Patent Application
20250148465
Population health management entails aggregating patient data across multiple health information technology resources, analyzing the data with reference to a single patient, and generating actionable items through which care providers can improve both clinical and financial outcomes. In some instances, coordinating health services to perform the actionable items among multiple entities in a healthcare ecosystem can be a daunting, inefficient, and/or cumbersome task.
A system and method for using a blockchain to manage medical transactions between entities in a healthcare ecosystem are disclosed herein. The medical transactions may be performed using the hyperledger to provide a population health management service. The steps of any of the following methods may be implemented as computer instructions stored on tangible, non-transitory media that are executable by one or more processors. Further, the methods may be implemented by a computing device and/or a system including one or more processors.
In one embodiment, a method for maintaining a hyperledger for healthcare transactions at one or more nodes includes receiving, a from a client device, a request to perform a transaction using the hyperledger wherein the transaction pertains to registering a medical personnel entity associated with the client device as a node of the one or more nodes, and the request includes an authorizing credential pertaining to the medical personnel entity. The method also includes determining, based on one or more rules, whether to allow the transaction to be performed in view of the request. Responsive to determining to tallow the transaction to be performed, the method also includes registering the node for the medical personnel entity by creating the node for the medical personnel entity, updating the hyperledger by adding a block to the hyperledger, where the block stores the transaction including the authorizing the credential pertaining to the medical personnel entity, and storing the hyperledger at the node for the medical personnel entity.
In one embodiment, a method for performing a medical transaction using a hyperledger includes receiving, from a computing device associated with a medical personnel entity, a request to perform the medical transaction between the medical personnel entity and a patient entity, where the medical personnel entity is associated with a medical personnel node and the patient entity is associated with a patient node, and a respective copy of the hyperledger is maintained at both the medical personnel node and the patient node. The method also includes identifying a transaction in the hyperledger that stores an authorizing credential of the medical personnel entity, determining whether to allow the medical transaction to be performed based on at least the authorizing credential of the medical personnel entity, and responsive to determining to allow the medical transaction to be performed, updating the respective copy of the hyperledger with the medical transaction at the medical personnel node and the patient node.
In one embodiment, a method for performing a medical transaction using a hyperledger includes identifying a medical facility computing device that is within a threshold distance of a geolocation of a patient computing device, where the medical facility computing device is associated with a medical facility entity and the patient computing device is associated with a patient entity. The method also includes transmitting, from the patient computing device, a request to perform a medical transaction to the medical facility computing device, where the request includes an authenticating credential associated with the patient entity, and a determination is made whether to allow performance of the medical transaction based on one or more rules that specify allowing the medical transaction to be added to a hyperledger when at least the authenticating credential in the hyperledger is verified. The method also includes receiving a notification from the medical facility computing device that indicates the medical transaction has been performed.
In one embodiment, a method may include recommending items in conversational streams by receiving conversation stream segments, defining a user action outcome objective based on the conversation stream segments and a user profile that may be stored on a hyperledger, selecting an action likely to advance the user action outcome objective, and presenting a conversation stream segment to motivate an action likely to advance the user action outcome objective.
In one embodiment, a computer-implemented method for providing therapeutic medical action recommendations in response to a medical information natural language conversation stream is disclosed. The method includes receiving segments of a medical information natural language conversation stream at an artificial intelligence-based health information conversation agent from a medical information conversation user interface. Based on the medical information content of a user medical information profile (e.g., stored in a hyperledger) associated with the medical information natural language conversation stream, the method further defines a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user medical information profile. The method further involves identifying a set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective. The method further involves selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective. The method further involves presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective. The method further involves presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a correlation between the medical intervention likely to advance the clinical management outcome objective and achievement of the clinical management outcome objective.
