some embodiments, the initial recommendation stage takes place prior to a user implementing the recommendation and/or treatment plan generated by the IT platform  200 . In some embodiments, the user profile collector  202  may collect the series of inputs from the user by sending data (e.g., instructions, code, data, questions, etc.) to the telehealth application  160  of the client device  150  to cause the telehealth application  160  to present a series of questions to the user on a display and send the user&#39;s responses to each of the questions to the user profile collector  202 . In some embodiments, the data that the telehealth application  160  sends to the telehealth application  160  may include (e.g., locally stored on client device  150 ) the series of questions. In some embodiments, the telehealth application  160  may already have a local copy of the series of questions, such that the data sent from the telehealth application  160  is only used to instruct the telehealth application  160  to begin presenting the series of questions. 
     The user may provide (e.g., transmit, send) the inputs to the user profile collector  202  via the telehealth application  160  executing on the client device  150 . In some embodiments, inputs may include information such as prior cannabis use, preferred products to include in the care plan (sometimes referred to as, “treatment plan” or “care program”), use preferences by day parts, diagnosed medical conditions, current medications, personality inputs, reasons for cannabis use, game results (e.g., to measure intoxication), user interaction data (e.g., images and video recordings of the user, how hard a user presses on a screen of the client device  150 , eye position and movement indicative of a user focusing on one or more screens of the telehealth application  160 ), data from third party applications indicative of user state, such as biometric devices, health applications, mood trackers, music playlists, and other sources, and/or what the user does to relax. As discussed below, in some embodiments, the inputs to the user profile collector  202  may also be sent to the feedback processing model  220 . 
     In some embodiments, the inputs are collected via the telehealth application  160  using multiple choice, sliders, text entry, and via open voice to text. The user profile collector  202  may store the inputs in the user&#39;s profile in the user data store  226 . The user profile collector  202  may send the inputs (e.g., open voice to text and text entry inputs) to the NLP model  204  to be processed. 
     In some embodiments, if the user skips answering any of the questions, or does not know the answer to some questions, then the IT platform  200  may still execute (e.g., process) to produce a recommendation by using prior probabilities for those nodes and/or components. 
     TABLE 1 shows non-limiting examples of questions that the user profile collector  202  (and/or the telehealth application  160 ) may generate and present to the user of client device  150  in order to collect user input data (e.g., user profile data) at the initial recommendation stage. Each question is grouped according to a question type (e.g., Basic User Information, About User&#39;s Pain/Sleep/Anxiety (Group 1), About User&#39;s Pain/Sleep/Anxiety (Group 2), About User&#39;s Pain/Sleep/Anxiety (Group 3), etc.) and associated with a presentation method (e.g., multiple choice, location selector, NLP, etc.). In some embodiments, the user collector  202  may be a Bayesian model that is configured to generate the questions in TABLE 1. The user profile collector  202  may generate and present one or more of the examples questions to the user in any order and using any presentation method. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 EXAMPLE QUESTIONS FOR COLLECTING USER INPUT DATA AT 
               
               
                 AN INITIAL RECOMMENDATION STAGE (QUESTION TYPE) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 Basic User Information 
                 Gender selection (multiple choice) 
               
               
                   
                 Age (entered text) 
               
               
                 About User&#39;s 
                 Where does it hurt? (location selector) 
               
               
                 Pain/Sleep/Anxiety (Group 1) 
                 How much does it hurt? (Stanford pain scale) 
               
               
                   
                 Describe your pain. Is it sharp or dull? Is it constant or does it come 
               
               
                   
                 and go? How often is the pain? (NLP) 
               
               
                   
                 Does your pain keep you from falling asleep or wake you up at night? 
               
               
                   
                 (NLP) 
               
               
                   
                 How is your life being impacted? Are your activities limited? Your 
               
               
                   
                 mood changed? (NLP) 
               
               
                   
                  NLP output for data model: model wants to gauge how 
               
               
                   
                  severe/urgent this issue is - also whether a mood lift may be 
               
               
                   
                  needed in addition to addressing the primary issue 
               
               
                   
                 Thank you. If there&#39;s anything else you&#39;d like to share, please feel 
               
               
                   
                 free. (NLP) 
               
               
                   
                 Cause/diagnosis (multiple choice options related to location on body) 
               
               
                   
                 POEM sliders relevant to pain location and diagnosis 
               
               
                   
                 Pain meds to be replaced (check boxes with pulldowns for frequency 
               
               
                   
                 and dose) 
               
               
                   
                 How did (medicine previously used) work for you? Why are you 
               
               
                   
                 looking to move on from (medicine used)? (NLP) 
               
               
                   
                 Pain meds to be used concurrently with cannabis (check boxes with 
               
               
                   
                 pulldowns for frequency and dose) 
               
               
                   
                 For how long have you been feeling the pain? 
               
               
                 About User&#39;s 
                 How would you rate the quality of your sleep over the last week? 
               
               
                 Pain/Sleep/Anxiety (Group 2) 
                 (Single item Sleep Quality 1-10 Scale (SQS) slider) 
               
               
                   
                 How often do you have trouble getting a good night of sleep? (NLP) 
               
               
                   
                 Do you have trouble getting to sleep or staying asleep? Is sleep 
               
               
                   
                 interrupted by outside forces? (NLP) 
               
               
                   
                 How is your life being impacted? Are your activities limited? Your 
               
               
                   
                 mood changed? (NLP) 
               
               
                   
                  NLP output for data model: model wants to gauge how 
               
               
                   
                  severe/urgent this issue is - also whether a mood lift may be 
               
               
                   
                  needed in addition to addressing the primary issue 
               
               
                   
                 Can you talk about your bedroom setting? Do you have a TV? Is it 
               
               
                   
                 very dark? What about electronic usage in bed? Unwinding prior to 
               
               
                   
                 going to sleep? (NLP) 
               
               
                   
                 Thank you. If there&#39;s anything else you&#39;d like to share, please feel 
               
               
                   
                 free. (NLP) 
               
               
                   
                 When you are in bed not sleeping, is it because you are thinking about 
               
               
                   
                 daytime concerns? (multiple choice) 
               
               
                   
                 Cause/diagnosis check boxes (options that could affect sleep) 
               
               
                   
                 Depending on medical condition, does it keep you awake? 
               
