System and Method for Clinical Curation of Crowdsourcing Data

Abstract

To provide a system and a method for accurately grasping a user's mental or physical state.SOLUTION: A method includes: obtaining, from a patient device associated with a patient, input data including free text data generated by the patient; determining whether the input data meets predetermined relevancy criteria; and comparing the input data with clinical data in a clinically curated database to generate comparison data. The method includes executing at least one of curation actions of: when the input data meets the predetermined relevancy criteria and the comparison data indicates that the input data is sufficiently different from the clinical data, adding the input data to the clinically curated database; when the input data meets the predetermined relevancy criteria and the comparison data indicates that the input data is sufficiently similar to the clinical data, merging the input data with the clinical data; and when determining that the input data does not meet the predetermined relevancy criteria, taking no action.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] [Cross - Reference to Related Applications] This U.S. patent application claims priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application No. 62 / 840,656, filed Apr. 30, 2019. The disclosure of this prior application is considered a part of the disclosure of this application and is hereby incorporated by reference in its entirety.

[0002] The present disclosure generally relates to the treatment of serious medical conditions, and more specifically to systems and methods for implementing and managing a clinically curated database for the treatment of serious medical conditions.

Background Art

[0003] The information provided in this section is generally for presenting the context of the present disclosure. The research of the currently named inventors, to the extent described in this section, is not admitted as prior art to the present disclosure, either expressly or implicitly, any more than aspects of this specification that may not qualify as prior art at the time of filing.

[0004] Drug therapy has played an important role in the treatment of various medical diseases and disorders. Conventional drug therapy involves the administration of drugs. Examples of conventional drugs can include small - molecule drugs usually derived from chemical synthesis and biologic agents including recombinant proteins, vaccines, blood products, monoclonal antibodies, and cell therapies that are therapeutically used in gene therapy.

[0005] Drug therapy has proven to be an effective mechanism for treating specific diseases and disorders, but it is not without drawbacks. For example, drugs are known to be associated with certain often - unwanted side effects. Also, drugs are often expensive, sometimes prohibitively so.

[0006] Accordingly, as a complement or alternative to conventional drug therapy techniques, digital solutions for treating various medical diseases and disorders have emerged. Such digital solutions (e.g., digital therapeutics, mobile health applications, etc.) can request information from their users (e.g., patients in the case of prescription digital therapy or "PDT"). Such information can include, by way of example and not limitation, information regarding the user's mental state (e.g., feelings the user is experiencing or has experienced), and / or physical state (e.g., physical symptoms related to mental or physical health status). SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0007] Conventional digital solutions have often presented users with a fixed set of selectable responses to a given question. For example, a conventional digital solution can present a user with a set of selectable options such as "happy," "sad," "scared," "tired," "painful," "sleepy," etc. in relation to a question such as "How are you feeling?" However, these preselected, predefined responses often do not accurately capture the user's mental or physical state. MEANS FOR SOLVING THE PROBLEMS

[0008] Accordingly, there is a perceived need for a system and method for implementing and managing a clinically curated database for the treatment of serious medical conditions.

[0009] Reference is now made to the accompanying drawings, which are not necessarily to scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0010]

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DETAILED DESCRIPTION OF THE INVENTION

[0011] Like reference symbols in the various drawings indicate like elements.

[0012] One aspect of the present disclosure provides a system that includes data processing hardware and memory hardware that communicates with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including obtaining input data including free text data generated by a patient from a patient device associated with the patient. The operations include analyzing the input data to determine whether the input data meets a predetermined relevance criterion. The operations include comparing the input data with clinical data in a clinically curated database to generate comparison data and, based on the comparison data, (i) adding the input data to the clinically curated database if the comparison data indicates that the input data meets the predetermined relevance criterion and is sufficiently different from the clinical data, (ii) fusing the input data with the clinical data if the comparison data indicates that the input data meets the predetermined relevance criterion and is sufficiently similar to the clinical data, and (iii) performing at least one of the curation operations of not performing any operation if the input data is determined not to meet the predetermined relevance criterion.

[0013] Implementations of the present disclosure can include one or more of any of the following features. In some implementations, the operations further include analyzing the input data to determine a likelihood of an adverse event and assigning a risk assessment value corresponding to the likelihood indicated by the input data that an adverse event has occurred or is about to occur to the input data. If the risk assessment value associated with the input data exceeds a predetermined threshold, the operations can further include performing an action mechanism to address the adverse event.

[0014] The operation mechanism can include transmitting an alert indicating that a harmful event has occurred or is about to occur to a healthcare provider device associated with a healthcare provider monitoring a patient. The operation mechanism can include transmitting an alert to a call center device associated with a call center, the alert indicating that a harmful event has occurred or is about to occur and instructing the call center to contact the patient via a patient device. The operation mechanism can include transmitting an alert to a patient device that provides information to the patient for dealing with the harmful event.

[0015] Analyzing the input data and comparing the input data with clinical data can be performed by implementing artificial intelligence. The artificial intelligence can be monitored by a medical expert. The artificial intelligence can include machine learning without a teacher.

[0016] The input data can be an input that responds to a question, and a predetermined relevance criterion can be satisfied when the input data is responding to the question.

[0017] Another aspect of the present disclosure provides a method that includes obtaining, via one or more processors, input data including free text data generated by a patient from a patient device associated with the patient. The method includes analyzing the input data via one or more processors to determine whether the input data meets a predetermined relevance criterion. The method includes comparing, via one or more processors, the input data with clinical data in a clinically curated database to generate comparison data. The method includes performing at least one of the following curation operations based on the comparison data: (i) adding the input data to the clinically curated database if the comparison data indicates that the input data meets the predetermined relevance criterion and is sufficiently different from the clinical data; (ii) fusing the input data with the clinical data if the comparison data indicates that the input data meets the predetermined relevance criterion and is sufficiently similar to the clinical data; and (iii) performing no action if the input data is determined not to meet the predetermined relevance criterion. This aspect can include one or more of any of the following features.

[0018] In some implementations, the method further includes analyzing the input data to determine a likelihood of a harmful event and assigning to the input data a risk assessment value corresponding to the likelihood that the input data indicates that a harmful event has occurred or is about to occur. The method can further include performing an action mechanism for addressing the harmful event if the risk assessment value associated with the input data exceeds a predetermined threshold.