In one embodiment, a computer program product in a non-transitory computer-readable medium for providing therapeutic medical action recommendations in response to a medical information natural language conversation stream is disclosed. The product contains instructions that cause a computer to receive segments of a medical information natural language conversation stream at an artificial intelligence-based health information conversation agent from a medical information conversation user interface. The product contains further instructions that cause the computer to define a clinical management outcome objective relevant to health management criteria and related health management data attributes of the profile in response to the medical information content of a user medical information profile (e.g., stored in a hyperledger) associated with the medical information natural language conversation stream. The product contains further instructions that cause the computer to select a medical intervention likely to advance the clinical management outcome objective. The product contains further instructions that cause the computer to present to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the action likely to advance the clinical management outcome objective.
In one embodiment, a system for providing therapeutic medical action recommendations in response to a medical information natural language conversation stream is disclosed, the system includes a knowledge cloud configured for receiving segments of a medical information natural language conversation stream at an artificial intelligence-based health information from a medical information conversation user interface of a cognitive agent. The system further includes a critical thinking engine. The critical thinking engine is configured to define a clinical management outcome objective relevant to health management criteria and related health management data attributes of the profile in response to medical information content of a user medical information profile (e.g., stored in a hyperledger) associated with the medical information natural language conversation stream in the knowledge cloud. The critical thinking engine is further configured to select a medical intervention likely to advance the clinical management outcome objective. The cognitive agent is configure for presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the action likely to advance the clinical management outcome objective.
In one embodiment, a computer-implemented method for providing action recommendations in response to a user-generated natural language conversation stream is disclosed. The method includes receiving segments of a user-generated natural language conversation stream at an artificial intelligence-based conversation agent from a user interface. The method further includes defining a user action outcome objective relevant to attributes of the profile in response to content of a user profile (e.g., stored in a hyperledger) associated with the user-generated natural language conversation stream. The method further includes selecting an action likely to advance the user action outcome objective. The method further includes presenting to the user in the user-generated natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user action outcome objective.
For a detailed description of example embodiments, reference will now be made to the accompanying drawings in which:
Various terms are used to refer to particular system components. Different companies may refer to a component by different names—this document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . ” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.
The following discussion is directed to various embodiments of the disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.
There are numerous entities involved in various medical transactions in a healthcare ecosystem. For example, the entities may involve patients (consumers), medical personnel (e.g., physicians, nurses, pharmacists, dentists, optometrists, orthodontists, etc.), insurance providers, clinics, hospitals, pharmacies, professional associations, government agencies, and so forth. Example medical transactions may include a physician writing a prescription for a patient, a physician requesting a pharmacy fill or refill a prescription for a patient, a physician updating a medical chart for a patient after a consultation, a patient requesting a pharmacy to fill or refill a prescription, a pharmacy filling a prescription for a patient, a pharmacy or clinic requesting an insurance provider pay for a consultation or for a prescription, a patient requesting an insurance provider pay for a consultation or for a prescription, and so forth.
Various information is provided by the various entities in order to perform the medical transactions. For example, a medical personnel may provide an authorizing credential that indicates the medical personnel is allowed to practice and perform the medical transaction. The authorizing credential may be a license or certificate that has been approved by a professional association or government agency and awarded to the medical personnel. For example, a physician and a pharmacists are required to obtain licenses to practice and perform medical transactions. In other instances, a patient may provide a form of identification (e.g., driver's license) and information pertaining to their insurance provider to a medical facility (e.g., clinic, hospital, pharmacy etc.) to engage in a medical transaction with a medical personnel at that medical facility.
Other information may be generated during the medical transactions. For example, a physician may write a prescription for a patient after diagnosing a medical condition of the patient. The prescription may be a physical document that is sent to the pharmacy or brought by the patient to the pharmacy. Records at the various medical facilities may be generated/updated when the user fills or refills the prescription.
Oftentimes, when a patient attempts to fill a prescription, a pharmacist may attempt to verify the prescription by contacting the physician who wrote the prescription or a representative of the physician. The pharmacist may not reach the physician. Even when the physician is reached, the process may take an undesirable amount of time from start to finish to verify that the physician wrote the prescription. In other instances, the pharmacist may attempt to contact an insurance provider of the patient to verify that the insurance provider is going to pay for any medicine or devices prescribed in the prescription. Again, the insurance provider may not be reached and/or the verification process may take longer than desired.