               
                   
                 Sleep meds to be replaced (check boxes with pulldowns for frequency 
               
               
                   
                 and dose) 
               
               
                   
                 How did (sleep medicine previously used) work for you? Why are 
               
               
                   
                 you looking to move on from (medicine used)? 
               
               
                   
                 Sleep meds to be used concurrently (check boxes with pulldowns for 
               
               
                   
                 frequency and dose) 
               
               
                   
                 Is sleep interrupted by body-related sensations (urination, pain, RLS, 
               
               
                   
                 spasms)? (NLP) 
               
               
                 About User&#39;s 
                 How would you rate the quality of your sleep over the last week? 
               
               
                 Pain/Sleep/Anxiety (Group 3) 
                 (Slider or emoji 0-10 scale) 
               
               
                   
                 How often do you feel anxious or stressed? (NLP) 
               
               
                   
                  NLP output for data model: keywords showing stress 
               
               
                   
                  frequency (all the time, only at night, once a week, etc.) 
               
               
                   
                 Is there a time of day when you tend to feel more stressed/anxious? 
               
               
                   
                 (multiple choice) 
               
               
                   
                 Are you up at night thinking about the day&#39;s stressors? (multiple 
               
               
                   
                 choice) 
               
               
                   
                 When do you generally prefer to unwind? (multiple choice) 
               
               
                   
                 How is your life being impacted? Are your activities limited? Your 
               
               
                   
                 mood changed? (NLP) 
               
               
                   
                  NLP output for data model: model wants to gauge how 
               
               
                   
                  severe/urgent this issue is - also whether a mood lift may be 
               
               
                   
                  needed in addition to addressing the primary issue 
               
               
                   
                 When you feel stressed or anxious, how intense are your feelings? 
               
               
                   
                 (scale) 
               
               
                   
                 What do you think causes the bulk of your stress? (NLP) 
               
               
                   
                 How do you cope with stress? (NLP) 
               
               
                   
                 Thank you. If there&#39;s anything else you&#39;d like to share, please feel 
               
               
                   
                 free. (NLP) 
               
               
                   
                 Cause/diagnosis check boxes (options related to stress/anxiety) 
               
               
                   
                 Anxiety meds to be replaced (check boxes with pulldowns for 
               
               
                   
                 frequency and dose) 
               
               
                   
                 How did (anxiety medicine previously used) work for you? Why are 
               
               
                   
                 you looking to move on from (medicine used)? 
               
               
                   
                 Anxiety meds to be used concurrently (check boxes with pulldowns 
               
               
                   
                 for frequency and dose) 
               
               
                 About the User &amp; Cannabis 
                 Have you used cannabis regularly in the last 60 days? (multiple 
               
               
                   
                 choice) 
               
               
                   
                  How often? (multiple choice) 
               
               
                   
                  Are you looking to replace a current cannabis regimen or add 
               
               
                   
                  to it? (multiple choice) 
               
               
                   
                  Why are you looking to replace a current cannabis regimen 
               
               
                   
                  or add to it? (NLP) 
               
               
                   
                   NLP output. Need to discern what about their 
               
               
                   
                   current regimen they don&#39;t like/what is missing, 
               
               
                   
                   search for key words like not strong enough, still 
               
               
                   
                   suffering from X, drowsy, anxious, etc. 
               
               
                   
                  What products and doses have you used most successfully in 
               
               
                   
                  the past? (up to 3 open text field pairings) 
               
               
                   
                  What products and doses have proved least successful in the 
               
               
                   
                  past? (up to 3 open text field pairings) 
               
               
                   
                  Which dispensary do you prefer to shop? 
               
               
                   
                 Are you open to using products that cause you to feel intoxicated? 
               
               
                   
                 (multiple choice) 
               
               
                   
                 At which times of the day would it be unwise for you to feel 
               
               
                   
                 intoxicated? (time sliders giving the option to indicate preference 
               
               
                   
                 variation for intoxication at different times of day) 
               
               
                   
                 Forms to avoid (multiple choice) 
               
               
                   
                 How much are you willing to spend on cannabis per month? (cost 
               
               
                   
                 slider of a range) 
               
               
                 About the User 
                 Which body type is closest to your own? (pick illustration) 
               
               
                   
                 Do you have any of the following chronic conditions? (check boxes 
               
               
                   
                 and open text option) 
               
               
                   
                 Do you have any of the following allergies? (check boxes and open 
               
               
                   
                 text option) 
               
               
                   
                 Are you taking any of the following medications? (multiple choice) 
               
               
                   
                 At about what time do you typically go to bed? (drop down menu) 
               
               
                   
                 What should you know about you that will make our collaboration 
               
               
                   
                 more successful - and feel most right? (personality and temperament 
               
               
                   
                 check boxes) 
               
               
                   
                 What do you most often do when you want to lift your mood? 
               
               
                   
                 (activity check boxes) 
               
               
                   
                 Anything else we should know that will be helpful to our 
               
               
                   
                 collaboration? (NLP) 
               
               
                   
               
            
           
         
       
     