[0019] The action mechanism can include transmitting to a healthcare provider device associated with a healthcare provider monitoring the patient an alert indicating that a harmful event has occurred or is about to occur. The action mechanism can include transmitting to a call center device associated with a call center an alert indicating that a harmful event has occurred or is about to occur and instructing the call center to contact the patient via the patient device. The action mechanism can include transmitting to the patient device an alert providing information for addressing the harmful event to the patient.

[0020] Analyzing input data and comparing the input data with clinical data can be performed by implementing artificial intelligence. The artificial intelligence can be monitored by medical experts. The artificial intelligence can include machine learning without a teacher.

[0021] The input data can be an input that responds to a question, and a predetermined relevance criterion can be satisfied when the input data is responding to the question.

[0022] The accompanying drawings and the following description show details of one or more implementations of the present disclosure. Other aspects, features, and advantages will become apparent from the description, the drawings, and the claims.

[0023] With reference to the accompanying drawings, some implementations of the technology of the present disclosure will be described more fully. However, the technology of the present disclosure can be embodied in many different forms and should not be construed as limited to the implementations shown herein.

[0024] Implementations of the technology of the present disclosure provide a system and method for implementing and managing a clinically curated database for the treatment of serious medical conditions.

[0025] For example, in the treatment of certain medical conditions or medical indications such as opioid abuse, multiple sclerosis, etc., the input from a patient can be useful when adapting the treatment to the specific needs of the patient by providing information to the medical expert controlling the treatment. Such input can take the form of responses to multiple-choice questions, responses to open-ended questions, unstructured free text, etc. Also, patient input can prompt the patient to show similar input from other patients from a clinically curated database, thereby making the patient feel that they are not the only one experiencing such specific thoughts and feelings.

[0026] In some cases, patients with certain medical conditions may experience mental health symptoms that are considered natural responses to the course of certain unpredictable medical conditions, such as severe chronic diseases. Mental health symptoms can include depression, anxiety disorders, mood swings, etc. Patients with certain medical conditions may be more likely to exhibit mental health symptoms due to psychological risk factors such as inappropriate coping or insufficient social support, as well as in vivo effects such as changes in brain structure.

[0027] There is no observed correlation between the severity of symptoms and the likelihood that a patient will experience mental health symptoms. Any patient with a medical condition may experience mental health symptoms at any time, and various factors can affect a patient's mental health symptoms. The period of mental health symptoms may occur after the patient's initial diagnosis. Patients may also experience mental health symptoms due to physical symptoms related to a particular medical condition. For example, patients with fatigue may have depleted emotional energy needed to combat mental health symptoms. Additionally, a patient's new symptoms and high uncertainty about the future may cause the patient to experience mental health symptoms. Physiological causes such as damage to the central nervous system, and chemical changes such as the expression of pro-inflammatory protein molecules involved in cell communication, may also cause patients to experience mental health symptoms. Drug side effects may exacerbate mental health symptoms. For example, steroids can cause a sense of intoxication in the short term, and there is a risk of triggering mental health symptoms when the intoxication wears off.

[0028] Mental health symptoms have a significant impact on the mood of patients with certain medical conditions, thereby adversely affecting the quality of life of the patients. Patients may prioritize physical health over emotional health and leave mental health symptoms untreated, which may lead to a decline in the quality of life and deterioration of cognitive function. For example, patients experiencing mental health symptoms may seek to withdraw from daily activities, resulting in a reduction in social stimulation. Patients may also experience an increased risk of suicide.

[0029] Current treatment options for the mental health symptoms of patients with certain medical conditions generally include pharmacotherapy and face-to-face therapy with a clinician. However, these treatment options can be complemented with more effective patient input.

[0030] An implementation example of the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0031] Referring to FIG. 1, in some implementations, a treatment prescription system 100 provides a patient 101 with access to a prescription digital therapeutic 120 prescribed for the patient 101 and monitors events related to the interaction between the patient 101 and the prescription digital therapeutic 120. In this specification, the digital therapeutic 120 is described as a "prescription" digital therapeutic, but according to some implementations, it is understood that the digital therapeutic 120 does not require a prescription from a clinician. Rather, in such implementations, the digital therapeutic 120 functions differently according to the description of the prescription digital therapeutic 120 provided herein, even though patients without a prescription can use the digital therapeutic 120. According to implementations where the digital therapeutic 120 is not prescribed, the person using or administered the digital therapeutic can be referred to as a "user". The "user" can include the patient 101 or any other person using or administered the digital therapeutic 120, regardless of whether the digital therapeutic 120 is prescribed for that person.

[0032] As used herein, digital therapy can also be referred to as a digital-therapeutic configured to provide evidence-based psychosocial interventions for treating patients with a disease or disorder, as well as symptoms and / or behaviors associated with a particular disease or disorder. As an example, when patient 101 is diagnosed with a chronic disease, the prescribed digital therapy 120 can be specifically adapted to address one or more depressive symptoms associated with the chronic disease that patient 101 may experience. A certified healthcare provider (HCP) 109 (e.g., a physician, nurse, etc.) monitoring patient 101 can prescribe to patient 101 a prescribed digital therapy 120 designed to help patient 101 identify the feelings they are experiencing and modify dysfunctional emotions, behaviors, and thoughts in order to treat patient 101's depressive symptoms. HCP 109 can include a physician, nurse, clinician, or other qualified medical professional.

[0033] In some examples, system 100 includes network 106, patient device 102, HCP system 140, and a treatment service 160 specialized for medical indications. For example, treatment service 160 can be related to specific indications such as opioid abuse, multiple sclerosis, depression, etc. Network 106 provides access to cloud computing resources 150 (e.g., a distributed system) that execute treatment service 160 to enable the execution of services on remote devices. Thus, network 106 enables the interaction between patient 101 and HCP 109 and treatment service 160. For example, treatment service 160 can provide patient 101 with access to prescribed digital therapy 120 and receive event data 122 related to the interaction between patient 101 and prescribed digital therapy 120, input by patient 101. Further, treatment service 160 can store event data 122 in storage resources 156.