There are a multitude of other verifications that may occur prior to allowing a medical transaction to be performed. The verifications may occur in real-time, near real-time, or on an ongoing basis to allow the medical transactions to be performed. Some examples may include verifying that medical personnel have up-to-date authorizing credential to practice the medical transactions, verifying that the medical facility (e.g., clinic, hospital, pharmacy, etc.) has an up-to-date authorizing credential to allow performance of the medical transaction at the medical facility, verifying the identity of the patient, verifying information (e.g., how many refills are allowed, how many refills have been used, etc.) pertaining to a prescription of the patient, and so forth.
Conventionally, the authorizing credentials of the medical personnel and/or information pertaining to a patient are stored in disparate data stores or may not be recorded in any data store. Thus, the verification process and the medical transactions in general can be inefficient. A verifiable trace of proof that the medical transactions have been performed and/or are determined to be allowed by being associated with an authorized medical personnel and patient is desired. Further, there is no reliable technique for tracking the information of the entities in the healthcare ecosystem and suggesting when an entity in the healthcare ecosystem should provide updated information to enable medical transactions to continue to be performed by the entities.
Accordingly, aspects of the disclosure generally relate to a cognitive intelligence platform using blockchain in a healthcare ecosystem to maintain information about entities and medical transactions in the healthcare ecosystem and to using analytics based rules that specify when to provide updates to the blockchain. A blockchain may refer to an immutable ledger for recording transactions. The cognitive intelligence platform integrates and consolidates data/information from various sources and entities and provides a population health management service. In some embodiments, at least some of the data/information from the various sources and entities may be stored in a blockchain. The blockchain may be maintained by a distributed network of nodes. In some embodiments, a consensus protocol may be used by the nodes to determine whether to allow transactions to be performed and groups the transactions into blocks that are added to the blockchain.
There are different kinds of blockchains, such as permissionless and permissioned. In a permissionless blockchain, any entity may participate without an identity. In a permissioned blockchain, each entity that participates in the blockchain is identified and known. An example of a permissioned blockchain is a hyperledger. The permissions cause the participating nodes to view only the appropriate transactions in the hyperledger. Programmable logic may be implemented as rules that are executed by the hyperledger. In some embodiments, the rules may be analytics-based and may specify scenarios when updates to the hyperledger are to be made by the various entities of the healthcare ecosystem. Using the analytics-based rules may make each node an active participant by updating the hyperledger at specified times.
The hyperledger may provide a verifiable trace of proof that the information is associated with entities involved in a medical transaction to facilitate more efficient medical transactions, among other things. The hyperledger may provide a secure chain of record that is used to enhance the efficiency and/or security of the medical transaction process in the healthcare ecosystem.
Each entity in the healthcare ecosystem may register as a node in a distributed, decentralized network. Registering a node for an entity may involve a transaction that is added to the hyperledger. Each node may maintain a respective copy of the hyperledger as a shared single source of truth. During registration, each entity may provide certain information pertaining to the entity to be maintained by the hyperledger at the nodes. For example, a physician may register as a node and may provide information (e.g., National Provider Identifier (NPI), license number, date licensed, date license last updated, etc.) pertaining to their authorizing credential, specialty of medical practice, location of practice, and any other information relevant to practicing in the healthcare ecosystem. A pharmacist may register as a node and may provide information (e.g., license number, date licensed, date license last updated, etc.) pertaining to their authorizing credential, location of practice, and any other information relevant to practicing in the healthcare ecosystem. A patient may register as a node and may provide personal information (e.g., driver's license number, social security number, name, insurance provider number, type of insurance, address, medical records, allergies, etc.) that enables verifying their identity and establishing a user profile, among other things.
