WO2021084523A2

WO2021084523A2 - Enhancing lifestyle of memory impaired patients using cbd

Info

Publication number: WO2021084523A2
Application number: PCT/IB2020/060278
Authority: WO
Inventors: Michael CABIGON; Steven Splinter; Denis TASCHUK
Original assignee: Radient Technologies Innovations Inc.
Priority date: 2019-10-31
Filing date: 2020-11-02
Publication date: 2021-05-06
Also published as: WO2021084523A3

Abstract

Systems and methods for assisting dementia patients are provided. This system also aids clinicians, family members, and caregivers as they treat and support dementia patients. In this system, patients are treated with CBD (cannabidiol)-containing medications for treatment of dementia-related symptoms. These medications may be enhanced through the inclusion of adjuvants such as terpenes, terpenoids, and anti-inflammatory compounds. On a scheduled basis, dementia-related symptoms are assessed using a variety of tests. This system also utilizes smart devices that not only assist in testing, but also in data tracking and analysis. Furthermore, this system includes the use of wearable sensors to measure patients' dementia-related symptoms. These devices communicate via cloud-based services; data collected is stored in a centralized system. These data may also be accessible to researchers, drug companies, pharmacies and other health service organizations.

Description

ENHANCING LIFESTYLE OF MEMORY IMPAIRED PATIENTS USING CBD

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority benefit of U.S. provisional patent application number 62,929,029, filed October 31, 2019, the disclosure of which is incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

[0002] The present disclosure is generally related to the use of cannabinoid-containing plant extracts in the prevention or treatment of cogitative disorders or neural degeneration. More specifically, the present disclosure is related to helping individuals such as clinicians, caregivers, and family members aid in the treatment of dementia patients.

2. Description of the Related Art

[0003] In recent years, various ailments that result in cognitive decline or dementia in individuals have increased. Dementia is an umbrella term that describes a group of symptoms associated with a decline in memory or other cognitive skills. Symptoms are severe enough to reduce a person's ability to perform everyday activities. Dementia patients include those with Alzheimer's disease, those who develop vascular dementia following a stroke, and those whose dementia arise from a variety of other conditions (i.e., thyroid problems, vitamin deficiencies, etc.). While symptoms of dementia can vary greatly, at least two of the following core mental functions must be significantly impaired: memory, communication and language, ability to focus and pay attention, reasoning and judgment, and visual perception. People with dementia may have difficulties making new short-term memories, planning and preparing meals, remembering appointments, maintaining organization, and/or traveling out of the neighborhood. Dementia also strongly impacts the family friends of the patient and current treatments remain inadequate. While some early evidence suggests cannabinoids such as cannabidiol (CBD) may be an effective treatment for some of the symptoms of dementia, the proper dosing and treatment regimen are unknown.

[0004] What are needed are improved systems and methods for managing treatment and care related to symptoms of dementia. What are also needed are systems that can assist clinicians, family members, and caregivers in treating, supporting, and caring for dementia patients.

SUMMARY OF THE PRESENLTY CLAIMED INVENTION

[0005] The presently claimed invention may relates to a method, a non-transitory computer readable storage medium, and an apparatus for identifying treatments for symptoms of dementia. In a first embodiment a method may receive a first set test data associated with a test performed on a patient, identify that the first set of test data is consistent with symptoms of dementia, classify the patient as a dementia patient based on the first set of test data, and identify a cannabinoid dosage to administer to the patient. The cannabinoid dosage may be administered to the patient after the identification of the cannabinoid dosage. This method may also include receiving a second set of test data associated with the test performed on the patient after the patient has consumed the cannabinoid dosage, the second test may have been performed based at least in part on the patient being classified as the dementia patient.

[0006] In a second embodiment the presently claimed method may be implemented as a non-transitory computer readable storage medium where a processor executes instructions out of a memory. Here again the method may include receiving a first set test data associated with a test performed on a patient, identifying that the first set of test data is consistent with symptoms of dementia, classifying the patient as a dementia patient based on the first set of test data, and identifying a cannabinoid dosage to administer to the patient. The cannabinoid dosage may be administered to the patient after the identification of the cannabinoid dosage. This method may also include receiving a second set of test data associated with the test performed on the patient after the patient has consumed the cannabinoid dosage, the second test may have been performed based at least in part on the patient being classified as the dementia patient.

[0007] In a third embodiment of the present invention may be implemented as an apparatus that includes a processor that executes instructions out of a memory to implement the presently claimed method. Here again the method may include receiving a first set test data associated with a test performed on a patient, identifying that the first set of test data is consistent with symptoms of dementia, classifying the patient as a dementia patient based on the first set of test data, and identifying a cannabinoid dosage to administer to the patient. The cannabinoid dosage may be administered to the patient after the identification of the cannabinoid dosage. This method may also include receiving a second set of test data associated with the test performed on the patient after the patient has consumed the cannabinoid dosage, the second test may have been performed based at least in part on the patient being classified as the dementia patient.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] FIG. 1 illustrates an exemplary network environment in which systems for managing care of memory-impaired/dementia patients may be implemented.

[0009] FIG.2 is a flowchart illustrating an exemplary clinician smart device method executable within systems for managing care of memory-impaired/dementia patients.

[0010] FIG.3 illustrates an exemplary graphic user interface that may be displayed by smart devices in systems for managing care of memory-impaired/dementia patients.

[0011] FIG.4 is a flowchart illustrating an exemplary caregiver device method executable within systems for managing care of memory-impaired/dementia patients.

[0012] FIG. 5 is a flowchart illustrating an exemplary family/patient device method executable within systems for managing care of memory-impaired/dementia patients.

[0013] FIG. 6 is a flowchart illustrating an exemplary wearable device method executable within systems for managing care of memory-impaired/dementia patients.

[0014] FIG. 7 is a flowchart illustrating an exemplary method for identifying effective dosages of medications.

[0015] FIG. 8 is a flowchart illustrating an exemplary method for applying artificial intelligence to analyze patient data.

[0016] FIG. 9 is a flowchart illustrating an exemplary caregiving device method for making recommendations based on patient data.

[0017] FIG. 10 is a flowchart illustrating an exemplary method for researching cannabinoid- based medications.

[0018] FIG. 11 illustrates an exemplary computing system that may be used to implement an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Embodiments of the present invention include a system and method for using cannabinoids, including but not limited to cannabidiol CBD to treat or manage symptoms of Alzheimer's disease, dementia, and age-related neurological disorders. For illustrative purposes, the present disclosure is described for treatments using CBD, but one skilled in the art will appreciate that the treatments and technology disclosed may equally be applied to treatments using any relevant cannabinoid or any combination of cannabinoids. Such treatments may also be combined with other medications or "adjuvants" known in the art. A system consistent with the present disclosure may include an electronic device that is communicatively coupled to other devices or networks via wireless protocols. Such systems may be operated at least to some extent interactively by a clinician or caregiver.