[0034] Network 106 can include any type of network that enables the transmission and reception of communication signals, such as a wireless communication network, a cellular phone network, a time division multiple access (TDMA) network, a code division multiple access (CDMA) network, a global system for mobile communications (GSM), a third generation (3G) network, a fourth generation (4G) network, a satellite communication network, and other communication networks. Network 106 can include one or more of a wide area network (WAN), a local area network (LAN), and a personal area network (PAN). In some examples, Network 106 includes a combination of data networks, communication networks, and combinations of data networks and communication networks. Patient device 102, HCP system 140, and treatment service 160 communicate with each other by transmitting and receiving signals (wired or wireless) via Network 106. In some examples, Network 106 provides access to cloud computing resources that can be elastic / on-demand computing and / or storage resources 156 available via Network 106. The term "cloud" service generally refers to a service that is not executed locally on a user's device but rather is provided from one or more remote devices that are accessible via one or more Networks 106.

[0035] The patient device 102 can include, but is not limited to, a portable electronic device (e.g., a smartphone, a mobile phone, a personal digital assistant, a personal computer, a wireless tablet device, or a wearable device), a desktop computer, or any other electronic device capable of transmitting and receiving information via the network 106. The patient device 102 includes data processing hardware 112 (a computer device that executes instructions), memory hardware 114, and a display 116 that communicates with the data processing hardware 112. In some examples, the patient device 102 includes a keyboard, a mouse, a microphone, and / or a camera that enables the input of data by the patient 101. The patient device 102 can also include one or more speakers that output audio data to the patient 101, in addition to or instead of the display 116. For example, audible alerts can be output by the speakers to notify the patient 101 of any time-dependent events related to the prescription digital therapy 120. In some implementations, the patient device 102 executes a patient application 103 (or accesses a web-based patient application) to establish a connection with the therapy service 160 and access the prescription digital therapy 120. For example, the patient 101 can access the patient application 103 over the duration of the prescription digital therapy 120 (e.g., three months) prescribed to the patient 101. Here, the patient device 102 launches a patient application 103 that enables the patient 101 to access the content related to the prescription digital therapy 120 from the therapy service 160 that is specifically adapted to treat one or more symptoms related to a particular adaptation that the patient 101 may be experiencing / to address such symptoms, by first providing an access code 104 when the prescription digital therapy 120 is prescribed by the HCP 109.When the patient application 103 is running on the data processing hardware 112 of the patient device 102, it enables, among other things, the input of event data 122 related to specific sensations that the patient 101 is experiencing himself, requests information from the patient 101, and presents journal entries for the patient 101 to confirm. It is configured to display various graphical user interfaces (GUIs) (e.g., the patient input GUI 231 as shown in FIG. 3) on the display 116 of the patient device 102.

[0036] Storage resource 156 can provide a data storage 158 that stores event data 122 received from patient 101 and patient records 105 corresponding to the prescribed digital treatment 120 for patient 101. In some implementations, data storage 158 communicates with a clinically curated database 220 that communicates with a cloud computing system 150. For example, data storage 158 can share patient records 105, prescribed digital treatment 120, and / or any other suitable information with a clinically curated database 220, and the clinically curated database 220 can share clinically curated entries and / or any other suitable information with data storage 158. In other implementations, data storage 158 stores a clinically curated database 220. Patient records 105 can be encrypted such that any information identifying patient 101 is anonymized while stored in data storage 158, but can be decrypted later (assuming the requester is authorized / authenticated to access patient records 105) when patient 101 or a monitored HCP 109 requests patient records 105. All data transmitted between patient device 102 and cloud computing system 150 via network 106 can be encrypted and transmitted over a secure communication channel. For example, patient application 103 can encrypt event data 122 before transmitting it to treatment service 160 via the HTTPS protocol and decrypt patient records 105 received from treatment service 160. If a network connection is not available, patient application 103 can store event data 122 in an encrypted queue within memory hardware 114 until a network connection becomes available.

[0037] The HCP system 140 can be placed in a clinic, examination room, or facility managed by the HCP 109, and includes data processing hardware 142, memory hardware 144, and a display 146. The memory hardware 144 and the display 146 communicate with the data processing hardware 142. For example, the data processing hardware 142 can be present on a desktop computer or a portable electronic device so that the HCP 109 can input and retrieve data with the treatment service 160. In some examples, when the HCP 109 prescribes the prescription digital treatment 120 to the patient 101, it can first onboard some or all of the patient data 107. The HCP system 140 includes a keyboard 148, a mouse, a microphone, speakers, and / or a camera. In some implementations, the HCP system 140 (i.e., via the data processing hardware 142) runs the HCP application 110 (or accesses a web-based patient application) to establish a connection with the treatment service 160 and input and retrieve data with the treatment service 160. For example, the HCP system 140 can access the anonymous patient record 105 securely stored in the storage resource 156 by the treatment service 160 by providing an authentication token 108 that proves that the HCP 109 is monitoring the patient 101 and is permitted to access the corresponding patient record 105. The authentication token 108 can identify a specific patient 101 related to the patient record 105 that the HCP system 140 is permitted to obtain from the treatment service 160. The patient record 105 can include timestamped event data 122 indicating the interaction between the patient and the prescription digital treatment 120 through the patient application 103 running on the patient device 102.

[0038] The cloud computing resource 150 can be a distributed system (e.g., a remote environment) having scalable / elastic resources 152. The resources 152 include computing resources 154 (e.g., data processing hardware) and / or storage resources 156 (e.g., memory hardware). The cloud computing resource 150 facilitates communication with the patient device 102 and the HCP system 140, stores data on the storage resource 156 within the data storage 158, and executes a treatment service 160 that stores data in the clinically curated database 220. In some examples, the treatment service 160, the data storage 158, and the clinically curated database 220 are present on a stand-alone computer device. The treatment service 160 can provide to the patient 101 a patient application 103 (e.g., a mobile application, a website application, or a downloadable program including an instruction set) that is executable on the data processing hardware 112 and is accessible through the network 106 via the patient device 102 when the patient 101 provides a valid access code 104. Similarly, the treatment service 160 can provide to the HCP 109 an HCP application 110 (e.g., a mobile application, a website application, or a downloadable program including an instruction set) that is executable on the data processing hardware 142 and is accessible through the network 106 via the HCP system 140.