Each time a medical transaction is requested to be performed between entities, the hyperledger may be used to verify information pertaining to the entities engaged in the medical transaction and to determine whether to allow the medical transaction to be performed. The system may already know, based on the information pertaining to the entities stored in the hyperledger, that the patient is verified and the medical personnel (e.g., physician, pharmacist, etc.) involved in the medical transaction (e.g., writing a prescription, filling the prescription, dispensing medicine) is verified. In some embodiments, a consensus protocol may be used by the nodes to validate the medical transactions, thereby enhancing security of performing the medical transactions. One or more rules may dictate when medical transactions are allowed and/or when to provide updates to the hyperledger. For example, one rule may indicate that the authorizing credential of the physician is required to be valid at the time the prescription was written or when the prescription is filled prior to allowing the medical transaction. In some embodiments, the other nodes may approve the medical transaction based on whether the one or more rules are satisfied.
The medical transactions may be recorded on the hyperledger stored at each node to maintain a ledger of the medical transactions in the healthcare ecosystem. Information maintained by the hyperledger may be protected such that just authorized nodes in any given medical transaction are allowed to use the information. For example, the information stored at a patient node typically is not allowed to be referenced by other patient nodes, unless certain circumstances apply (e.g., the patient nodes are related (parents and children)).
In some embodiments, benefits of the disclosed techniques may enable a patient that initially purchased medicine using a prescription at a first pharmacy to purchase the medicine using the prescription at a second pharmacy without any action by the pharmacist at the second pharmacy. The patient may request the prescription be refilled by the second pharmacy and the authenticating credentials of the patient may be verified by the hyperledger. The hyperledger may identify the prescription stored in a transaction in the hyperledger for the patient, determine whether the physician that wrote the prescription has a valid authorizing credential to write the prescription, whether the pharmacist at the second pharmacy has a valid authorizing credential to fill a prescription, whether the second pharmacy has a valid authorizing credential to dispense medication, and whether there are any refills left on the prescription, among other things. If the results of the determination satisfy one or more rules, then the medical transaction may be approved and the second pharmacy may refill the prescription by just using the information stored in the hyperledger. Accordingly, the medical transaction process for approving filling prescriptions, for example, may be enhanced by removing third-parties.
The cognitive intelligence platform has the ability to extract concepts, relationships, and draw conclusions from a given text posed in natural language (e.g., a passage, a sentence, a phrase, and a question) by performing conversational analysis which includes analyzing conversational context. For example, the cognitive intelligence platform has the ability to identify the relevance of a posed question to another question.
The benefits provided by the cognitive intelligence platform, in the context of healthcare, include freeing up physicians from focusing on day to day population health management. Thus a physician can focus on her core competency—which includes disease/risk diagnosis and prognosis and patient care. The cognitive intelligence platform provides the functionality of a health coach and includes a physician's directions in accordance with the medical community's recommended care protocols and also builds a systemic knowledge base for health management. The cognitive intelligence platform may leverage the information stored in the hyperledger to recommend certain actions be taken by a patient. For example, using the hyperledger, the recommended actions may include setting up a consultation with a physician having a valid authorizing credential at a location near the patient (e.g., based on geolocations of devices of the entities).
The cognitive intelligence platform may implement an intuitive conversational cognitive agent that engages in a question and answering system that is human-like in tone and response. The described cognitive intelligence platform endeavors to compassionately solve goals, questions and challenges. Further, the cognitive intelligence platform may use a hyperledger to manage medical transactions between entities in a healthcare ecosystem more efficiently and/or securely. The described methods and systems are described as occurring in the healthcare space, though other areas are also contemplated.
The several computing devices work in conjunction to implement components of the cognitive intelligence platform 102 including: a knowledge cloud 106; a critical thinking engine 108; a natural language database 122; a cognitive agent 110; and a node 116. The cognitive intelligence platform 102 is not limited to implementing only these components, or in the manner described in
The node 116 represents a single computing device in a distributed blockchain network of nodes 116 (also referred to as a distributed hyperledger fabric herein) of the cognitive intelligence platform 102. A permissioned type of blockchain, referred to as a hyperledger 118, may be implemented and a respective copy of the hyperledger 118 may be stored on a respective node 116. The nodes 116 may represent any suitable entity in a healthcare ecosystem. For example, some of the entities may include a service provider 112 (e.g., medical personnel entity, such as a physician, dentist, pharmacist, optometrist, orthodontic, nurse, etc.), a facility 114 (e.g., medical facility entity), a patient entity, and so forth. Each entity may be associated with a respective computing device that they use to register as a node on the blockchain network and request transactions to be performed using the hyperledger 118.