     The NLP model  204  receives the user input data (e.g., open voice to text and text entry inputs) from the user profile collector  202 . The NLP model  204  may process the user input data into probabilistic multiple choice outputs that can be used by other models in the IT platform  200 , such as the diagnosis model  206 , the regimen recommendation models  208 , and the feedback processing model  220 . An example of an open voice input may include a question that asks the user to talk about their pain diagnosis. 
     The NLP model  204  may classify (e.g., group, organize) the text into predefined diagnosis categories to match the response to a specific diagnosis, and generate probabilistic values based on the classification. For example, the NLP model  204  may be X % certain that the user has arthritis, and Y % certain that the user has another condition. The NLP model  204  may then send this form of input into the diagnosis model  206  using the probabilistic values for each diagnosis category. The NLP model  204  may analyze (e.g., process) voice notes for tone, mood, stress level, etc. In some embodiments, the NLP model  204  may send the user input data (e.g., user profile data) to the diagnosis model  206 . 
     The diagnosis model  206  may be configured to determine and select which regimen recommendation model  208  (sometimes referred to as, “malady model) would be the best fit for the user and launch (e.g., run) the selected regimen recommendation model  208 . In some embodiments, the diagnosis model  205  may be a Bayesian model, which is a statistical model that uses probability to represent all uncertainty within the Bayesian model, and where the uncertainly may include the uncertainty regarding the output and the uncertainty regarding the input (e.g., parameters) to the Bayesian model. A malady model may include models that provide specific regimen recommendations for pain, sleep, anxiety, and/or other medical or wellness conditions like athletic recovery, stress relief, social enjoyment, personal fulfillment, depression, etc. In some embodiments, the diagnosis model  206  may determine certain user buckets, such as personality type, and user type based on cannabis experience. In some embodiments, the diagnosis model  206  may be included in one or more of the regimen recommendation models  208 . 
     The diagnosis model  206  may be configured to receive the user input data (e.g., as collected by the user profile collector  202 ) and the probabilistic values from the NLP model  204 . 
     The diagnosis model  206  may be configured to generate a diagnosis model output indicative of the ideal regimen recommendation model for that user, as well as the probability of varying person types, such as the probability of being “happy go lucky”, or how much of an experienced user someone is. The diagnosis model  206  may store the diagnosis model output (e.g., ideal regimen recommendation model for that user and the probability of varying person types) in the user&#39;s profile in the user data store  226 . The regimen recommendation model  108  may retrieve the diagnosis model output from the user data store  126  and use as probabilistic inputs. 
     The IT platform  200  may train (e.g., manually or automatically) the diagnosis model  206  using research data associated with cannabis, clinical trials associated with cannabis, and/or data indicative of physician expertise and experience associated with cannabis. In some embodiments, the IT platform  200  may be configured to further train (e.g., tune) the diagnosis model  206  by matching physician recommendations to the model output. The IT platform  200  may continually re-train (e.g., update) the diagnosis model  206  using the dataset generated from one or more of the users (e.g., patients) of the intelligent telehealth platform  200 , their user profile data, all their feedback inputs (sometimes referred to as, “feedback data”), the careplan attributes for recommended products by phase, and/or their progress in well-being and activity improvement metrics. In some embodiments, the IT platform  200  may estimate the one or more probabilities in each node of the diagnosis model  206  based on trial results and physician knowledge and experiences to represent the uncertainty throughout the model. 
     The diagnosis model  206  may include (or be associated with) a patient success node that is indicated as the target node that may be optimized. The patient success node may be based on how well that type of user does with the output from the model recommended. The outputs from the feedback processing model  220  for patient satisfaction may be used as inputs for this model to provide evidence for the patient success node. For example, a user may have both anxiety and pain, and which regimen recommendation model  208  to run may be less certain between those two. When data is used from similar users, the IT platform  200  may optimize for the patient success node and be able to determine which model works more frequently for these types of users. The IT platform  200  may find that the pain model may be better suited for older users who suffer from both pain and anxiety, while younger users may benefit more from addressing the anxiety first. 
     The IT platform  200  may use parameter estimation and/or parameter updating to update the model. Parameter estimation may be used to update the probability weightings within the nodes to optimize for patient satisfaction. Parameter updating may be used to update the connections between the nodes to also optimize for patient satisfaction. In some embodiments, certain nodes within the model can be replaced by machine learning (ML). For example, whether or not a user is “happy go lucky” can be determined outside of the Bayesian model using ML techniques, and the output of that can populate the “happy go lucky” diagnosis directly. ML techniques could include regression, classification, clustering, dimensionality reduction, neural nets and deep learning, reinforcement learning, natural language processing, association models, causation models, reinforced learning, etc. These models may replace the Bayesian models, or they may run outside of the Bayesian models and generate outputs that can be used as inputs to the Bayesian. 
     The regimen recommendation model  209  may be configured as a Bayesian model that generates (outputs) all of the care plan attributes. For each day, sessions are recommended to fill the day parts. Each session may have a start time, recommended product types (e.g., form and cannabidiol:tetrahydrocannabinol (CBC:THC) ratio), recommended doses, and/or recommended use type (e.g., directed versus optional). The product form is output in probabilities, so that the highest recommended form has the highest probability. Any forms the model recommends completely avoiding are given a probability of 0. 
     As discussed below, the regimen recommendation model  209  updates its recommendation when new feedback inputs are provided. The inputs for the feedback are processed in the feedback processing model  220 . The new recommendation is used to generate an updated careplan draft for the next phase of the process. In some embodiments, the feedback processing model  220  may also ingest one or more of the inputs (as discussed herein) that are ingested by the user profile collector  202 . 
     The IT platform  200  may train the regimen recommendation model  209  using research data associated with cannabis, clinical trials associated with cannabis, and/or data indicative of physician expertise and experience associated with cannabis. In some embodiments, the IT platform  200  may be configured to further train (e.g., tune) the regiment recommendation model  208  by matching physician recommendations to the model output. The IT platform  200  may continually re-train (e.g., update) the recommendation model  209  using the dataset generated from one or more of the users of the intelligent telehealth platform  200 , their user profile data, all their feedback inputs, the careplan attributes for recommended products by phase, and/or their progress in well-being and activity improvement metrics. In some embodiments, the IT platform  200  may estimate the one or more probabilities in each node of the recommendation model  209  based on trial results and physician knowledge and experiences to represent the uncertainty throughout the model. 
     The recommendation model  209  may include (or be associated with) a patient success node that is indicated as the target node that may be optimized. The patient success node may be based on patient well-being metrics, progress on their activities of daily living (ADLs) (e.g., which is output by the feedback processing model  220 ), specifics of each phase of their careplan, how each phase was modified, and/or how the update worked for the user. 
     The IT platform  200  may use parameter estimation and/or parameter updating to update the model. Parameter estimation may be used to update the probability weightings within the nodes to optimize for patient satisfaction. Parameter updating may be used to update the connections between the nodes to also optimize for patient satisfaction. In some embodiments, certain nodes within the model can be replaced by machine learning (ML). For example, whether or not a user is “happy go lucky” can be determined outside of the Bayesian model using ML techniques, and the output of that can populate the “happy go lucky” diagnosis directly. 