[0020] Systems and methods for assisting dementia patients are provided. This system also aids clinicians, family members, and caregivers as they treat and support dementia patients. In this system, patients are treated with CBD (cannabidiol)-containing medications for treatment of dementia-related symptoms. These medications may be enhanced through the inclusion of adjuvants such as terpenes, terpenoids, and anti-inflammatory compounds. On a scheduled basis, dementia-related symptoms are assessed using a variety of tests (physiological tests, memory tests, language skills tests, etc.) Patients' scores on these tests are recorded and tracked over time. This system also utilizes smart devices and applications for the caregivers, family members, and clinicians. These devices not only assist in testing, but also in data tracking and analysis. Furthermore, this system includes the use of wearable sensors to measure patients' dementia-related symptoms. These devices may communicate via cloud-based services, where data collected may be stored in a centralized system. These data may also be accessible to researchers, drug companies, pharmacies and other health service organizations. Utilizing patient and historical case data, this system is able leverage AI engines and machine learning to determine improved treatment plans for dementia patients.

[0021] FIG. 1 illustrates an exemplary network environment in which systems for managing care of memory-impaired/dementia patients may be implemented. The network environment illustrated in FIG. 1 includes clinician device 110, caregiver device 115, family device 120, and patient device 125. FIG. 1 also includes various other computing devices that include dementia usage network 130, patient wearable 135, medical test facility 145, pharmacy network 155, third party network 160, drug company network 165, and cannabidiol (CBD) network 170. Each of the various devices in FIG. 1 may send and receive data via cloud or Internet management system 150. Pharmacy network 155, third party network 160, drug company network 165, and CBD manufacturing network 170 may each be or include a computer server configured to provide data to other computing devices on demand.

[0022] Smart device 110, 115, 120, and 125 may each include respective software modules, graphical user interfaces (GUI), databases (memory), and communication interfaces. While not illustrated in FIG. 1, the devices in FIG. 1 may each include a processor that executes instructions of respective software modules out of memories (e.g., random access memory - RAM) at each respective smart device.

[0023] Clinician smart device 110 may include a smart phone or tablet known in the art for use by a physician or nurse practitioner. Such devices may include software that allows a clinician, such as a physician, to input and review patient symptoms and treatment response, review data from other sources, and determine treatment regimens. Clinician module 110A may include a clinician graphical user interface (clinician GUI 110B), which may be a user interface that allows a clinician to interact with electronic devices through graphical icons and visual indicators. Clinician database llOC may include an organized collection of data pertaining to patients' treatments, testing results, physiological data, and responses that is stored in the clinician smart device. Communication network 110D may include a communication link, that is, hardware and software that enable a smart device or computer to communicate with one another over a network. Exemplary communication networks include, yet are not limited to cellular, Wi-Fi (802.11), or Bluetooth types of wireless networks.

[0024] Caregiver smart device 115 may include an electronic device connected to other devices or networks via wireless protocols and able to operate to some extent interactively and utilized by a caregiver by way of inter alia a smart phone or tablet. Caregiver module 115A may include software that allows a caregiver to input patient data, symptoms, and behavior, allows the caregiver to review data from other sources, and provides the caregiver with possible appropriate response to patient symptoms and behaviors. Caregiver GUI 115B may include a caregiver graphical user interface, that is, a user interface that allows a caregiver to interact with electronic devices through graphical icons and visual indicators. Caregiver database 115C may include an organized collection of data pertaining to the patient's treatment, behavior, and physiological data that is stored in the caregiver smart device.

[0025] Family smart device 120 may include an electronic device connected to other devices or networks via wireless protocols and able to operate to some extent interactively and utilized by a family member of the patient, such as a smart phone or tablet. Family module 120A may include software that allows a family member to input patient data, that allows the family member to review data from other sources, and that provides the family member with alerts about possible problems and support assistance. Family GUI 120B may include a family graphical user interface, that is, a user interface that allows a family member to interact with electronic devices through graphical icons and visual indicators. Family database 120C may include an organized collection of data pertaining to the patient's treatment, behavior, and physiological data, along with data about routine and home life that is stored in the family smart device. Family device 115 of FIG. 1 also includes communication interface 120D that may also be any type of network interface known in the art.

[0026] Patient smart device 125 may include an electronic device, connected to other devices or networks via wireless protocols and able to operate to some extent interactively and utilized by a patient, such as a smart phone or tablet. Patient module 125A may include software that allows a patient to input and save data, send requests for assistance, and review information and respond to requests from clinicians, caregivers, and family members. Patient GUI 125B may include a patient graphical user interface, that is, a user interface that allows a patient to interact with electronic devices through graphical icons and visual indicators. Patient database 125C may include an organized collection of data pertaining to the patient's treatment, behavior, and physiological data, along with data about routine and home life that is stored in the patient smart device. Patient device 125 of FIG. 1 also includes communication interface 125D that may also be any type of network interface known in the art. [0027] Pharmacy network server 155 may include a digital communications network server that sends, receives, and stores information about patient medications. Third party researchers network server 160 may include a digital communications network server that sends, receives, and stores information about patient symptoms, treatment, and response for use by researchers. Drug company network server 165 may include a digital communications network server that sends, receives, and stores information about patient symptoms, treatment, and response for use by a drug company. CBD manufacturers network server 170 may include a digital communications network server that sends, receives, and stores information about patient symptoms, treatment, and response for use by a CBD manufacturer.

[0028] Cloud 150, which may include the Internet, may include networked online storage and communication technology that allows patient and treatment data to be shared between authorized devices.

[0029] Patient wearable 135 may include an electronic device that may be attached to the patient's body for the purpose of collecting physiological data, such as a heart rate monitor. Wearable module 135A may include software that allows the wearable device to collect and send physiological data. Sensor module 135B may include a device or sensors that detects or senses physiological events or changes in the user's body. Sensor module 135B may be or include a heart rate monitor, a blood pressure meter, a skin thermometer, or a sweat detector. Wearable database 135D may include an organized collection of physiological data collected by the wearable device and is stored in the wearable device. Wearable device 135 of FIG. 1 also includes communication interface 135D that may also be any type of network interface known in the art.

[0030] Medical test facility 145 may include an institution that performs any of a wide variety of medical testing procedures and evaluations. Such test procedures may include cognitive tests such as a memory test or laboratory tests, blood tests to measure a level of a given biomarker in the blood of a patient. The medical test facility 145 may practice a method for enhancing the lifestyle of patients with symptoms of dementia, that includes providing a cannabinoid (e.g., tetrahydrocannabinol- THC, [5 - 5.5%] CBD, cannabinol - CBN, or cannabigerol - CBG). Medical test facility 145 may also be instrumental in diagnosing a patient with symptoms of dementia. Such a process may include [orally] administering a dose of the cannabinoid, assessing the symptoms of dementia (or other symptoms - macular degeneration) of the patient, and then update the administered dose of the cannabinoid when symptoms of dementia (and/or macular degeneration) have not improved. This process may also include administering an updated dose that results in improvement of symptoms of dementia (and/or macular degeneration).

[0031] Medical test module 145A may include software that allows for evaluation of a patient's symptoms and mental status and calculates and outputs a suggested medication dosage and composition. Medical test database 145B may include an organized collection of data pertaining to patients' treatments, testing results, physiological data, and responses that is stored in the medical test facility. Medical test facility 145 of FIG. 1 also includes communication interface 145C that may also be any type of communication network interface known in the art. [0032] Dementia usage network 130 of FIG. 1 may include a digital communications network (not illustrated) that sends, receives, and stores information and recommendations about patient medical data, treatments, dementia testing and results, responses, and schedules. Dementia usage network 130 also includes various different software modules. These software modules include a base module, a caregiver module, a clinician module, a treatment module, a research module a patient module, a test module, a CBD manufacturer (MFG) module, a drug company module, and a pharmacy module. While illustrated as separate modules, functions associated with these different modules may be incorporated into any number of software modules.