In a permissioned blockchain, such as the hyperledger 118, the entities register by providing certain information to the hyperledger. Based on one or more rules associated with the hyperledger 118, the entity may be registered as a node 116 on the blockchain network and provided authenticating credentials that are used to identify the entities when they perform transactions. The rules may be executable software modules that are installed in the hyperledger 118 itself. In some instances, when a user sends a transaction to the hyperledger 118, the hyperledger 118 may invoke the rules, which perform functions depending on the type of transaction being requested. In addition, the nodes 116 may employ a consensus protocol whereby the nodes 116 communicate with each other to determine whether to allow the transaction to be performed to modify the hyperledger 118.
The entities use computing devices to send requests to perform transactions (e.g., medical transactions) using the hyperledger 118 to the cognitive intelligence platform 102. When applicable rules and/or the consensus protocol is satisfied, the transaction may be completed (e.g., writing a prescription, filling the prescription, etc.) and a record of the transaction may be added to the hyperledger 118. In some instances, the transactions may not be altered or removed, thereby providing an immutable quality to the hyperledger 118. Further, cryptography may be used to secure the hyperledger 118 and the messages between the nodes 116 of the blockchain network and/or the computing devices requesting the transactions. In some embodiments, just the authorized entities are allowed to perform the transactions on the hyperledger 118, and in some instances, just the appropriate entities are allowed to view details of particular transactions in the hyperledger 118.
In some embodiments, a request to register as a node 116 may be a type of transaction that is recorded in the hyperledger 118. The entities may send the requests to register as a node 116 using the hyperchain 118, and the requests include certain information pertaining to the entities. For example, a medical personnel entity may provide an authorizing credential, such as a medical license number. If the rules and/or the consensus protocol is satisfied, the entity may be associated with a node 116. Further, the hyperledger 118 may be updated by adding a block storing the transaction including the information pertaining to the entity that is associated with the node 116. The updated hyperledger 118 may be stored at the node for the entity. In some embodiments, the copies of the other hyperledgers 118 at the other nodes 116 in the blockchain network may be updated with the new transaction. Further, when the entity is registered as a node 116, the computing device associated with that entity may be provided with authenticating credentials for that entity. The computing device may use the authenticating credentials to make subsequent requests to the hyperledger 118.
In some embodiments, a medical transaction, such as a physician writing a prescription for a patient, a physician updating health records of a patient, a physician requesting a pharmacy fills a prescription, a physician approving additional refills on a prescription, a patient requesting a pharmacy fill or refill a prescription, a pharmacist filling or refilling a prescription for a patient, a pharmacy dispensing medicine specified in a prescription for a user, and so forth, may be a type of transaction that is recorded in the hyperledger 118. The hyperledger 118 may be used as a verifiable trace of proof to determine that the source of certain medical transactions (e.g., a prescription was written by a physician with a valid medical license) were performed by proper entities having valid authorizing credentials prior to allowing subsequent medical transactions (e.g., filling the prescription for the authenticated patient).
The knowledge cloud 106 represents a set of instructions executing within the cognitive intelligence platform 102 that implement a database configured to receive inputs from several sources and entities. For example, some of the sources and entities include a service provider 112, a facility 114, and a microsurvey 116—each described further below.
The critical thinking engine 108 represents a set of instructions executing within the cognitive intelligence platform 102 that execute tasks using artificial intelligence, such as recognizing and interpreting natural language (e.g., performing conversational analysis), and making decisions in a linear manner (e.g., in a manner similar to how the human left brain processes information). Specifically, an ability of the cognitive intelligence platform 102 to understand natural language is powered by the critical thinking engine 108. In various embodiments, the critical thinking engine 108 includes a natural language database 122. The natural language database 112 includes data curated over at least thirty years by linguists and computer data scientists, including data related to speech patterns, speech equivalents, and algorithms directed to parsing sentence structure.