[0039] FIG. 2 is a diagram showing a system 200 that implements and manages a clinically curated database 220 according to an exemplary implementation of the present disclosure. According to one example, aspects of system 200 can be executed by computing resources 154 of a cloud computing system 150. In another example, aspects of system 200 can be executed by an electronic device such as data processing hardware 112 of patient device 102. In yet another example, aspects of system 200 can be executed by some combination of computing resources 154 and data processing hardware 112. In some implementations, externally available data 210 is obtained (e.g., fetched or received) by a clinically curated database 220. Externally available data 210 can be obtained from various sources such as, for example, the Federal Drug Administration (FDA), the World Health Organization (WHO), the International Classification of Diseases, Tenth Revision (IDC-10). As described above, the clinically curated database 220 can communicate with cloud computing resources 150 or be stored in data storage 158 of cloud computing resources 150. In other implementations, the clinically curated database 220 can be stored in memory hardware 114 of patient device 102, memory hardware 144 of HCP system 140, or any other suitable storage location.

[0040] System 200 includes an input module 230 having a predetermined entry module 230a and a free text entry module 230b. The input module 230 can be executed by the patient device 102, that is, by the data processing hardware 112 in conjunction with the display 116 and / or other peripheral devices such as the microphone, speaker, mouse, keyboard, camera of the patient device 102. The input module 230 communicates with the clinically curated database 220 to obtain (e.g., fetch or receive) data from the clinically curated database 220.

[0041] Referring to FIGS. 2 and 3, in some examples, the display 116 of the patient device 102 includes a touch screen that displays the patient input GUI 231. The data processing hardware 112 can execute GUI software adapted to facilitate human interaction with the patient input GUI 231. As will be described in more detail below, the patient 101 can provide a user selection indicating a choice to interact with the patient input GUI 231. As used herein, a user selection can be directed to a UI control including any display element or component of the patient input GUI 231 displayed on the display 116. Thus, a user selection indicating the selection of a UI control can enable the patient 101 to provide an input, observe data, and / or otherwise interact with the patient input GUI 231. Examples of UI controls include buttons, drop-down menus, menu items, tap-and-hold functions, and the like.

[0042] The patient input GUI 231 displays a plurality of predetermined entries 238 including a free text data entry element 232, a data entry header element 236, and individual exemplary entries 238a to 238d. In some examples, the data entry header element 236 and the free text data entry element 232 can each include an entry prompt 237. The entry prompt 237 can be a question or statement for eliciting a response from the patient 101. For example, the entry prompt 237 can indicate "My automatic thought was..." to prompt the patient 101 to respond with their automatic thought. Each entry prompt 237 can be associated with a plurality of predetermined entries 238 that are selectable responses to the entry prompt 237. For example, a predetermined entry module 230a can determine which predetermined entry 238 was selected by the patient 101 at any time. The entry prompt 237 and its associated predetermined entries 238 are retrieved from a clinically curated database 220 and displayed on the patient input GUI 231. For example, as shown in FIG. 3, one of the predetermined entries 238c can indicate "Worrying that I might always have a panic attack" in response to the entry prompt 237 "My automatic thought was...". As will become apparent, the predetermined entries 238 can be at least partially based on externally available data 210, free text responses from other patients added to the clinically curated database 220 upon reconsideration, or a combination thereof.

[0043] In some implementations, the patient input GUI 231 can display a string of free text data 234 that reflects the patient's response to the entry prompt 237 typed or spoken by the patient. As shown in FIG. 3, the string of free text data 234 can be entered in the underlined portion after the entry prompt 237. The free text data entry element 232 enables the patient 101 to enter the string of free text data 234 by typing via a keyboard (not shown) within the free text data entry element 232 or by speaking into the microphone of the patient device 102. The free text data 234 is displayed within the free text data entry element 232 and, in some embodiments, is appended to the entry prompt 237.

[0044] The patient 101 can respond to the entry prompt 237 in a plurality of ways. According to one embodiment, the patient 101 can respond by selecting (e.g., through a touch gesture or other suitable input mechanism) one of a predetermined set of entries 238 displayed on the display 116 of the patient device 102 as determined by a predetermined entry module 230a. According to another embodiment, the patient 101 can respond by entering free text data 234 into the free text data entry element 232 as determined by the free text entry module 230b, e.g., by typing on a keyboard (e.g., via a keyboard GUI that can be floated over at least a portion of the patient input GUI 231) or by speaking into the microphone of the patient device 102. According to some examples, the patient can end or confirm their response to the entry prompt 237 by selecting a send button 233 as determined by the input module 230.

[0045] Referring to FIGS. 2 and 3, the input module 230 communicates with a GUI generation module 240 configured to generate a GUI such as a patient input GUI 231 (FIG. 3) or a patient trigger GUI 300 (FIG. 4) that is displayed on the display 116 of the patient device 102. When a user selection instruction indicates a selection of one of the predetermined entries 238, the GUI generation module 240 displays the selected one of the predetermined entries 238 on the patient input GUI 231. For example, although not shown, the selected one of the predetermined entries 238 can be emphasized, separated, or identified in any suitable manner to indicate the selection of that predetermined entry 238. When a user selection instruction indicates a selection of the entry prompt 237 and subsequent entry of free text data 234, the GUI generation module 240 displays the free text data 234 and / or the entry prompt 237 on the patient input GUI 231. For example, although not shown, the free text data 234 can be emphasized, separated, or identified in any suitable manner to indicate the entry of the free text data 234.

[0046] The free text review module 250 is configured to reexamine and analyze the free text data 234 to determine what further actions should be taken according to the free text data 234. For example, the free text review module 250 determines whether the free text data 234 meets a predetermined relevance criterion (i.e., whether the free text data 234 responds to the entry prompt 237), and whether the free text data 234 indicates the likelihood that a harmful event has occurred or is about to occur. According to some examples, the free text review module 250 is configured to implement artificial intelligence and / or machine learning (with or without a teacher) to determine what further actions should be taken with respect to the free text data 234. Based on its determination, the free text review module 250 is configured to selectively transfer the free text data 234 to the clinical data curation module 260 for further processing.