[0033] The clinician module of FIG. 1 may include software that polls for and receives patient information from various databases over the cloud and sends it to the clinician smart device. Base dementia network module may include software that polls for, receives, and stores patient data from various databases and executes other network modules as needed. Network caregiver module may include software that polls for and receives patient information from various databases over the cloud and sends it to the caregiver smart device. Treatment module may include software that receives patient data and compares it with historically similar data to generate a recommended therapy regimen. Network family module may include software that polls for and receives patient information from various databases over the cloud and sends it to the family smart device. Research module may include software that receives requests from the third-party research network 160 of FIG. 1. The research module of dementia usage network 130 may, create databases of de-identified (anonymous) data, and provides access to this data by researchers. The term de-identified data may refer to anonymous data that does not disclose the identity of patients. Network Patient Module may include software that polls for and receives patient information from various databases over the cloud and sends it to the patient smart device. Test module of the dementia usage network 130 may include software the receives patient data and compares it with historically similar data to generate recommendation for tests to be administered to particular patients.

[0034] CBD manufacturer module of the dementia usage network 130 may include software that receives requests from the CBD manufacturers network server 170, that creates databases of de-identified data, and that provides access to the data by CBD manufacturers. Drug company module of the dementia usage network 130 may include software that receives requests from the third party researchers network server 138, creates databases of de-identified data, and provides access to the data by drug companies. The pharmacy module of FIG. 1 may include software that evaluates requests from the pharmacy network, that creates databases of de- identified data, and that provides access to the data by pharmacies.

[0035] Dementia databases 130A of FIG. 1 may include an organized collection of data pertaining to patients' treatments, testing results, physiological data, and responses that is stored in the dementia usage network. Test databases 130B may include an organized collection of data pertaining to the various tests suitable for diagnosing and describing dementia and is stored in the dementia usage network 130.

[0036] FIG. 2 is a flowchart illustrating an exemplary clinician smart device method executable within systems for managing care of memory-impaired/dementia patients. The steps illustrated in FIG. 2 may be performed by a processor at clinician device executing instructions of the clinician module 110A of FIG. 1. The process begins with receiving patient data from the dementia databases located in the dementia usage network at step 205. Step 205 may also include receiving patient data from the patient database located on the patient smart device, receiving patient data from the caregiver database located on the caregiver smart device, receiving patient data from the family database located on the family smart device, and receiving patient data from the medical test database located in the medical test facility. As such, step 205 may receive data from one or more databases by receiving a series of messages from different devices or by querying the set of devices for relevant patient data.

[0037] A clinician device may receive data, including any alerts, from the patient's wearable device at step 210. Step 210 may also include displaying patient data and any alerts to the clinician for review. After this, a software module at the clinician device may prompt a clinician to update the patient treatment. If no treatment change is required, the clinician device may indicate that the patient's treatment has been reviewed. Next, determination step 215 may identify whether a treatment change has been recommended or elected by the clinician. If no treatment change is elected by the clinician, program flow may move to step 220 where an approval notification may be received when required. This approval may include receiving a verification from the clinician via a user interface confirming that the clinician does not recommend a treatment change. This verification may indicate that the patient has been reviewed and requires no adjustment to treatment at step 220.

[0038] When determination step 215 identifies that a treatment change has been elected, program flow may move to step 225 where a recommendation may be retrieved from the dementia usage network. Alternatively or additionally the dementia usage network validates that a recommendation made by the clinician should be implemented or visa versa. Data or responses received from the dementia usage network may be received/retrieved and displayed in step 225 of FIG. 2. Information displayed on the display may include the recommended dosage and composition that the clinician is authorized to administer to the patent. This new recommendation may include changes to a CBD (cannabinoid) dosage and/or adjuvant composition.

[0039] Program flow may then move to step 230 where a confirmation of the new dosage is received from the clinician. Step 230 may include receiving input from the clinician via a user interface at a clinician device indicating that the clinician may administer the new dosage to the patient in a next administration. In step 235, patient scheduling information may be identified or received from the dementia usage network. Step 235 may also include displaying the identified or received scheduling information in the display of the clinician device. This may allow the clinician to setup appropriate scheduling of follow-up visits, re-testing of the patient, etc., at step 235.

[0040] The method of FIG. 2 may further include sending the updated patient data to the medical test database located in the medical test facility at step 240. Step 240 may also include storing scheduling data at the clinician database located in the clinician smart device, sending the scheduling data to the dementia database 130A located in the dementia usage network 130 of FIG. 1. Determination step 245 may identify whether an alert has been received from the dementia usage network 130 of FIG. 1. When an alert has been received, program flow may move to step 250 to determine whether received information may include a recommended dosage change or a scheduling change. Step 250 may also include displaying the update dosage recommendation to the clinician and displaying receiving recommended tests types, schedules changes, and the updated recommendations that may be reviewed by the clinician.

[0041] FIG. 3 illustrates an exemplary graphic user interface that may be displayed by smart devices in systems for managing care of memory-impaired/dementia patients. Such method may be performed when data associated with the treatment of a patient is collected or evaluated. As such, FIG. 3 may display information collected by clinician device 110, caregiver device 115, family device 120, or patient device 125 of FIG. 1. Furthermore, data displayed in the graphical user interface (GUI) of FIG. 3 may be displayed on any of clinician device 110, caregiver device 115, family device 120, or patient device 125 of FIG. 1. Because of this, a first smart device may be configured to collect and display information that was originally collected by another smart device or by the first smart device itself.

[0042] The GUI of FIG. 3 may include a display window that shows the patient name and/or code that uniquely identifies the patient at patient ID field 310. Medical data field 320 may include a control element which, if activated by the clinician, displays the patient's medical data. Note that the GUI of FIG. 3 also includes several different fields or areas identified as items 330, 340, 350, 360, and 370. [0043] Item 330 of FIG. 3 includes a clinician notes field that may include a control element which, if activated by the clinician, displays the clinician's notes relevant to the patient. Item 330 also includes several other fields: Caregiver data field that may include a control element which, if activated by the clinician, displays information saved by the patient's caregiver: Family data field that may include a control element which, if activated by the clinician, displays information saved by the patient's family: and Patient data field that may include a control element which, if activated by the clinician, displays information saved by the patient. Item 330 also includes historical comparisons data field that may include a control element which, if activated by the clinician or user, displays historical data from the patient's record. Each of these different fields in item 330 may be different selection boxes that may be selected by a user when that user wishes to review clinician notes, caregiver data, family data, patient data, or historical comparison data. All a user need do is make an appropriate selection to review data of a certain category.

[0044] The GUI of FIG. 3 also includes item 340 that may allow a user to view medical test news or a dosage history of a patient. Here again all a user need do is to select an appropriate selection box to review data of a certain type. The medical test facility news field may include a control element which, if activated by the clinician or user, displays updates that have been shared by the medical test facility. The dosage history field may include a control element which, if activated by the clinician, displays the patient's dosage history and relevant information from the medical test facility.