Furthermore, the critical thinking engine 108 is configured to deduce causal relationships given a particular set of data, where the critical thinking engine 108 is capable of taking the individual data in the particular set, arranging the individual data in a logical order, deducing a causal relationship between each of the data, and drawing a conclusion. The ability to deduce a causal relationship and draw a conclusion (referred to herein as a “causal” analysis) is in direct contrast to other implementations of artificial intelligence that mimic the human left brain processes. For example, the other implementations can take the individual data and analyze the data to deduce properties of the data or statistics associated with the data (referred to herein as an “analytical” analysis). However, these other implementations are unable to perform a causal analysis—that is, deduce a causal relationship and draw a conclusion from the particular set of data. As described further below—the critical thinking engine 108 is capable of performing both types of analysis: causal and analytical.
The cognitive agent 110 represents a set of instructions executing within the cognitive intelligence platform 102 that implement a client-facing component of the cognitive intelligence platform 102. The cognitive agent 110 is an interface between the cognitive intelligence platform 102 and the user device 104. And in some embodiments, the cognitive agent 110 includes a conversation orchestrator 124 that determines pieces of communication that are presented to the user device 104 (and the user). When a user of the user device 104 interacts with the cognitive intelligence platform 102, the user interacts with the cognitive agent 110. The several references herein, to the cognitive agent 110 performing a method, can implicate actions performed by the critical thinking engine 108, which accesses data in the knowledge cloud 106, the natural language database 122, and/or the hyperledger 118.
In various embodiments, the several computing devices executing within the cognitive intelligence platform are communicably coupled by way of a network/bus interface. Furthermore, the various components (e.g., the knowledge cloud 106, the critical thinking engine 108, the cognitive agent 110, and the node 116), are communicably coupled by one or more inter-host communication protocols 118. In one example, the knowledge cloud 106 is implemented using a first computing device, the critical thinking engine 108 is implemented using a second computing device, the cognitive agent 110 is implemented using a third computing device, and the node 116 is a fourth computing device, where each of the computing devices are coupled by way of the inter-host communication protocol 118. Although in this example, the individual components are described as executing on separate computing devices this example is not meant to be limiting, the components can be implemented on the same computing device, or partially on the same computing device, without departing from the scope of this disclosure.
The user device 104 represents any form of a computing device, or network of computing devices, e.g., a personal computing device, a smart phone, a tablet, a wearable computing device, a notebook computer, a media player device, and a desktop computing device. The user device 104 includes a processor, at least one memory, and at least one storage. A user uses the user device 104 to input a given text posed in natural language (e.g., typed on a physical keyboard, spoken into a microphone, typed on a touch screen, or combinations thereof) and interacts with the cognitive intelligence platform 102, by way of the cognitive agent 110.
A user (e.g., patient entity) may also use a software application installed on the user device 104 to request medical transactions to be performed using authenticating credentials provided to the user device 104 during registration of the user as a node 116 on the blockchain network. Such an implementation makes the blockchain node 116 an active participant in the hyperledger 118. In some embodiments, the medical transactions may include filling a prescription written for the user, refilling a prescription written for the user, scheduling a consultation with a medical personnel entity, and so forth. The requests may be sent to the cognitive intelligence platform 102 (e.g., by way of the cognitive agent 110) to determine, based on the one or more rules and/or the consensus protocol, whether to allow the medical transaction to be performed and to update the hyperledger 118 with a record of the medical transaction once performed.
In some embodiments, the software application may be logged into the cognitive intelligence platform 102 via the cognitive agent 110 using the authenticating credential for the user, and the software application may query the hyperledger 118 and determine that the user has not filled or refilled their prescription. The software application may present a prompt to the user on the user device 104 instructing the user to fill or refill their prescription.
The architecture 100 includes a network 120 that communicatively couples various devices, including the cognitive intelligence platform 102 and the user device 104. The network 120 can include local area network (LAN) and wide area networks (WAN). The network 102 can include wired technologies (e.g., Ethernet®) and wireless technologies (e.g., Wi-Fi®, code division multiple access (CDMA), global system for mobile (GSM), universal mobile telephone service (UMTS), Bluetooth®, and ZigBee®. For example, the user device 104 can use a wired connection or a wireless technology (e.g., Wi-Fi®) to transmit and receive data over the network 120.