     The inventory model  210  may include all the products in the product pool (e.g., in the product inventory storage  222 ). The inventory model  210  may tag each product for attributes related to that product. In some embodiments, a tag may include CBD:THC type, other dominant cannabinoids, dominant terpenes, dispensary availability, product descriptions, whether or not the product is full spectrum or not, allergens, form, etc. 
     The inventory model  210  may automatically tag (e.g., assign) the inventory for dose conversions, so that each product is able to be presented in an easy to follow dose (e.g., drops, gummies, squares of chocolate, etc.) instead of a milligram (mg) amount. 
     The inventory model  210  may tag the individual products in the inventory with collective user scores. Each product that has been recommended and used is scored by the inventory model  210  as collective user feedback data comes in for effectiveness for varying maladies and for typical feeling states induced. In some embodiments, these scores include pain_relief_score, sleep_score, etc. They are also scored based on how well tolerated they are at different times of the day. 
     The inventory model  210  may be a machine learning model, a Bayesian model, or a rules-based model. The inventory model  210  may consist of ranking and filtering through the inventory to find a product that matches the attributes that were output from the regimen recommendation model and scores the highest based on the review score tags. The inventory model  210  was developed using physician guidance for determining the ranking and filtering logic. 
     The inventory model  210  uses product scores and other inputs to select products for the user. The inventory model  210  selects the closest dispensary to the user that matches the medical card status. The inventory model  210  then ranks and filters (e.g., based on user and care plan attribute information from the regimen recommendation model  208 ) the products from the selected dispensary. The inventory model  210  filters out products based on allergens, THC type, and/or forms that have a probability of 0. 
     The inventory model  210  then ranks (e.g., prioritizes, orders, sorts) the remaining products based on their scores for malady (e.g., disease, pain, sleep, anxiety), day part score, clarity, and/or energy, etc. The inventory model  210  selects the highest scoring product for the user for each product role. 
     The instructions model  212  may be a machine learning model, a Bayesian model, or a rules-based model. The inputs of the instructions model  212  may include the specific product information included in the recommendation, the dosages recommended, as well as user profiling inputs like medical conditions, a wellness condition, use preferences, user experience, history with certain cannabis forms, past side effects, set and setting history and feedback, other medication usage, and/or other feedback or profiling inputs provided. The output of the instructions model  212  is user-specific product use instructions, expected effect onset and durations for each session, set and setting recommendations, general guidance, as well as specific use instructions given a user&#39;s incoming physical and mental state or situation, and any relevant session warnings based on a user&#39;s specific profile (e.g., user input profile). 
     The instructions model  212  runs ahead of the careplan approval, and provides instructions for use given the status of the user. Additionally, instructions for use are also provided assuming different potential states of the user. This could include things like higher pain than normal, an extra stressful situation, etc. Modified use instructions are then provided and approved based on these situations, so that should they arise, the instructions model  112  can update the instructions in real-time. For example, if the user is in more pain than usual, a higher dose of a product may be recommended. This model was developed using a combination of available research data and medical expertise from cannabis physicians. 
     The why recommended model  224  may be a machine learning model, a Bayesian model, or a rules-based model. The why recommended model  224  is configured to generate a model output describing reasons for the product selections and use times that were made. The inputs to the why recommended model  224  may include one or more of the following: user profile data (e.g., collected from both profiling and feedback); information about the user&#39;s preferences regarding form, mental clarity, desired energy, and relaxation by day; information about the user&#39;s experience with cannabis; information about different medical conditions a user (which may also drive the recommendation for certain product types); the products selected for the user; and/or the outputs from the diagnosis model  206  regarding person type. 
     The outputs of the why recommended model  224  are divided into categories that include dosing, product mix, bedtime, accessories, and/or cost. Each output is a string of copy that populates the “Why Recommended” section of the telehealth application  160 . The specific copy that is output is determined by the recommended model  224 . 
     The IT platform  200  may train (e.g., manually or automatically) the why recommended model  224  using research data associated with cannabis, clinical trials associated with cannabis, and/or data indicative of physician expertise and experience associated with cannabis. In some embodiments, the IT platform  200  may be configured to further train (e.g., tune) the why recommended model  224  by matching physician recommendations to the model output. The IT platform  200  may continually re-train (e.g., update) the why recommended model  224  using the dataset generated from one or more of the users of the intelligent telehealth platform  200 , their user profile data, all their feedback inputs, the careplan attributes for recommended products by phase, and/or their progress in well-being and activity improvement metrics. In some embodiments, the IT platform  200  may estimate the one or more probabilities in each node of the why recommended model  224  based on trial results and physician knowledge and experiences to represent the uncertainty throughout the model. 
     As the user uses the products (e.g., during treatment) and follows the careplan that was produced by the IT platform  200 , the feedback collector  216  may collect a series of inputs (sometimes referred to as, “feedback inputs” or “feedback data”) from the user. In some embodiments, the feedback collector  216  may collect the series of feedback inputs from the user by sending data (e.g., instructions, code, data, questions, etc.) to the telehealth application  160  of the client device  150  to cause the telehealth application  160  to present a series of questions to the user on a display and send the user&#39;s responses to each of the questions to the feedback collector  216 . In some embodiments, the data that the telehealth application  160  sends to the telehealth application  160  may include (e.g., locally stored on client device  150 ) the series of questions. In some embodiments, the telehealth application  160  may already have a local copy of the series of questions, such that the data sent from the telehealth application  160  is only used to instruct the telehealth application  160  to begin presenting the series of questions. 
     The user may provide (e.g., transmit, send) the feedback inputs to the feedback collector  216  via the telehealth application  160  executing on the client device  150 . In some embodiments, feedback inputs may include information related to pain level before and during use, mood, ADL performance, well-being, and/or sleep quality, etc. In some embodiments, the feedback inputs are collected via the telehealth application  160  using multiple choice, sliders, text entry, and via open voice to text. The feedback collector  216  may store the feedback inputs in the user&#39;s profile in the user data store  226 . 
     TABLE 2 shows non-limiting examples of questions that the feedback collector  216  (and/or the telehealth application  160 ) may generate and present to the user of client device  150  in order to collect feedback inputs about the user&#39;s experience using the product and following the careplan. Each question is grouped according to a time period in which the questions are asked and associated with a presentation method (e.g., multiple choice, location selector, NLP, etc.). In some embodiments, the feedback collector  216  may be a Bayesian model that is configured to generate the questions in TABLE 2. The feedback collector  216  may present one or more of the examples questions to the user in any order and using any presentation method. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 EXAMPLE QUESTIONS FOR COLLECTING FEEDBACK INPUTS 
               