[0045] Also included in the GUI of FIG. 3 is item 350 that may allow a user to view prescriptions, scheduled tests, or other information that relates to scheduled treatments of a patient. Item 350 includes a prescriptions field that may include a control element which, if activated by the clinician or user, displays the patient's current medications and dosages: a test scheduling field that may include a control element which, if activated by the user, displays the recommended cognitive functioning testing schedules: and a patient scheduling field that may include a control element which, if activated by the user, displays the recommended dates for testing the patient, as recommended by the test scheduling. [0046] FIG. 3 includes another area/item 360 within the GUI where elements may be selected to view other types of patient data. Item 360 includes a wearable data selection box, an alert selection box, a communication (COMM) selection box, a test score selection box, and a "Rim AI" selection box. The wearable data field selection box may include a control element which, if activated by the user, displays information collected and saved by a patient's wearable device. Data collected by this wearable device may include physiological data such as heart rate, blood pressure, perspiration rate data, or other physiological data.

[0047] The Alerts field of FIG. 3 may include a control element which, if activated by the user, displays safety alerts generated by the patient's wearable device. After a user that selects the Alerts field, the GUI may be provided with alert information received from dementia database 130, clinician device 110, caregiver deice 115, family device 120, or patient device 125 of FIG. 1. For example, an alert may identify that a clinician has identified that the caregiver should update a dosage to be provided to a patient based on an evaluation of test data stored at test medical test database 145B of medical test facility 145.

[0048] The communications field (COMM) of FIG. 3 may include a control element which, if activated by the user, displays information about the wearable device's communication link.

This field may be selected to see if a user device has received any general messages related to a patient. This field may also be used to view information that identifies whether a particular user device in a set of user devices was unavailable for a period of time, for example. The test scores field of FIG. 3 may include a control element which, if activated by the user, displays the patient's test scores. For example, after a caregiver may administrate an electro-cardio gram (EKG) or an electroencephalogram (EEG) to a patient after attaching a number of electrodes to the body of the patient. This data may be sent to a medical test facility where it is evaluated. This evaluation process may include sending an alert to a clinician informing that clinician to review the collected data. A score may then be associated with the EKG test or EEG test and that score may be viewed by selecting the test scores selection box of FIG. 3. The evaluation of this data may also result in the clinician sending alerts to a caregiver device, a family device, or to a patient device. This alert may result in the dosage provided to a patient being changed based on the clinician evaluation. [0049] The "Run AIA field of FIG. 3 may include a touch sensitive control element which, if activated by a user, displays an output of an AI algorithm. Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. In this application, the AI engine may access data on all available databases, such as the medical test facility, medical database, the doctors device, doctors database, the caregivers device, caregivers database, the family device, family device, the patients device, patients database, the patients wearable device, wearable database, the dementia usage network and associated databases (e.g., test database, dementia database).

[0050] Since all these databases have records associated with time and patients, it is possible to "Rim AI" algorithms to find a correlation between one field of one database (e.g., test data from a first test) and another field (e.g., wearable data). If a strong correlation found (e.g., by looking at the patients test score), the Run AI algorithms may return the correlations of the patients and what the correlation may be to wearable data. In this way, Run AI algorithms becomes a way to evaluate all the population against a particular piece of patient data.

[0051] FIG. 3 also includes note menu 370 that includes various types of notes 370A. FIG. 3 also includes a selection box of enter new note 370B that when selected may allow a user to enter a new note. Note types 370A included in note menu 370 of FIG. 3 include notes relating to memory testing, medication difficulties, driving capabilities, an exposure to danger, emotional support, patient routines or breaks, caregiver assessments, and notes that identify wills and estates of a patient.

[0052] The device illustrated in FIG. 3 may be a device operated by a user that may be a clinician, a caregiver, a family member, or a patient. Each of these different users may be assigned different privileges, where a clinician may have the right to prescribe medications, a caregiver may have the right to administer medications and perform patient tests, a family member may have a right to provide data, and a patient may be allowed to review stored information, for example. [0053] Table 1 illustrates a set of data that may be stored in clinician database, such as clinician database HOC of FIG. 1. The database includes data organized in a tabular format that includes rows and columns. The first row of table 1 contains numeric or alphanumeric codes that uniquely identify patients (e.g., patient number AB1568, FF9851, and TR9513). Table 1 also includes a row that contains the dates on which information was entered into the database. Another row in Table 1 contains keywords that may indicate a type of event noted in a specific record, for instance, a test being performed, or a result being recorded. Another row in Table 1 identifies an amount or dosage, if any, of a cannabinoid (e.g., CBD) administered to the patient. Table 1 also includes a row that contains an indicator that identifies whether CBD was administered to a particular patient. The dosage row and the "CBD used" row may be reviewed to identify a dosage of CBD that was administered to a patient on a day. For example, patent AB1568 received 0 milligrams (mg) of CBD (e.g., no CBD) on April 25, 2019, and patient FF9851 received 40 mg of CBD (Yes) on April 30, 2019.

[0054] The next row in Table 1 contains an indicator that identifies whether a placebo (an inert dose with no intended therapeutic value) was administered to the patient. Note that patient FF9851 was not administered a placebo on April 30, 2019, yet patient TR9513 was provided a placebo (Yes) on May 4, 2019.

[0055] Other rows included in Table 1 identify whether other (adjuvant) compounds were provided to a patient. Rows adjuvant 1 and adjuvant 2 may be used to identify other or additional compounds that were provided to a patient. Note that the adjuvant 1 and adjuvant 2 rows identity other or additional compounds administered to some patients of ibuprofen, pinene, and limonene. In certain instances, these other or additional compounds may be added to a treatment regimen with the goal of increasing efficacy of a CBD dose. For example, a terpene (pinene or limonene), a terpenoid, or an anti-inflammatory compound (ibuprofen) may be added to a treatment regimen.

[0056] Table 1 also includes a row that contains information that identifies a test type and another row that identifies whether a test a particular test passed or failed. Exemplary tests illustrated in Table 1 are a language skills test, an attention test, and an orientation test. These tests, if any, may be performed to assess the patient's mental functioning; for example, an orientation test may test a patient⁷ s ability to find their way back to their house from a nearby park. A memory test may be used to see if a patient can remember a list of items over a span of time. An attention test may identify whether a patient can stay focused on a task for a period of time. A language skills test may test whether a patient can identify words appropriate to describe a set of circumstances or images provided to the patient.

[0057] Another row of Table 1 may contain a link to more complete data regarding the patient's performance on an administered test. For instance, the patient⁷ s specific responses to the test questions or the clinician's full report following test administration may be identified as a by file name (e.g., ABF1568A.DAT). Table 1 may include a row that contains a calculated range that represents the level at which the patient has scored in one or more cognition or physiological tests. Test ranges included in Table 1 are: 0-25; 25-50; 75-20, and 75-100. These ranges may provide a single metric that acts as an overall indicator of the patient⁷ s response to a treatment. This may be accomplished by administering a test before the patient consumes a medication and this test may be administered again after the patient consumes the medication. [0058] Table 1 also include a row that identify alerts that have been generated by the patient's wearable device, for example, a notification of clinically relevant high or low blood sugar, high or low pulse rate, or high or low blood pressure. The wearable data row of Table 1 may identify a link or a data file that contains parametric data collected by a wearable device. Data files that begin with the text "pulseox" may identify a pulse rate and a blood oxygenation level of a patent and data files that begin with "glucose⁷⁷ may identify a specific blood sugar level (e.g., a level of 90, 100, or 120). These data files may include a full data set recorded by a wearable device.