[0047] The clinical data curation module 260 includes an entry addition module 262, an entry fusion module 264, and a no-action module 266. The entry addition module 262 is configured to add free text data 234 to a clinically curated database 220 (e.g., as a new entry in the database 220) based on a determination that the free text data 234 meets a predetermined criterion. Such predetermined criteria can include, but are not limited to, (i) the relevance of the free text data 234 (e.g., according to a calculated relevance score), (ii) a determination that the free text data 234 contains sensitive or inappropriate content (e.g., based on a determination that the free text data 234 includes certain known words such as slang or obscenities), (iii) a determination that the clinically curated database 220 already contains a similar entry (e.g., based on generating comparison data by comparing the free text data 234 with a predetermined entry 238 in the clinically curated database 220), (iv) and others. For example, if the free text data 234 reads "I feel like a burden to my family" and there is no similar relevant predetermined entry 238 in the clinically curated database 220, the comparison data can indicate that this free text data 234 should be added to the clinically curated database 220, and the entry addition module 262 can add this free text data 234 to the clinically curated database 220.

[0048] According to one example, to generate comparison data, free text data 234 can be vectorized and compared with corresponding vector data related to a predetermined entry 238 in a clinically curated database 220. According to this example, the entry addition module 262 can determine that an entry of a given free text data 234 is sufficiently different from a predetermined entry 238 in a clinically curated database 220 when the vector of each entry deviates from a predetermined threshold. Such determination can be made by artificial intelligence and / or machine learning (with or without a teacher). If the free text data 234 is determined to be sufficiently different from one of the predetermined entries 238 in the clinically curated database 220, the entry addition module 262 can add this free text data 234 as a new entry to the clinically curated database 220.

[0049] The entry fusion module 264 is configured to determine whether the free text data 234 is closely related to any given entry 238 within the clinically curated database 220. According to one example, to generate comparison data, the free text data 234 can be vectorized and compared with corresponding vector data related to a given entry 238 within the clinically curated database 220. According to this example, the entry fusion module 264 can determine that an entry of a given free text data 234 is related to a given entry (e.g., entry 238a) within the clinically curated database 220 if the vectors of each entry are within a given threshold. Such determination can be made by artificial intelligence and / or machine learning (with or without a teacher). If the free text data 234 is determined to be sufficiently related to one of the given entries 238 within the clinically curated database 220, the entry fusion module 264 can fuse the free text data 234 with the related given entry (e.g., entry 238a). According to some examples, the similarity between the free text data 234 and the given entry 238 can be based on the meaning of the free text data 234 and the meaning of the given entry 238. For example, if the free text data 234 reads "I’m scared" and one of the given entries 238 reads "I’m afraid", the entry fusion module 264 can fuse the free text data 234 of "I’m scared" with the given entry corresponding to "I’m afraid". As another example, the comparison data can also indicate that the free text data 234 contains a typo or a misspelling (e.g., "I’m afriad") but is sufficiently similar to one of the given entries 238 (e.g., the given entry corresponding to "I’m afraid"), as a result of which the entry fusion module 264 will fuse this free text data 234 with the given entry corresponding to "I’m afraid".

[0050] If the free text data 234 does not meet the criteria for being added to the clinically curated database 220 or for being merged into one of the predetermined entries 238 within the clinically curated database 220 (i.e., the free text data 234 does not meet the predetermined relevance criteria), no action is taken in the no-action module 266, and the free text data 234 remains in the input state. For example, if the free text data 234 contains meaningless text (e.g., "abcd1234", "!@kfycn", "lplahsnxc", etc.) or irrelevant text (i.e., text that does not respond to the entry prompt 237 such as "The sky is blue"), the no-action module 266 is configured to prevent the free text data 234 from being added to the clinically curated database 220.

[0051] Among several advantages, in particular, by adding new data entries to the clinically curated database 220, merging data entries into the clinically curated database 220, and preventing entries from being added to the clinically curated database 220, the most relevant results are input into the clinically curated database 220, and as a result, the outcome of patient 101 can be improved.

[0052] According to some examples, the free text review module 250 can determine, based on the content of the free text data 234, that the free text data 234 implies the possibility of an adverse event for patient 101 or other people when analyzing the free text data 234. Such determination can be made by artificial intelligence and / or machine learning (with or without a teacher). For example, the free text review module 250 can detect an adverse event state based on the presence of specific keywords representing harm to patient 101 or other people. The adverse event state can be detected by the free text review module 250 when the free text data 234 reflects harmful statements such as "want to commit suicide", "want to hurt myself", "want to hurt others", etc.

[0053] When the free text review module 250 detects a harmful event state, it can transfer the free text data 234 to the regulatory review module 270 for further processing. The regulatory review module 270 can include a harmful event review module 272 and a reporting module 274. The harmful event review module 272 is configured to determine the likelihood of a harmful event based on the free text data 234. This determination can be based on, for example, the presence of certain keywords (e.g., "kill", "harm", "hurt", etc.) within the free text data 234, the presence of certain drug street names (e.g., "heroin", "cocaine", etc.), drug brand names (e.g., "Suboxone®"), and / or drug manufacturer names (e.g., "Big Pharma") within the free text data 234, and / or a comparison of the free text data 234 with responses indicating previously re-investigated harmful events. In some implementations, the determination of the likelihood of a harmful event can be based on established clinical scales for evaluating self-harm behavior. For example, the harmful event review module 272 can analyze the free text data 234 and generate a risk assessment value based on the free text data 234. In some implementations, the generation of the risk assessment value can include a comparison of the free text data 234 with entries in a previously existing database such as an established clinical database. Such analysis and / or comparison can be performed by artificial intelligence and / or machine learning (with or without a teacher). When the harmful event review module 272 determines that the free text data 234 indicates a harmful event, it can trigger an action by the reporting module 274.

[0054] The reporting module 274 is configured to perform operational mechanisms such as, for example, sending an alert to the HCP system 140, sending an alert to the treatment service 160, or sending an alert and the phone number of the patient 101 to a suicide hotline, or other suitable call center, crisis hotline, etc., to instruct the suicide hotline to contact the patient 101. In addition to or instead of this, the reporting procedure can also follow the processes specified by organizations such as the FDA, WHO, etc.

[0055] As described above, the patient input GUI 231 is related to the automatic thinking of the patient 101. However, it should be understood that the patient input GUI 231 shows one exemplary GUI that can be displayed on the display 116, and other GUIs can also be displayed on the display 116 in the same way.