               
                 DURING AND/OR AFTER TREATMENT (QUESTION TYPE) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 Checking-In Feedback 
                 It&#39;s been about [time period] since you used your drops. How&#39;s your 
               
               
                 (i.e., at some point during the 
                 pain level now? (Stanford Pain Scale Slider) 
               
               
                 course of treatment) 
                 Describe any side effects or notable pleasant/unpleasant sensations? 
               
               
                   
                 (NLP) 
               
               
                   
                  NLP output for data model: search for side effect keywords 
               
               
                   
                  (dry eyes, paranoia, too intense, anxiety, sleepy, etc.) as well 
               
               
                   
                  as pleasant sensation keywords (elation, euphoria, relaxed, 
               
               
                   
                  giggly, etc.) 
               
               
                 End of Session Feedback 
                 It&#39;s been about [time period we expect to conclude the session] since 
               
               
                   
                 you used your drops. How&#39;s your pain level now? (Stanford Pain 
               
               
                   
                 Scale Slider) 
               
               
                   
                 Rate your session (1-5 stars) 
               
               
                   
                 Provide a short review of your session. (NLP) 
               
               
                   
                  NLP output for data model: if session had low stars, search 
               
               
                   
                  for keywords like felt nothing, didn&#39;t work, etc. Model will 
               
               
                   
                  also track the output of the stress level in the user&#39;s voice 
               
               
                   
                  compared to their stress level prior to starting to be used in 
               
               
                   
                  combination with the star rating. 
               
               
                 End of Day Feedback 
                 Reflect back. How satisfied were you with your experience of 
               
               
                   
                 cannabis today? Consider effectiveness in managing your pain and 
               
               
                   
                 side effects, in particular. (slider) 
               
               
                   
                 Throughout the day, about how often did you feel: (WHO Well-Being 
               
               
                   
                 Scale sliders for each) 
               
               
                   
                  Cheerful &amp; in good spirits? 
               
               
                   
                  Calm &amp; relaxed? 
               
               
                   
                  Attentive &amp; engaged? 
               
               
                   
                  Energetic &amp; vigorous? 
               
               
                   
                 Anything else you&#39;d like to share about today? Any unusual feeling, 
               
               
                   
                 highs, or lows? (NLP) 
               
               
                   
                  NLP output for data model: model will track keywords for 
               
               
                   
                  highs and lows and trend over time. Model will also use the 
               
               
                   
                  output of stress level from user&#39;s voice and track this trend 
               
               
                   
                  over time. 
               
               
                   
               
            
           
         
       
     