[0059] A last row of Table 1 includes information regarding the treatment or test schedule of a patient. Note that this last row indicates that a caregiver should visit (follow up) with a particular patent in a week, in two weeks, or once a month. This scheduling data and the date information stored in table may be used to send alerts to a clinician or caregiver devices to remind the clinician or caregiver when they should visit a particular patient. Such an alert may be sent the day of or day before a scheduled visit.

Table 1: Clinician Database Data

[0060] FIG. 4 is a flowchart illustrating an exemplary caregiver device method executable within systems for managing care of memory-impaired/dementia patients. The process FIG. 4 begins with step 405 where patient data may be received from the dementia database. This data may have been retrieved from dementia database 130A of located on the dementia usage network 130 of FIG. 1. Step 405 may also include displaying the patient data received from the dementia database in a display at a caregiver device. Alternatively or additionally this data may have been received from the patient wearable device or from the clinician, family, or patient via their respective smart devices and databases.

[0061] Step 410 of FIG. 4 may receive data from the medical test database located in the medical test facility. Step 410 may also display the patient's medical data (received from the medical test database) to a caregiver. Next in step 415, a checklist may be displayed to the caregiver to assist in making contact with the patient. This checklist, for example, instruct the caregiver to get the person's attention, clearly state a message, ask simple questions, listen attentively, break down activities into a series of steps, and respond with affection and reassurance. This process may result in patient condition data being identified in step 420 of FIG. 4. This condition data may identify a patient behavior or a patient condition that the caregiver should address.

[0062] Instructions for handling troubling the behavior or the patient condition may then be provided in a display to the caregiver in step 425 of FIG. 4. These instructions, for example, might include possible accommodations for a patient behavior. As such, these instructions may include environmental adjustments and advice that assists the caregiver in understanding the root cause of the patient behavior. The instructions provided to the caregiver in step 425 may include instructions for handling wandering or unwanted movements or actions performed by a patient when a patient is known to have a propensity for losing a sense of direction. For example, these instructions might include the installation of safety equipment, use of visual barriers, and the use of identification bracelets and labels that may prevent a patient from leaving a house. Other instructions may identify procedures for handling incontinence of a patient. For example, these instructions might include establishing a routine, making adjustments to clothing, and adding signs to indicate the location of the bathroom. Other exemplary instructions may relate to handling agitation (for example, these instructions might include maintaining structure, using gentle music and touch, acknowledging anger, and distraction), to handling paranoia (for example, these instructions might include avoiding arguing, acknowledging fear, using gentle touch, and distraction), to handling sleeplessness (for example, these instructions might include increasing daytime activity, adjusting diet, and minimizing use of lights in the evening hours ), to handling eating disorders (for example, these instructions might include serving several small meals, prioritizing independence, and making mealtime special), to handling bathing issues (for example, these instructions might include prioritizing modesty, considering the patient's preferences, and avoiding risk of falls).

[0063] Information relating to the behavior or condition may be provided to a clinician or to a dementia usage network and a caregiver or AI algorithm. The dementia usage network may identify a dosage of a cannabinoid (e.g., CBD) to provide to the patent to address the behavior or condition. Next in step 430, an alert may be received the dementia database or from a clinician device. Such alerts may include updated information about the dosage of CBD and/or use or dosage of adjuvants that may be provided to a patient. Using data from the dementia database or clinician device, processes consistent with the present disclosure may identify and display the next patient data for the caregiver to review in step 435 of FIG. 4.

[0064] Next in step 440, a caregiver device may prompt the caregiver to acknowledge receipt of an alert and information included in that alert. Note this confirmation may be requested even if no action is required. Receiving and saving the caregiver's confirmation of receipt of all information and updates may occur at step 440. Next in step 450, a response to the prompt may be received by a user interface at the caregiver device and then, if the caregiver wishes to enter any new notes via the caregiver GUI, those notes may be received and saved in step 455. These notes may be sent to the caregiver database or be sent to other devices. Step 455 may send the data to the medical test database located in the medical test facility, may send the data to the clinician database located in the clinician smart device, or may send the data to the dementia database located in the dementia usage network. Next, determination step 460 may identify whether a family notification is included in the caregiver notes. When yes, that notification may be sent to a family device in step 465 of FIG. 4. Either after step 465 or when determination step 460 identifies that a family notification is not available, the program flow may move back to step 405 of FIG. 4 [0065] Table 2 includes much of the same information included in Table 1, here however the data of Table 2 is stored in a "caregiver database" such as caregiver databased 115C of FIG. 1. As in Table 1, Table 2 includes rows of patient ID, date, event, dosages of medications (e.g., CBD), adjuvant 1 and adjuvant 2 data, alerts, and wearable data. Each of these rows may contain data that is identical to similar rows of Table 1. As such, the data of Table 1 may identify that patient FF9851 on April 30, 2019 received 40 mg of CBD, was associated with an event of "Result," an alert of "Low Blood Sugar," and with wearable data of glucosel23.dat. The data of Table 2 also identifies the specific identity, if any, of any further (adjuvant) compounds administered to the patient with the goal of increasing efficacy of the CBD dose; for example, a terpene, a terpenoid, or an anti-inflammatory compound.

[0066] Table 2 includes a row that contains keywords that indicate a type of wearable event (data upload or alert) and a row that contains keywords that indicate the type of caregiver visit (routine visit, response to alert, and create new checklist).

[0067] Table 2 also includes a row that contains information regarding the length of time before the next recommended patient visit (2 week follow up and monthly maintenance) and various rows that include different types of notes regarding a patient. The different types of notes included in Table 2 are communication notes, checklist notes, incontinence notes, agitation notes, paranoia notes, sleeplessness notes, eating disorder notes, and bathing notes. Table 2 includes several different text notes and a data file "cll23.dat" that may include notes recorded by a caregiver. Each of these types of notes may identify or relate to how well a patient is responding to treatment or that may be used to assess conditions that a patient is suffering from.

Table 2: Caregiver Database Data [0068] FIG. 5 is a flowchart illustrating an exemplary family/patient device method executable within systems for managing care of memory-impaired/dementia patients. In step 505, patent data may be received and displayed in a display of a family or a patient device. The data received and displayed in step 505 may have been received from the patient wearable device or from a clinician, caregiver, family member, or patient via their respective smart devices. Next in step 510, data may be received from the medical test database located in the medical test facility 145 of FIG. 1. Step 510 may also include displaying the patient's medical data (received from the medical test database) to a family member or to a patient. Optional step 515 may include displaying a checklist that may include actions or questions provided to a family member. This check list may operate in a manner similar to the checklist 415 of FIG. 4 discussed above.