[0056] For example, referring to FIG. 4, the input module 230 can execute the patient trigger GUI 300 and display it on the display 116 of the patient device 102. The patient trigger GUI 300 can display a plurality of predetermined entries 306 including a free text data entry element 302, a data entry header element 304, and individual exemplary entries 306a - 306e via the input module 230. In some examples, the data entry header element 304 can include an entry prompt 305. The entry prompt 305 can be a question or statement for eliciting a response from the patient 101 similar to the entry prompt 237. For example, the entry prompt 305 can show "What is the trigger for this response?" to prompt the patient 101 to respond to a specific trigger. In some implementations, the response can be related to a recurrence (e.g., recurrence of drug or alcohol use), and the trigger can be related to the event, action, emotion, etc. that caused the recurrence.

[0057] Each entry prompt 305 can be associated with a plurality of predetermined entries 306 that are selectable responses to the entry prompt 305. For example, a predetermined entry module 230a can determine which predetermined entry 306 was selected by the patient 101 at what time. The entry prompt 305 and its associated predetermined entries 306 are retrieved from a clinically curated database 220 and displayed on the patient trigger GUI 300. For example, as shown in FIG. 4, the first predetermined entry 306a can indicate "stress", the second predetermined entry 306b can indicate "work", the third predetermined entry 306c can indicate "desire", the fourth predetermined entry 306d can indicate "anger", and the fifth predetermined entry 306e can indicate "loneliness". In addition to these triggers, any other suitable triggers such as fatigue, tiredness, social pressure, pain, boredom, etc. are also contemplated. Similar to the predetermined entries 238 described above, the predetermined entries 306 can also be at least partially based on externally available data 210, free text responses from other patients added to the database 220 that are reexamined and clinically curated, or combinations thereof.

[0058] In some implementations, the patient trigger GUI 300 can display a string of free text data 308 that reflects a response to an entry prompt 305 typed or spoken by the patient via the free text entry module 230b. As shown in FIG. 4, the string of free text data 308 can be entered in the underlined portion after "Other". The free text data entry element 302 enables the patient 101 to enter a string of free text data 308 by typing via a keyboard (not shown) within the free text data entry element 302 or by speaking into the microphone of the patient device 102. The free text data 308 is displayed within the free text data entry element 302 and, in some embodiments, is appended to the entry prompt 305. As described above with respect to the entry prompt 237, the patient 101 can respond to the entry prompt 305 in a plurality of ways such as touch gestures, voice, etc. According to some examples, the patient can end or confirm their response to the entry prompt 305 by selecting a send button 310 determined by the input module 230.

[0059] Similar to the above description of the system 200 and the patient input GUI 231, the system 200 can similarly execute the GUI generation module 240, the free text review module 250, the clinical data curation module 260, and the regulatory review module 270 on information obtained from the interaction between the patient 101 and the patient trigger GUI 300.

[0060] Figures 5A - 5D roughly show an exemplary graphic representation of a process 500 for curating a clinically curated database 220 using the system 200. The process 500 includes an entry prompt 502 that, in some examples, can be "My automatic thought was...". The process 500 includes a plurality of database groups 504 that, in one example, include a first group 512 and a second group 514. The process 500 includes entries 506 associated with each group 504. For example, the first group 512 includes entries 512a - 512g, and the second group 514 includes entries 514a - 514c. The process 500 is configured to receive free - text entries 508, such as a first free - text entry 508a corresponding to, for example, "I am angry" (e.g., through the free - text entry module 230b). The free - text review module 250 is configured to determine an action 510 in response to the free - text entry 508. For example, as shown in Figure 5A, in response to the first free - text entry 508a, the free - text review module 250 determines a first action 510a corresponding to adding the first free - text entry 508a as a new entry to the clinically curated database 220 through, for example, the entry addition module 262.

[0061] Referring to FIG. 5B, a third database group 516 including a first entry 516a corresponding to the already added "I am mad" is added to the database group 504. The free text entry module 230b is configured to receive a second free text entry 508b corresponding to "I am mad". The free text review module 250 is configured to determine that the second free text entry 508b is sufficiently similar to the first entry 516a corresponding to "I am mad". Thus, the free text review module 250 determines a second operation 510b corresponding to, for example, fusing the second free text entry 508b into the third group 516 through the entry fusion module 264.

[0062] Referring to FIG. 5C, the third group 516 includes a second entry 516b corresponding to "I am mad" that has already been fused into the third group 516. The free text entry module 230b is configured to receive a third free text entry 508c corresponding to "ka8jd7". The free text review module 250 is configured to determine that the third free text entry 508c does not respond to the entry prompt 502. Thus, the free text review module 250 determines a third operation 510c corresponding to performing no operation on the third free text entry 508c, for example, through the no action module 266.

[0063] Referring to FIG. 5D, the free text entry module 230b is configured to receive a fourth free text entry 508d corresponding to "I'm going to hurt myself". The free text review module 250 is configured to assign a high risk assessment value indicating a high likelihood that a harmful event has occurred or is about to occur to the fourth free text entry 508d. Accordingly, the free text review module 250 adds the fourth free text entry 508d as a new entry to the clinically curated database 220, for example, through the entry addition module 262, and determines a fourth action 510d corresponding to performing an action mechanism to address the harmful event indicated by the fourth free text entry 508d, for example, through the regulatory review module 270.

[0064] In some implementations, as described above, artificial intelligence and / or machine learning can be utilized in various features, functions, components, processes, modules (e.g., free text review module 250, clinical data curation module 260, and / or regulatory review module 270, etc.) of system 200. For example, free text data 234 can be compared with a dictionary or database (e.g., externally available data 210 and / or clinically curated database 220) containing specific keywords (e.g., trademarks, company names, drug names, etc.) to trigger escalation of free text data 234. Such escalation can be utilized by implementing fuzzy matching that compares free text data 234 with entries in externally available data 210 and / or clinically curated database 220. In some implementations, the fuzzy matching process can include a relatively high sensitivity setting that flags or identifies as likely to match one or more entries in externally available data 210 and / or clinically curated database 220 that would escalate such free text data 234. This escalation of this specific free text data 234 can be reexamined by a human (e.g., clinician, healthcare provider, third - party service, etc.), artificial intelligence, machine learning, etc. to verify the match between free text data 234 and the entries in the database. Once verified, the fuzzy match between free text data 234 and the entries in externally available data 210 and / or clinically curated database 220 can be added / fused to the clinically curated database 220 so that the same free text data 234 can be automatically categorized as a match for escalation during subsequent entry of that specific free text data 234.