     When a user completes a telemedicine session, the telemed session data collector  218  collects the telemedicine session data and automatically enters the telemedicine session data into an administrator (“admin”) portal of the IT platform  200 . In some embodiments, the telemed session data collector  218  may present the telemedicine session data on a display, at which point, a physician may manually enter the telemedicine session data directly into the admin portal in the form of multiple choice inputs. For example, the patient may note that one of their products make them too tired, and the physician can then enter that side effect for that product into the admin portal. These inputs are then able to be used by the feedback processing model  220  to provide more evidence for an updated careplan. 
     The feedback processing model  220  is a Bayesian model that is configured to process all the feedback data collected from the user. This includes the in-session feeling indications, as well as weekly survey results where they indicate how each product is working and how the plan is working for them in general. Additional feedback from the telemed session data collector  218  may also be included if available. Even if the IT platform  200  determines that there is missing (or no) data, then the model may still decide to run without it. The model will incorporate as much or as little feedback as is available, using prior probabilities (e.g., associated with a prior run of the model) when feedback data is missing. The model is designed with specific nodes and probabilities in order to weigh all the available evidence and determine the update to the plan that is needed, with a degree of certainty. Outputs include nodes such as THC adjustment (whether or not to increase or decrease the THC for a given feeling state), dose adjustment (whether or not the recommended dose for a given feeling state should increase or decrease), and/or target strain. The probabilistic outputs of this model are then used as additional inputs into the regimen recommendation model  208  to provide an updated recommendation. The feedback processing model  220  computes overall progress and satisfaction, as well as the most likely adjustments needed to the careplan. Although  FIG. 2  shows the feedback processing model  220  as a single model, in some embodiments, the feedback processing model  220  may include a plurality of feedback processing models  220  that are interconnected to process all the feedback data collected from the user. 
     The IT platform  200  may train the feedback processing model  220  using research data associated with cannabis, clinical trials associated with cannabis, and/or data indicative of physician expertise and experience associated with cannabis. In some embodiments, the IT platform  200  may be configured to further train (e.g., tune) the feedback processing model  220  by matching physician recommendations to the model output. The IT platform  200  may continually re-train (e.g., update) the feedback processing model  220  using the dataset generated from one or more of the users of the intelligent telehealth platform  200 , their user profile data, all their feedback inputs, the careplan attributes for recommended products by phase, and/or their progress in well-being and activity improvement metrics. In some embodiments, the IT platform  200  may estimate the one or more probabilities in each node of the feedback processing model  220  based on trial results and physician knowledge and experiences to represent the uncertainty throughout the model. 
     The feedback processing model  220  may include (or be associated with) a patient success node that is indicated as the target node that may be optimized. The patient success node may be based on patient well-being metrics, progress on their activities of daily living (ADLs) (e.g., which is output by the feedback processing model  220 ), specifics of each phase of their careplan, how each phase was modified, and/or how the update worked for the user. 
     The IT platform  200  may use parameter estimation and/or parameter updating to update the model. Parameter estimation may be used to update the probability weightings within the nodes to optimize for patient satisfaction. Parameter updating may be used to update the connections between the nodes to also optimize for patient satisfaction. In some embodiments, certain nodes within the model can be replaced by machine learning (ML). For example, whether or not a user is “happy go lucky” can be determined outside of the Bayesian model using ML techniques, and the output of that can populate the “happy go lucky” diagnosis directly. 
     The IT platform  200  tags the inventory in the product inventory store  222  by time of day use scores, feeling state use scores (e.g., energy, mental clarity, relaxation, etc.), malady score (e.g., disease, pain, anxiety, sleep, etc.), CBD:THC type, form, allergens, cannabinol (CBN), cannabigerol (CBG), cannabichromene (CBC), cannabidiol (CBD), etc.), terpenes, dose to product conversions, etc. 
     The IT platform  200  may use the collective user reviews and data (e.g., from the collective user reviews and data store  228 ) to tag the inventory in the product inventory store  222 . The IT platform  200  may use the scores (e.g., time of day use score, feeling state use score, malady score) to generate a recommendation. 
     The final careplan generator  224  is configured to send the final careplan to the telehealth application  160  of the client device  150  to cause the telehealth application  160  to present the final careplan to the user on a display. The final careplan it presented to the user after both profiling and after each phase, and includes the recommended products, dosages, times of day for use, instructions, and copy explaining why the IT platform  200  chose the products in the final careplan. In addition, the user is able to view their progress at any time through their patient progress portal. 
     The user data store  226  is configured to store the user profile data for all users of the IT platform  200 . Each of the models of the IT platform  200  are able to store their respective data in the user data store  226 . User profile data may include every piece of data the IT platform  200  gathers about a user, such as all of the data the user provides during the profile phase, all of the feedback data the user provides during and after treatment, the user&#39;s progress toward well-being, all of the products they&#39;ve used, doses, use times, etc. and how effective the products have been at making the user feel how they want to feel. In some embodiments, the IT platform  200  may organize the data in the user data store  226  into an ontology to improve the efficiency in which the IT platform  200  can retrieve the data from the user data store  226 . 
     The collective user reviews and data store  228  is configured to store all of the user feedback and reviews into a dataset that can be used to tag the inventory. Each time a user leaves feedback on a certain product, that feedback is processed by the feedback processing model  220  to output a score for that product based on attributes like pain, energy, time of day use, etc. The resulting score from that user is added to the dataset of existing scores for that product, and the average/median score is used to tag the inventory. The data is also organized by person type and other diagnoses, so each person type may have a different score for relaxation, for example. 
     Prior to the final careplan being sent to the user, the instructions model  212  may send the careplan to a physician review  230  (e.g., physicians, medical group) to review and approve all aspects of the careplan (e.g., regimen, products, doses, why recommended copy, instructions, use times, patient feedback, etc.) and modify the careplan, if needed. The physician review  230  may then send the approved careplan to the final careplan generator  224 . 
     As discussed herein, a Bayesian model may be used in place of a rules-based model to provide several benefits. A first benefit is that a Bayesian model allows the IT platform  200  to run the Bayesian model with an incomplete dataset. For example, the IT platform  200  may be missing one or more datapoints from a user (e.g., a user may miss one or more sessions, fail to provide feedback, or not know responses to some of the questions asked during the profiling phase), but the Bayesian model can use assume priors to put a reasonable guess into what that datapoint would be. While, the overall uncertainty may increase, the Bayesian model would still be able to produce a result. Additionally, as data is collected, these priors become better informed, and can actually decrease the amount of upfront information the IT platform  200  would need to collect from a user. This is especially important given the nature of the NLP inputs that the IT platform  200  collects, as there could be a higher likelihood of not receiving complete answers. 
     A second benefit is the scaling ability when using a Bayesian model. Instead of hard cut-offs like in rules, a Bayesian approach puts a value of certainty to each input. The certainties for all the inputs are then combined to produce a decision. Each decision is weighted, so that the best and second-best decisions can be seen clearly. This creates a much more flexible platform, and allows for better-informed adjustments when the IT platform  200  receives user feedback. 