[0069] Determination step 520 may identify whether an alert has been received. When yes, the program flow may move to step 525 where the alert may be displayed on a display of a family device or a patient device. In certain instances, an alert may have been received from the patient's wearable or received from the database of the caregiver or clinician. Along with the alert, the module may display instructions about appropriate actions (for example, to contact the clinician or to arrange an appointment for the patient) at step 525. Either after step 525 or when step 520 identifies that an alert has not been received, program flow may move to determination step 530. Determination step 530 may then identify whether a family member to chooses to receive an early detection relating to whether the patient is suffering from early loss of memory. When yes, the program flow may move to step 535 where information may be displayed that may assist a family member in performing or arranging for an early detection test. Receiving an affirmative early detection response from family member may result in the family member receiving information with early detection of memory loss may result in the display of instructions for assessing medication difficulties, driving capabilities, and changes in personality.

[0070] Either after step 535 or when determination step 530 identifies that early detection is not desired, program flow may move to determination step 540 where a family member or the patient is prompted to choose to receive additional support. If family member chooses to receive additional support, information relating to receiving additional support may be displayed in step 545 of FIG. 5. This information may include ideas to an improve emotional support network, may identify useful routines, may include contact information of local support groups, or may identify sources of financial assistance.

[0071] Next, a family member or a patent may be polled to provide an acknowledgement receipt of all alerts and updated information sent to a user device. This may result in the family memory or patient providing a confirmation via a user interface in step 555 of FIG. 5. This confirmation may be sent to external computing devices for storage. Data may then be sent to various databases regarding actions that occurred in FIG. 5. As such, data may be sent to the medical test database located in the medical test facility, to the clinician database located in the clinician smart device, or to the dementia database located in the dementia usage network at step 565. Data may then be received from the dementia database regarding additional or next patient data, and this additional data may be displayed at a family or patient device.

[0072] Table 3 illustrates data that may be stored at a family database consistent with the present disclosure. Much of the data included in Table 3 is similar or identical to the data of Tables 1 and 2. This illustrates that the clinician database, the caregiver database, and the family database may be shared. Table 3 includes patient identifiers (ID), dates of treatment, event data, CBD dosage data, adjuvant 1 and adjuvant 2 data, alert data, wearable data, and patient testing data discussed in respect to Tables 1 and 2.

[0073] Table 3 include other rows of data that may be populated by family members and that may be shared with the other devices and databases of FIG. 1. These additional rows of data include rows of memory testing, medication difficulties, driving capabilities, exposure to danger, driving capabilities, emotional support needs, routines and breaks, health support, financial assessment, and wills and estates. Note that family members have provided information that identifies that the patient has forgotten to take medication twice, that the patient has felt high anxiety, has had trouble focusing, has experience an improvement with a new dosage, and identifies that they have requested information regarding a living will.

Table 3: Family Database Data [0074] A "patient database" such as patient database 125C of FIG. 1 may store any or all of the information discussed in respect to the data stored at the clinician database, the caregiver database, or the family database discussed above. As such, a patient database may store information that identifies patients by a patient number, that identifies medications, test, or test results as discussed in respect to Tables 1-3.

[0075] FIG. 6 is a flowchart illustrating an exemplary wearable device method executable within systems for managing care of memory-impaired/dementia patients. FIG. 6 begins with step 610, where the operation of a wearable device is initiated. Sensor data may be received, for instance, receiving blood pressure or pulse data that has been sensed by the sensor at step 610 may also be saved in step 610. Next, a communications link (for example a WIFI or Bluetooth link) may be accessed in step 630 when the received data is sent to a patient device. This data may be stored at the patient database 125C. Further, additional or next patient data may be received in step 640 of FIG. 6, and all of the data collected by the wearable device may be sent for storage at other databases in step 650. This may include relaying data through patient device 125 of FIG. 1. Data may be sent via the cloud, to the patient database located in the patient smart device, may be sent to the dementia database located in the dementia usage network 130, may be sent for storage to a clinician device 110 or may be sent to caregiver device 120 in step 650. After step 650, program flow may move back to step 610 of FIG. 6.

[0076] Table 4 illustrates data that may be stored in a wearable database consistent with the present disclosure. Table 4 includes rows of data that identifies patient identifiers (ID), dates when patient data was collected, patient pulse rates, patient blood glucose levels, blood pressure data, and other sensor data (e.g., sensor M data).

Table 4: Wearable Device Data

[0077] FIG. 7 is a flowchart illustrating an exemplary method for identifying effective dosages of medications. FIG. 7 begins with step 705, where an examination is initiated. Data from the medical test database is received at step 710. Next, data may be retrieved from a medical test database in step 710. Step 710 may include extracting the dementia test and preparing it for administration to determine type and level of dementia. Next in step 715, a user or an employee of the medical testing laboratory may be prompted to perform a dementia test. The results of the dementia test are received from the user, for example by user input at step 720. Using the received test results, a level and type of dementia are calculated at step 725 of FIG. 7. Such a dementia level may correspond to a severity of symptoms. This may result in a medical test database being queried as part of a process that identifies an appropriate dosage of CBD and/or other medicines to provide to a patient. This identification may be based on a correlation between the new dementia test results and historical data stored at the test database.

[0078] As such, the test data and information in the medical test database may be compared and correlated to a calculated level and type of dementia and a recommended CBD dosage and adjuvant composition may be identified in step 730 of FIG. 7. The recommended dosage and composition and treatment schedule may be displayed to the user at step 735. After the medication has been administered to the patient, the user may be prompted to repeat the dementia test in step 740. This may include following the recommended schedule and performing the dementia test again in the future. For example, after beginning a new CBD dosage. A patient may return 2 weeks later to be retested at step 740 when new test results are collected in step 750 of FIG. 7. Using the new received test results, the level and type of dementia may be re-calculated at step 750, and these test results may be stored in step 755. Using the received test results, performing calculations, may include, for example averaging or weighted averaging. This may produce a measure of the overall test performance, for example, a range of numbers that indicates the patient's performance over several tests of cognition at step 750. Step 755 may include saving changes in test results, the calculated high-level test results, and the administered dosage/composition when identifying whether the patient is improving. This process may be repeated several times until a most effective treatment regimen has been identified. After step 755, the program flow may move to determination step 760 that identified whether a family notification should be made available to a family device. When yes, the program flow may move to step 765 where the family notification is sent to the family device. When determination step 760 identifies that a family notification should not be made available or after step 765, program flow may move back to step 705 where the process may repeat.

[0079] A "medical test database" such as medical test database 145B of FIG. 1 may store any or all of the information discussed in respect to the data stored at the clinician database, the caregiver database, the family database discussed above, or the patient database. As such, a medical test database may store information that identifies patients by a patient number, that identifies medications, test, or test results as discussed in respect to Tables 1-3.

[0080] FIG. 8 is a flowchart illustrating an exemplary method for applying artificial intelligence to analyze patient data. Steps 805, 810, 815, and 820 of FIG. 8 may receive data from various devices owned by different entities. Each of these different entities may have different levels of authority or permissions. For example, a clinician may be authorized to provide medical advice or prescribe dosages of certain medications, a caregiver may be authorized to administer tests or administer dosages to patients after those test or dosages have been authorized by the clinician, a family member may be authorized to provide observations or assist a patient in consuming a prescribed medication, and patient may be authorized to provide information and conditionally be allowed to consume a prescribed medication when not under direct supervision. Alternatively or additionally, an artificial intelligent machine process may make recommendations that may in turn be accepted or rejected by the clinician.