[0065] In some implementations, at least a basic dataset of classifications including "add entry", "merge entry", "no action", and "adverse event", completed and verified by humans (e.g., clinicians, healthcare providers, third-party services, etc.), can be pre-entered into the clinically curated database 220. Using this dataset, a natural language processing (NPL) text classification model can be provided to pre-sort the free text data 234 into one of these classifications. In some implementations, the free text data 234 pre-sorted into the "adverse event" classification can be prioritized for manual review and potential escalation, and greater emphasis can be placed on the free text data 234 that can indicate that an adverse event has occurred or is about to occur. For free text data 234 not pre-sorted into the "adverse event" classification, fuzzy matching or other defined rules can be executed to determine whether an entry should be added, whether an entry should be merged, or whether no action should be taken. In some implementations, these entries can be re-reviewed later by humans (e.g., clinicians, healthcare providers, third-party services, etc.) for verification. Subsequently, the verified data can be added / merged into the entries in the clinically curated database 220 to train the model and improve the accuracy over time.

[0066] FIG. 6 shows a flowchart of method 600 described herein. Method 600 includes, at 602, obtaining input data including free text data generated by a patient from a patient device associated with the patient. Method 600 includes, at 604, analyzing the input data. Method 600 includes, at 606, generating comparison data by comparing the input data with clinical data in a clinically curated database. Method 600 performs at least one of steps 608-614 based on the comparison data. If the comparison data indicates that the input data meets a predetermined relevance criterion and is sufficiently different from the clinical data, method 600 includes, at 608, adding this input data to a clinically curated database. If the comparison data indicates that the input data meets a predetermined relevance criterion and is sufficiently similar to the clinical data, method 600 includes, at 610, fusing the input data with the clinical data. If it is determined that the input data does not meet a predetermined relevance criterion, method 600 includes, at 612, not performing any action.

[0067] In some implementations, method 600 includes assigning a risk assessment value corresponding to the likelihood that the input data indicates that a harmful event has occurred or is about to occur to the input data. If the risk assessment value associated with the input data exceeds a predetermined threshold, method 600 includes, at 614, executing an action mechanism. For example, the action mechanism can include sending an alert to a healthcare provider device associated with a healthcare provider monitoring the patient, sending an alert to a call center device associated with a call center, and / or sending an alert to the patient device.

[0068] FIG. 7 is a schematic diagram of an example electronic device 700 (e.g., a computer device) that can be used to implement the systems and methods described in this document. The electronic device 700 is intended to represent various forms of digital computers such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The components, connections, and relationships shown here, as well as their functions, are intended only as examples and do not limit the implementation of the inventions described and / or claimed in this document.

[0069] The electronic device 700 includes a processor 710, a memory 720, a storage device 730, a high-speed interface / controller 740 connected to the memory 720 and a high-speed expansion port 750, and a low-speed interface / controller 760 connected to a low-speed bus 770 and the storage device 730. Each of the components 710, 720, 730, 740, 750, and 760 is interconnected using various buses and can be attached, as necessary, on a common motherboard or otherwise. The processor 710 processes instructions executed within the electronic device 700, including instructions stored in the memory 720 or the storage device 730, to display graphical information for a graphical user interface (GUI) on an external input / output device such as a display 780 coupled to the high-speed interface 740. In other implementations, multiple processors and / or multiple buses can be used, along with multiple memories and memory types, as necessary. Also, multiple electronic devices 700 can be connected so that each device provides a portion of the required operations (e.g., as a server bank, a group of blade servers, or a multiprocessor system).

[0070] Memory 720 stores information non - temporarily within the electronic device 700. The memory 720 can be a computer - readable medium, a (single or plural) volatile memory unit, or a (single or plural) non - volatile memory unit. The non - temporary memory 720 can be a physical device used to store temporarily or permanently a program (e.g., a series of instructions) or data (e.g., program state information) used by the electronic device 700. Examples of non - volatile memory include, but are not limited to, flash memory and read - only memory (ROM) / programmable read - only memory (PROM) / erasable programmable read - only memory (EPROM) / electrically erasable programmable read - only memory (EEPROM) (e.g., used for firmware such as a typical boot program). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase - change memory (PCM), and disks or tapes.

[0071] The storage device 730 can provide mass storage for the electronic device 700. In some implementations, the storage device 730 is a computer - readable medium. In various different implementations, the storage device 730 can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid - state memory device, or an array of devices including devices within a storage area network or other configurations. In further implementations, a computer program product is tangibly embodied within an information carrier. The computer program product includes instructions that, when executed, perform one or more of the methods as described above. The information carrier is a computer - readable or machine - readable medium such as the memory 720, the storage device 730, or the memory on the processor 710.

[0072] The high-speed controller 740 manages the bandwidth-intensive operations of the electronic device 700, and the low-speed controller 760 manages the low-bandwidth-intensive operations. Such an assignment of responsibilities is merely illustrative. In some implementations, the high-speed controller 740 is coupled to the memory 720, the display 780, and a high-speed expansion port 750 that can accept various expansion cards (not shown) (e.g., via a graphics processor or accelerator).

[0073] The electronic device 700 can be implemented in a plurality of different forms as shown in FIG. 7. For example, the electronic device 700 can be implemented as a standard server 700a, or multiple times as a group of such servers 700a, as a laptop computer 700b, as part of a rack server system 700c, as a smartphone 700d, or as a tablet computer 700e.

[0074] The various implementations of the systems and techniques described herein can be realized in digital electronic circuits and / or optical circuits, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include an implementation in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor coupled to send and receive data and instructions between a storage system, at least one input device, and at least one output device, which can be either dedicated or general purpose.

[0075] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor and can be implemented in high-level procedural languages and / or object-oriented programming languages, and / or assembly / machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, non-transitory computer-readable medium, apparatus and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives the machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0076] A software application (i.e., a software resource) can mean computer software that causes a computer device to perform a task. In some examples, a software application can also be referred to as an "application", an "app", or a "program". Examples of applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and game applications.