     A third benefit is the easy addition or deletion of certain variables. Since utility values are all combined using separate nodes, variables can be added or removed relatively easily, compared to a rules-based platform which would likely require rewriting many of the rules. This provides flexibility on the front end. 
     A fourth benefit is that IT platform  200  (or administrator) may have greater visibility into the activities of each of the models, such that if there is an unexpected output, then the IT platform  200  may quickly identify the model causing the unexpected output. This also gives the IT platform  200  the ability to generate data that is indicative of the reasons for a given recommendation. 
     A fifth benefit is that once data becomes available, then the same network can be updated based on the data. This allows the same network to be used instead of the need to develop a new machine learning model. It also allows the IT platform  200  (or administrator) to maintain control over the network. For example, the IT platform  200  can limit the effect of the data on the network if the IT platform  200  has a partial dataset (e.g., less than desired). 
       FIGS. 3A-3N  depict various example graphical user interfaces of the telehealth application  160  in  FIG. 1 , according to some embodiments. In some embodiments, the telehealth application  160  may be configured to execute on the host system  110 , where the output of the execution is presented on a screen of the host system  110 . In some embodiments, the telehealth application  160  may be configured to execute on the host system  110 , where the output of the execution is sent to the client device  150  via the network  105  to cause the client device  150  to display the output on a screen of the client device  150 . In some embodiments, some screens of the graphical user interface (GUI) may only be viewable by an administrator of the IT platform  200 , while other screens may only viewable by a user (e.g., a patient) using the IT platform  200 . In some embodiments, the telehealth application  160  may be a web browser. 
       FIG. 3A  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a list of dispensaries stored in a storage of the IT platform  200 , according to some embodiments. As shown in the GUI  300   a , the dispensaries are either tagged as local, meaning they have a physical store, or online, meaning anyone can order the products online. An administrator of the IT platform  200  may use the GUI  300   a  to upload new dispensaries to the IT platform  200  via a file (e.g., a table, a mapping file, a comma separated value (.CSV) file) to cause the IT platform  200  to store the new dispensaries in a storage of the IT platform  200 . 
       FIGS. 3B-3C  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a list of products stored in a storage of the IT platform  200 , as well as the details of each of the products, according to some embodiments. An administrator of the IT platform  200  may use the GUI  300   b  to upload new products to the IT platform  200  via a file (e.g., a table, a mapping file, a comma separated value (.csv) file) to cause the IT platform  200  to store the new products in a storage of the IT platform  200 . As shown in the GUI  300   b , the IT platform  200  tags products for things like category, cannabinoid type, brand, product name and image, dispensary location, and/or cost. As shown in the GUI  300   c , clicking on a product reveals more product details such as allergens, description, total mg, accessories required, etc. The product file (e.g., csv file) may also contain more detail such as day part score, malady score, specific amounts of cannabinoids, full spectrum, etc. 
       FIGS. 3D-3E  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a list of patient profiles stored in a storage of the IT platform  200 , as well as the details of each of the patient profiles, according to some embodiments. An administrator of the IT platform  200  may use the GUI  300   d  to upload new patient profiles to the IT platform  200  via a file (e.g., a table, a mapping file, a comma separated value (.csv) file) to cause the IT platform  200  to store the new patient profiles in a storage of the IT platform  200 . The patient profiles are filterable by the next action needed, as well as by the specific patient info. When the user needs to have a careplan created, a pending tag is shown. When the user needs their plan reviewed again because they are nearing the end of the phase, a review phase tag is shown. In some embodiments, a tag (e.g., a red tag) may indicate that feedback data from a user may need attention. 
     As shown in GUI  300   e , the detail tab contains all of the profiling detail, including all the open-ended responses and medical information from the patient. This is also edit-able, and some of the open-ended responses are translated into multiple choice inputs for the model. Once the profile is ready, a user (or administrator) of the IT platform  200  may create a careplan by clicking the Create Careplan button, which causes one or more components (e.g., models) of the IT platform to ingest the user profile data (and other sets of data discussed herein) to create a draft careplan. 
       FIG. 3F  is an example graphical user interfaces of the telehealth application  160  depicting a method for displaying the model outputs from each of the models of the IT platform  200 , according to some embodiments. As shown, the model outputs are organized into an ontology for more organized tracking of the dataset created. 
       FIGS. 3G-3H  are example graphical user interfaces of the telehealth application  160  depicting a method for displaying a draft careplan created by the models of the IT platform  200 , according to some embodiments. Specifically, GUI  300   g  shows an example phase  1  of a draft careplan and GUI  300   h  shows an example phase  2  of the draft careplan. The IT platform  200  model outputs a draft careplan, filling in the day parts with the recommended product types and doses. Each dose is also converted automatically to a dose the user can understand (e.g., drops, squares of chocolate, gummies, etc.). The IT platform  200  may filter out the products of the careplan based a matching product type. The IT platform  200  may filter out the products from the careplan that are associated with allergies that match the user&#39;s allergies (as learned during the patient profiling phase). The IT platform  200  sorts the products by probability of the best matching product, and the It platform  200  automatically selects the top (e.g., best matching that of the user&#39;s profile data, etc.) product from the list. 
       FIG. 3I  is an example graphical user interface of the telehealth application  160  depicting a method for displaying product specific instructions, according to some embodiments. For example, GUI  300 I may display duration, onset, and general use instructions, as well as product-specific use instructions for each product. 
       FIG. 3J  is an example graphical user interface of the telehealth application  160  depicting a method for displaying the model output of the why recommendation model  114  in  FIG. 1 , according to some embodiments. The IT platform  200  may transmit the draft careplan to a physician to allow the physician to edit and/or approve the draft careplan. 
       FIG. 3K  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a user&#39;s shopping list, according to some embodiments. The user&#39;s shopping list can be viewed and edited, as well as a preview of their entire care plan by day part. 
       FIG. 3L  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a draft careplan created by the models of the IT platform  200 , according to some embodiments. That is, once the plan is ready to go, IT platform  200  (or administrator) may send the draft careplan to a physician (physician review  230  in  FIG. 2 ) for the physician to review the draft careplan, and make edits as needed. When the physician approves the draft careplan, then the user is notified (e.g., via email, phone, text, etc.) that their plan has been finalized/approved, and that their shopping list is ready. 
       FIG. 3M  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a user&#39;s progress in completing the careplan, according to some embodiments. The GUI  300   g  shows the portions of the careplan that the use has completed, and the portion of the careplan that are pending. 
       FIG. 3N  is an example graphical user interface of the telehealth application  160  depicting a method for displaying a notes session to track any communication with the patient, survey data, or physician telemed notes, according to some embodiments. The data in the notes session may be fed back and added to the feedback data that is gathered by the feedback collector  216 . In some embodiments, the telemed notes may be entered automatically via multiple choice selection by the physician while on the call with the patient. 