[0081] The data received in steps 805, 810, 815, and 829 may be used by to identify a dosage composition that should next be administered to a patient in step 830 of FIG. 8. In certain instances, all of the steps illustrated in FIG. 8 may be performed by an AI machine process. Next in step 835, additional medical test data may be retrieved and stored, this may include retrieving new test data after a patient has consumed a new dosage of a medication over time. Determination step 840 may then identify whether other software modules should be executed. When yes, the program flow may move to step 845 where instructions of those other software modules are executed by one or more processors of one or more computing devices. Step 845 may include passing data to other computers such that those other computers may perform operations associated with other software modules. This may include sending queries to clinician devices, caregiver devices, family devices, devices that collect and evaluate data for research, devices of a CBD or drug manufacturer, or to a computer of a pharmacy. As such, step 845 may initiate operations at any of the devices illustrated in FIG. 1. FIG. 8 may thus perform steps in the cloud management system 150 or dementia usage network 130. The other software modules discussed in respect to step 845 above may be part of the functioning of a base dementia network module or "base module" of FIG. 1.

[0082] The steps of FIG. 8 may include polling the wearable devices and when new data is found, receiving the data, and saving it to the dementia database located in the dementia usage network. Polling the medical test facility and, when new data is found, receiving the data, and saving it to the dementia database located in the dementia usage network.

[0083] After step 845 or when determination step 840 identifies that other software modules need not operation, program flow may move to step 850 where new CBD and/or adjuvant recommendation are identified. This process may result in the sending of updated recommendations on appropriate CBD dosage and adjuvant identity and dosage to the clinician smart devices. Updated recommendations may be received from a treatment module, and may be made based upon changes in patient symptoms and behavior. This information may be drawn from test scores, medical data, or data from the smart devices of the patient, family, or caregivers - in combination with relevant data from the dementia database. For example, if the module receives updated patient test results that reveal a drop in language skills, it may send a new recommended adjuvant that has been shown historically (in the dementia database data) to improve language skills at step 850.

[0084] FIG. 9 is a flowchart illustrating an exemplary caregiving device method for making recommendations based on patient data. FIG. 9 begins with step 905 where a patient record is created or is opened. Next, data from one or more devices may be received in step 910. Step 910 may include polling the caregiver smart devices and, when new data is found, receiving the data, and saving it to the dementia database located in the dementia usage network. Step 910 may also include similarly polling and storing data from family smart devices, patient smart devices, wearable devices, and medical test facility of FIG. 1. Next in step 915, the patient data may be manipulated to generate a test score and those test scores may be compared to other test scores. Determination step 920 may then identify whether there has been a change in test scores over time after dosages of a medication have been changed over time. When these test score have not changed, program flow may move to step 940 where a recommendation or other data is sent to a dementia database. When these test scores have changed over time, determination step 925 may identify whether a similar change has been observed before, when not program flow may flow to the previously discussed step 940. When determination step 925 identifies that a previous change has been observed previously, program flow may move to step 930 where a CBD dosage and/or an adjuvant therapy consistent with the change data is identified. Next, optional step 935 may compare current therapy data to change therapy data, this comparison may result in the identified CBD and/or adjuvant therapy being updated yet again. For example, if it appears that the patient has gained a tolerance to the medication, a dosage may be updated. [0085] Next in step 940, data or a recommendation may be sent to the dementia database. Determination step 950 may the identify whether an updated recommendation or authorization has been received from the dementia database. When determination step 950 receives an update or an authorization, program flow may move to step 955 where an alert is sent to a clinician device. In certain instances, a response may be required to be received from the clinician device to act as a final approval to change a dosage of a medication. After step 955 or when determination step 950 identifies that an update has not been received, program flow may move back to step 905 where another patient record is created or opened. The steps of FIG. 9 may alternatively be performed by a clinician software module, by a treatment module, or combination thereof.

[0086] FIG. 10 is a flowchart illustrating an exemplary method for researching cannabinoid- based medications. The steps illustrated in FIG. 10 may be consistent with steps performed by the research module, the CBD manufacturing (MFG) module, the drug company module, or the pharmacy module of FIG. 1.

[0087] FIG. 10 includes a first step where one or more networks are polled for requests. This may include polling a 3rd party research network, polling computers of CBD or drug manufacturers, or polling pharmacy computer for any data requests relating to research, medication manufacturing, or pharmacy distribution networks. Determination step 1020 may the identify whether any requests have been received. When no, the program flow may move back to step 1010. When a request has been received, program flow may move to step 1030 where a temporary database patient related data may be created. All identifying details such as patient, clinician, family member, and caregiver names may have been removed from this temporary data. Next queries may be accepted to the temporary database from a requesting computer and data may be sent to the requesting computer in step 1020, and data may be sent to the requested computer in step 1030 of FIG. 10. Next in step 1040, a fee (for the use of the temporary database) may be calculated and an invoice may be sent to the requesting computer. After step 1040, program flow may move back to step 1010 where the process repeats.

[0088] Table 5 includes much of the same data discussed in respect to Table 1. The data of Table 5 may be stored in a dementia database, such as the dementia database 130A of FIG. 1. Like Table 1, Table 4 includes patient identifying (ID) numbers, date information, event data, milligram dosages, an indication whether CBD was dispensed to a patient, an identification whether a placebo was used, adjuvant 1 and adjuvant 2 data, test type information, test result information, test result link or file identifiers, and test level range data. [0089] Table 5 also include three sets of correlated data: correlated data 1, correlated data 2, and correlate data M. Note that the data of table 5 may correlate clinician notes with brain scan images or may correlate other data with acquired video.

[0090]

Table 5: Dementia Database Data [0091] Data stored at any of the respective databased discussed herein may include further tests results, historical notes, videos, photos or other potentially useful information.

[0092] FIG. 11 illustrates an exemplary computing system that may be used to implement an embodiment of the present invention. The computing system 1100 of FIG. 11 includes one or more processors 1110 and main memory 1120. Main memory 1120 stores, in part, instructions and data for execution by processor 1110. Main memory 1120 can store the executable code when in operation. The system 1100 of FIG. 11 further includes a mass storage device 1130, portable storage medium drive(s) 1140, output devices 1150, user input devices 1160, a graphics display 1170, peripheral devices 1180, and network interface 1195.

[0093] The components shown in FIG. 11 are depicted as being connected via a single bus 1190. However, the components may be connected through one or more data transport means. For example, processor unit 1110 and main memory 1120 may be connected via a local microprocessor bus, and the mass storage device 1130, peripheral device(s) 1180, portable storage device 1140, and display system 1170 may be connected via one or more input/output (I/O) buses.

[0094] Mass storage device 1130, which may be implemented with a magnetic disk drive or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 1110. Mass storage device 1130 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 1120.

[0095] Portable storage device 1140 operates in conjunction with a portable non-volatile storage medium, such as a FLASH memory, compact disk or Digital video disc, to input and output data and code to and from the computer system 1100 of FIG. 11. The system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system 1100 via the portable storage device 1140.

[0096] Input devices 1160 provide a portion of a user interface. Input devices 1160 may include an alpha-numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. Additionally, the system 1100 as shown in FIG. 11 includes output devices 1150. Examples of suitable output devices include speakers, printers, network interfaces, and monitors.