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Throughout the course of a relationship between the service provider 112 and a user (e.g., the service provider 112 provides healthcare to a patient), the service provider 112 collects and generates data associated with the patient or the user, including health records that include doctor's notes and prescriptions, billing records, and insurance records. The service provider 112, using a computing device (e.g., a desktop computer or a tablet), provides the data associated with the user to the cognitive intelligence platform 102, and more specifically the knowledge cloud 106. This data associated with the user may be stored in the hyperledger 118, in some embodiments.
For example, the service provider 112 (e.g., medical personnel entity) may use a computing device associated with the service provider 112 to make requests for medical transactions to be performed using authenticating credentials provided to the computing device during registration of the service provider 112 as a node 116 on the blockchain network. Such an implementation makes the blockchain node 116 an active participant in the hyperledger 118. In some embodiments, the medical transactions may include writing a prescription for the patient entity, requesting a pharmacy fill a prescription for the patient entity, authorizing additional refills for a prescription for a patient entity, updating a medical file for the patient entity based using the data associated with the user, and so forth. The requests may be sent to the cognitive intelligence platform 102 (e.g., by way of the cognitive agent 110) to determine, based on the one or more rules and/or the consensus protocol, whether to allow the medical transaction to be performed and to update the hyperledger 118 with a record of the medical transaction once performed. In some embodiments, the rule may specify that the service provider 112 is required to have a valid authorizing credential to perform the medical transaction. The rule may execute and check a transaction in the hyperledger 118 storing the authorizing credential of the service provider 112 to determine whether the authorizing credential is valid. In some embodiments, a request may be transmitted to a professional association or government agency that issued the authorizing credential to the service provider 112 to determine if the authorizing credential is still valid.
Another example source that provides data to the knowledge cloud 106 is the facility 114 (e.g., medical facility entity). The facility 114 represents a location owned, operated, or associated with any entity including the service provider 112. As used herein, an entity represents an individual or a collective with a distinct and independent existence. An entity can be legally recognized (e.g., a sole proprietorship, a partnership, a corporation) or less formally recognized in a community. For example, the entity can include a company that owns or operates a gym (facility). Additional examples of the facility 114 include, but is not limited to, a hospital, a trauma center, a clinic, a dentist's office, a pharmacy, a store (including brick and mortar stores and online retailers), an out-patient care center, a specialized care center, a birthing center, a gym, a cafeteria, and a psychiatric care center.
As the facility 114 represents a large number of types of locations, for purposes of this discussion and to orient the reader by way of example, the facility 114 represents the doctor's office or a gym. The facility 114 generates additional data associated with the user such as appointment times, an attendance record (e.g., how often the user goes to the gym), a medical record, a billing record, a purchase record, an order history, and an insurance record. The facility 114, using a computing device (e.g., a desktop computer or a tablet), provides the data associated with the user to the cognitive intelligence platform 102, and more specifically the knowledge cloud 106. This data associated with the user may be stored in the hyperledger 118, in some embodiments.
For example, the facility 114 may use a computing device associated with the facility to make requests for medical transactions to be performed using authenticating credentials provided to the computing device during registration of the facility 114 as a node 116 on the blockchain network. In some embodiments, the medical transactions may include providing an authorizing credential that authorize the facility 114 to perform a medical transaction (e.g., dispense a controlled substance or medicine), requesting the data generated by the facility 114 to be stored for the user, requesting to dispense medication or fill a prescription and so forth. The requests may be sent to the cognitive intelligence platform 102 (e.g., by way of the cognitive agent 110) to determine, based on the one or more rules and/or the consensus protocol, whether to allow the medical transaction to be performed and to update the hyperledger 118 with a record of the medical transaction once performed. In some embodiments, the rule may specify that the facility 114 is required to have a valid authorizing credential to perform the medical transaction. The rule may execute and check a transaction in the hyperledger 118 storing the authorizing credential of the facility 114 to determine whether the authorizing credential is valid. In some embodiments, a request may be transmitted to a professional association or government agency that issued the authorizing credential to the facility 114 to determine if the authorizing credential is still valid.