[0077] As used herein, the term "module" can mean hardware, software, firmware, or any combination thereof. The processes and logic flows described herein can be performed by one or more programmable processors, also referred to as data processing hardware that executes one or more computer programs, by operating on input data to generate output. The processes and logic flows can be performed by dedicated logic circuitry, such as, for example, an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). Examples of processors suitable for executing computer programs include both general purpose and special purpose microprocessors, as well as processors of any type of one or more digital computers. In general, a processor receives instructions and data from a read only memory or a random access memory, or both. Essential elements of a computer are a processor that performs operations in accordance with instructions, and one or more storage devices that store instructions and data. In general, a computer also includes, or is operatively coupled to perform, or both, one or more mass storage devices that store data, such as, for example, magnetic disks, magneto-optical disks, or optical disks. However, a computer need not have such devices. Examples of computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including semiconductor memory devices such as EPROM, EEPROM, and flash memory devices, magnetic disks such as internal hard disks or removable disks, magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, dedicated logic circuitry.

[0078] One or more aspects of the present disclosure can be implemented on a computer having a display device for displaying information to a user, such as a CRT (cathode ray tube), an LCD (liquid crystal display) monitor, or a touch screen, and optionally a keyboard to enable the user to provide input to the computer, and a pointing device such as a mouse or a trackball. Other types of devices can also be used to enable interaction with the user. For example, the feedback provided to the user can be any form of sensory feedback such as visual feedback, auditory feedback, or tactile feedback, and the input from the user can be received in any form including acoustic input, speech input, or tactile input. Also, the computer can interact with the user by sending and receiving documents to and from the devices used by the user, for example, by sending a web page to a web browser in response to a request received from the web browser on the user's client device.

[0079] Multiple implementations have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the present disclosure. Accordingly, other implementations are included in the following claims.

Description of Reference Numerals

[0080] 100 Treatment Prescription System 101 Patient 102 Patient Device 103 Patient Application 104 Access Code 105 Patient Record 106 Network 107 Patient Data 108 Authentication Token 109 Physician / Nurse / Healthcare Provider (HCP) 110 HCP Application 112 Data Processing Hardware 114 Memory Hardware 116 Display 120 Prescribed Digital Therapy 122 Event Data 140 HCP System 142 Data Processing Hardware 144 Memory Hardware 146 Display 148 Keyboard 150 Cloud Environment 152 Resource 154 Computing Resource 156 Storage Resource 158 Data Storage 160 Treatment Services Specialized for Adaptation 220 Clinically Curated Database

Claims

1. A data processing system having one or more than two processors coupled to a memory for communicating with a plurality of user devices, obtaining input data including free text data generated by a user of one of the plurality of user devices from one of the plurality of user devices; using a machine learning model to compare the free text data of the input data with at least one of a plurality of entries of clinical data in a database; based on the comparison, determining that the free text data meets a criterion for inclusion in the database, the criterion defining at least one of a relevance threshold or a similarity threshold; in response to determining that the free text data meets the criterion, including the input data in the database A data processing system configured to perform the above. A system characterized by including the above.

2. The data processing system is further configured to receive input data including free text data generated by the user via at least one of (i) the user inputting in a graphical user interface displayed on the user device or (ii) the user speaking towards a microphone of the user device. The system according to claim 1.

3. The data processing system is further configured to obtain the input data including the free text data generated by the user in response to a prompt presented via the user device. The system according to claim 1.

4. The data processing system is further configured to obtain input data including free text generated by the user selecting from a plurality of predetermined entries presented via the user device. The plurality of predetermined entries are generated using the clinical data of the database. The system according to claim 1.

5. The data processing system: (i) when the free text data meets the relevance threshold and does not meet the similarity threshold, adds the input data to the database; (ii) when the free text data meets the relevance threshold and meets the similarity threshold, fuses the input data with at least one entry of the clinical data in the database, configured to include the input data, The system according to claim 1.

6. The data processing system is further configured to prevent addition or fusion of the input data and a plurality of entries of clinical data in the database when the free text data does not meet the relevance threshold and does not meet the similarity threshold. The system according to claim 1.

7. The user device is further configured to present a plurality of entries selected as the free text data in response to detection of a trigger related to the user. The system according to claim 1.

8. The user of the user device receives treatment at least partially simultaneously with inputting the free text data for the input data. The system according to claim 1.

9. obtaining, by a data processing system, input data including free text data generated by a user of one of a plurality of user devices from the one user device; comparing, by the data processing system, the free text data of the input data with at least one of a plurality of entries of clinical data in a database using a machine learning (ML) model; The data processing system determines, based on the comparison, that the free text data meets a criterion for inclusion in the database, the criterion defining at least one of a relevance threshold or a similarity threshold. In response to determining that the free text data meets the criterion, the input data is included in the database. A method characterized by including the above.

10. The obtaining of the input data further includes receiving input data including free text data generated through at least one of (i) the user inputting in a graphical user interface displayed on the user device, or (ii) the user speaking towards a microphone of the user device. The method according to claim 9.

11. The obtaining of the input data further includes obtaining the input data including the free text data generated by the user in response to a prompt presented via the user device. The method according to claim 9.

12. The obtaining of the input data includes obtaining input data including free text generated by the user selecting from a plurality of predetermined entries presented via the user device, and the plurality of predetermined entries are generated using clinical data in the database. The method according to claim 9.

13. The including of the input data further includes: (i) when the free text data meets the relevance threshold but does not meet the similarity threshold, adding the input data to the database; (ii) when the free text data meets the relevance threshold and meets the similarity threshold, including the input data by fusing the input data with at least one entry of the clinical data in the database. The method according to claim 9.

14. When the free text data does not meet the relevance threshold and does not meet the similarity threshold, further comprising preventing the data processing system from adding or fusing the input data with a plurality of entries of clinical data in the database. The method according to claim 9.

15. The user device is further configured to present a plurality of entries selected as the free text data in response to detection of a trigger related to the user. The method according to claim 9.

16. The user of the user device receives treatment at least partially simultaneously with inputting the free text data for the input data. The method according to claim 9.

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