[0097] Display system 1170 may include a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display, an electronic ink display, a projector-based display, a holographic display, or another suitable display device. Display system 1170 receives textual and graphical information, and processes the information for output to the display device. The display system 1170 may include multiple-touch touchscreen input capabilities, such as capacitive touch detection, resistive touch detection, surface acoustic wave touch detection, or infrared touch detection. Such touchscreen input capabilities may or may not allow for variable pressure or force detection.

[0098] Peripherals 1180 may include any type of computer support device to add additional functionality to the computer system. For example, peripheral device(s) 1180 may include a modem or a router.

[0099] Network interface 1195 may include any form of computer interface of a computer, whether that be a wired network or a wireless interface. As such, network interface 1195 may be an Ethernet network interface, a BlueToothTM wireless interface, an 802.11 interface, or a cellular phone interface.

[0100] The components contained in the computer system 1100 of FIG. 11 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 1100 of FIG. 11 can be a personal computer, a hand held computing device, a telephone ("smart" or otherwise), a mobile computing device, a workstation, a server (on a server rack or otherwise), a minicomputer, a mainframe computer, a tablet computing device, a wearable device (such as a watch, a ring, a pair of glasses, or another type of jewelry/clothing/accessory ), a video game console (portable or otherwise), an e-book reader, a media player device (portable or otherwise), a vehicle-based computer, some combination thereof, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. The computer system 1100 may in some cases be a virtual computer system executed by another computer system. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, Android, iOS, and other suitable operating systems.

[0101] The present invention may be implemented in an application that may be operable using a variety of devices. Non-transitory computer-readable storage media refer to any medium or media that participate in providing instructions to a central processing unit (CPU) for execution. Such media can take many forms, including, but not limited to, non-volatile and volatile media such as optical or magnetic disks and dynamic memory, respectively. Common forms of non-transitory computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, RAM, PROM, EPROM, a FLASH EPROM, and any other memory chip or cartridge.

[0102] While various flow diagrams provided and described above may show a particular order of operations performed by certain embodiments of the invention, it should be understood that such order is exemplary (e.g., alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, etc.).

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for analyzing cannabinoid dosage efficacy on dementia patients, the method comprising: receiving a first set test data associated with a test performed on a patient; detecting that the first set of test data correspond to one or more predetermined symptoms of dementia; classifying the patient as a dementia patient based on the first set of test data corresponding to the predetermined symptoms of dementia; identifying a first cannabinoid dosage for the patient, wherein the first cannabinoid dosage is identified based on the first set of test data; receiving a second set of test data associated with the test performed on the patient after administration of the first cannabinoid dosage, wherein the second test was performed based at least in part on the dementia classification of the patient; and identifying an improvement level to one or more of the symptoms of dementia exhibited by the patient based on the second set of test data.

2. The method of claim 1, further comprising storing the first set of test data and the second set of test data in a database in memory in association with the dementia classification and the first cannabinoid dosage.

3. The method of claim 1, wherein identifying an improvement level comprises: assigning a first score to the patient based on the first set of test data; assigning a second score to the patient based on the second set of test data; and comparing the first score to the second score.

4. The method of claim 2, further comprising: identifying a second cannabinoid dosage to administer to the patient based on the first set of test data and the second set of test data; receiving a third set of test data associated with the test performed on the patient after administration of the second cannabinoid dosage; and identifying a preferred cannabinoid dosage based on a comparison of the first set of test data, the second set of test data, and the third set of test data.

5. The method of claim 1, further comprising recording a first time period between administration of the first cannabinoid dosage and receipt of the second set of test data, and storing the first time period in memory.

6. The method of claim 4, further comprising recording a second time period between administration of the second cannabinoid dosage and receipt of the third set of test data, and storing the second time period in memory.

7. The method of claim 4, further comprising: detecting that a first set of test data of a second patient corresponds to one or more predetermined symptoms of dementia; comparing the stored data associated with the first patient to the first set of data of the second patient; and identifying a recommendation that at least one of the first cannabinoid dosage or the second cannabinoid dosage be administered to the second patient based on the comparison.

8. The method of claim 7, further comprising identifying an improvement level to one or more of the symptoms of dementia exhibited by the second patient after administration of the recommended dosage.

9. A system for analyzing cannabinoid dosage efficacy on dementia patients, the system comprising: a communication interface that receives a first set test data associated with a test performed on a patient; a processor that executes instructions stored in memory, wherein the processor executes the instructions to: detect that the first set of test data correspond to one or more predetermined symptoms of dementia; classify the patient as a dementia patient based on the first set of test data corresponding to the predetermined symptoms of dementia; identify a first cannabinoid dosage for the patient, wherein the first cannabinoid dosage is identified based on the first set of test data, wherein the communication interface further receives a second set of test data associated with the test performed on the patient after administration of the first cannabinoid dosage, wherein the second test was performed based at least in part on the dementia classification of the patient; and identify an improvement level to one or more of the symptoms of dementia exhibited by the patient based on the second set of test data.

10. The system of claim 9, further comprising memory that stores the first set of test data and the second set of test data in a database in association with the dementia classification and the first cannabinoid dosage.

11. The system of claim 9, wherein the processor identifies the improvement level by:: assigning a first score to the patient based on the first set of test data; assigning a second score to the patient based on the second set of test data; and comparing the first score to the second score

12. The system of claim 10, wherein the processor executes further instructions to: identify a second cannabinoid dosage to administer to the patient based on the first set of test data and the second set of test data, wherein the communication interface further receives a third set of test data associated with the test performed on the patient after administration of the second cannabinoid dosage; and identify a preferred cannabinoid dosage based on a comparison of the first set of test data, the second set of test data, and the third set of test data.

13. The system of claim 9, wherein the processor executes further instructions to record a first time period between administration of the first cannabinoid dosage and receipt of the second set of test data, and further comprising memory that stores the first time period.

14. The system of claim 12, wherein the processor executes further instructions to record a second time period between administration of the second cannabinoid dosage and receipt of the third set of test data, and further comprising memory that stores the second time period.

15. The system of claim 12, wherein the processor executes further instructions to: detect that a first set of test data of a second patient corresponds to one or more predetermined symptoms of dementia; compare the stored data associated with the first patient to the first set of data of the second patient; and identify a recommendation that at least one of the first cannabinoid dosage or the second cannabinoid dosage be administered to the second patient based on the comparison.

16. The system of claim 17, wherein the processor executes further instructions to identify an improvement level to one or more of the symptoms of dementia exhibited by the second patient after administration of the recommended dosage.

17. A non-transitory computer-readable storage medium having embodied thereon a program executable by a processor for implementing a method for treating dementia, the method comprising: receiving a first set test data associated with a test performed on a patient; detecting that the first set of test data correspond to one or more predetermined symptoms of dementia; classifying the patient as a dementia patient based on the first set of test data corresponding to the predetermined symptoms of dementia; identifying a first cannabinoid dosage for the patient, wherein the first cannabinoid dosage is identified based on the first set of test data; receiving a second set of test data associated with the test performed on the patient after administration of the first cannabinoid dosage, wherein the second test was performed based at least in part on the dementia classification of the patient; and identifying an improvement level to one or more of the symptoms of dementia exhibited by the patient based on the second set of test data.