       FIG. 4  is a flow diagram depicting a method for using the intelligent telehealth platform  200  to generate a recommended treatment plan to treat a malady of a user, according to some embodiments. Method  400  may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions and/or an application that is running/executing on a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, method  400  may be performed by a host system, such as host system  110  in  FIG. 1 . In some embodiments, method  400  may be performed by an IT platform, such as IT platform  200  in  FIG. 2 . In some embodiments, method  400  may be performed by a client device, such as client device  150  in  FIG. 1 . 
     With reference to  FIG. 4 , method  400  illustrates example functions used by various embodiments. Although specific function blocks (“blocks”) are disclosed in method  400 , such blocks are examples. That is, embodiments are well suited to performing various other blocks or variations of the blocks recited in method  400 . It is appreciated that the blocks in method  400  may be performed in an order different than presented, and that not all of the blocks in method  400  may be performed. 
     As shown in  FIG. 4 , the method  400  includes the block  402  of obtaining a user profile dataset associated with a user. The method  400  includes the block  404  of selecting, based on the user profile dataset, a first machine learning model from a set of machine learning models respectively trained to predict cannabis treatment responses. The method  400  includes the block  406  of generating, by a processing device, a recommended treatment plan for using one or more cannabis products to treat a malady of the user based on the user profile dataset and the first machine learning model. The method  400  includes the block  408  of transmitting the recommended treatment plan to a client device to cause the client device to display the recommended treatment plan. 
       FIG. 5  is a block diagram of an example computing device  500  that may perform one or more of the operations described herein, in accordance with some embodiments. Computing device  500  may be connected to other computing devices in a LAN, an intranet, an extranet, and/or the Internet. The computing device may operate in the capacity of a server machine in client-server network environment or in the capacity of a client in a peer-to-peer network environment. The computing device may be provided by a personal computer (PC), a set-top box (STB), a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single computing device is illustrated, the term “computing device” shall also be taken to include any collection of computing devices that individually or jointly execute a set (or multiple sets) of instructions to perform the methods discussed herein. 
     The example computing device  500  may include a processing device (e.g., a general purpose processor, a PLD, etc.)  502 , a main memory  504  (e.g., synchronous dynamic random access memory (DRAM), read-only memory (ROM)), a static memory  506  (e.g., flash memory and a data storage device  518 ), which may communicate with each other via a bus  530 . 
     Processing device  502  may be provided by one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. In an illustrative example, processing device  502  may comprise a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. Processing device  502  may also comprise one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device  502  may be configured to execute the operations described herein, in accordance with one or more aspects of the present disclosure, for performing the operations and steps discussed herein. 
     Computing device  500  may further include a network interface device  508  which may communicate with a communication network  520 . The computing device  500  also may include a video display unit  510  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device  512  (e.g., a keyboard), a cursor control device  514  (e.g., a mouse) and an acoustic signal generation device  516  (e.g., a speaker). In one embodiment, video display unit  510 , alphanumeric input device  512 , and cursor control device  514  may be combined into a single component or device (e.g., an LCD touch screen). 
     Data storage device  518  may include a computer-readable storage medium  528  on which may be stored one or more sets of instructions  525  that may include instructions for one or more components and/or applications  542  (e.g., IT component  125  in  FIG. 1 ) for carrying out the operations described herein, in accordance with one or more aspects of the present disclosure. Instructions  525  may also reside, completely or at least partially, within main memory  504  and/or within processing device  502  during execution thereof by computing device  500 , main memory  504  and processing device  502  also constituting computer-readable media. The instructions  525  may further be transmitted or received over a communication network  520  via network interface device  508 . 
     While computer-readable storage medium  528  is shown in an illustrative example to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform the methods described herein. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media and magnetic media. 
     Unless specifically stated otherwise, terms such as “obtaining,” “selecting,” “generating,” “transmitting,” or the like, refer to actions and processes performed or implemented by computing devices that manipulates and transforms data represented as physical (electronic) quantities within the computing device&#39;s registers and memories into other data similarly represented as physical quantities within the computing device memories or registers or other such information storage, transmission or display devices. Also, the terms “first,” “second,” “third,” “fourth,” etc., as used herein are meant as labels to distinguish among different elements and may not necessarily have an ordinal meaning according to their numerical designation. 
     Examples described herein also relate to an apparatus for performing the operations described herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computing device selectively programmed by a computer program stored in the computing device. Such a computer program may be stored in a computer-readable non-transitory storage medium. 
     The methods and illustrative examples described herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear as set forth in the description above. 
     The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples, it will be recognized that the present disclosure is not limited to the examples described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled. 
     As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, and/or “including”, when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Therefore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 
     It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     Although the method operations were described in a specific order, it should be understood that other operations may be performed in between described operations, described operations may be adjusted so that they occur at slightly different times or the described operations may be distributed in a system which allows the occurrence of the processing operations at various intervals associated with the processing. 
     Various units, circuits, or other components may be described or claimed as “configured to” or “configurable to” perform a task or tasks. In such contexts, the phrase “configured to” or “configurable to” is used to connote structure by indicating that the units/circuits/components include structure (e.g., circuitry) that performs the task or tasks during operation. As such, the unit/circuit/component can be said to be configured to perform the task, or configurable to perform the task, even when the specified unit/circuit/component is not currently operational (e.g., is not on). The units/circuits/components used with the “configured to” or “configurable to” language include hardware—for example, circuits, memory storing program instructions executable to implement the operation, etc. Reciting that a unit/circuit/component is “configured to” perform one or more tasks, or is “configurable to” perform one or more tasks, is expressly intended not to invoke 35 U.S.C. § 112, sixth paragraph, for that unit/circuit/component. Additionally, “configured to” or “configurable to” can include generic structure (e.g., generic circuitry) that is manipulated by software and/or firmware (e.g., an FPGA or a general-purpose processor executing software) to operate in manner that is capable of performing the task(s) at issue. “Configured to” may also include adapting a manufacturing process (e.g., a semiconductor fabrication facility) to fabricate devices (e.g., integrated circuits) that are adapted to implement or perform one or more tasks. “Configurable to” is expressly intended not to apply to blank media, an unprogrammed processor or unprogrammed generic computer, or an unprogrammed programmable logic device, programmable gate array, or other unprogrammed device, unless accompanied by programmed media that confers the ability to the unprogrammed device to be configured to perform the disclosed function(s). 
     The foregoing description, for the purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the embodiments and its practical applications, to thereby enable others skilled in the art to best utilize the embodiments and various modifications as may be suited to the particular use contemplated. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the present disclosure is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.