An additional example source that provides data to the knowledge cloud 106 is the microsurvey 116. The microsurvey 116 represents a tool created by the cognitive intelligence platform 102 that enables the knowledge cloud 106 to collect additional data associated with the user. The microsurvey 116 is originally provided by the cognitive intelligence platform 102 (by way of the cognitive agent 110) and the user provides data responsive to the microsurvey 116 using the user device 104. Additional details of the microsurvey 116 are described below.
Yet another example source that provides data to the knowledge cloud 106, is the cognitive intelligence platform 102, itself. In order to address the care needs and well-being of the user, the cognitive intelligence platform 102 collects, analyzes, and processes information from the user, healthcare providers, and other eco-system participants, and consolidates and integrates the information into knowledge. The knowledge can be shared with the user and stored in the knowledge cloud 106.
In various embodiments, the computing devices used by the service provider 112 and the facility 114 are communicatively coupled to the cognitive intelligence platform 102, by way of the network 120. While data is used individually by various entities including: a hospital, practice group, facility, or provider, the data is less frequently integrated and seamlessly shared between the various entities in the current art. The cognitive intelligence platform 102 provides a solution that integrates data from the various entities. That is, the cognitive intelligence platform 102 ingests, processes, and disseminates data and knowledge in an accessible fashion, where the reason for a particular answer or dissemination of data is accessible by a user.
In particular, the cognitive intelligence platform 102 (e.g., by way of the cognitive agent 110 interacting with the user) holistically manages and executes a health plan for durational care and wellness of the user (e.g., a patient or consumer). The health plan includes various aspects of durational management that is coordinated through a care continuum.
The cognitive agent 110 can implement various personas that are customizable. For example, the personas can include knowledgeable (sage), advocate (coach), and witty friend (jester). And in various embodiments, the cognitive agent 110 persists with a user across various interactions (e.g., conversations streams), instead of being transactional or transient. Thus, the cognitive agent 110 engages in dynamic conversations with the user, where the cognitive intelligence platform 102 continuously deciphers topics that a user wants to talk about. The cognitive intelligence platform 102 has relevant conversations with the user by ascertaining topics of interest from a given text posed in a natural language input by the user. Additionally the cognitive agent 110 connects the user to healthcare service providers, hyperlocal health communities, and a variety of services and tools/devices, based on an assessed interest of the user. In some embodiments, the cognitive agent 110 may connect the user to healthcare service providers that are in a vicinity of the geolocation of the user device 104 based on certain information stored in the hyperledger 118 (e.g., which healthcare service providers have valid authorizing credentials, location of the healthcare service providers, speciality of the healthcare service provider, health issue of the patient, etc.).
As the cognitive agent 110 persists with the user, the cognitive agent 110 can also act as a coach and advocate while delivering pieces of information to the user based on tonal knowledge, human-like empathies, and motivational dialog within a respective conversational stream, where the conversational stream is a technical discussion focused on a specific topic. Overall, in response to a question—e.g., posed by the user in natural language—the cognitive intelligence platform 102 consumes data from and related to the user and computes an answer. The answer is generated using a rationale that makes use of common sense knowledge, domain knowledge, evidence-based medicine guidelines, clinical ontologies, and curated medical advice. Thus, the content displayed by the cognitive intelligence platform 102 (by way of the cognitive agent 110) is customized based on the language used to communicate with the user, as well as factors such as a tone, goal, and depth of topic to be discussed.
Overall, the cognitive intelligence platform 102 may be accessible to a user (e.g., patient entity), medical facility entities (e.g., a hospital system, clinics, pharmacies), medical personnel entities (e.g., physicians, pharmacists, dentists, optometrists, etc.), insurance provider entities, professional association entities, and government agency entities. Additionally, the cognitive intelligence platform 102 is accessible to paying entities interested in user behavior—e.g., the outcome of physician-consumer interactions in the context of disease or the progress of risk management. Additionally, entities that provides specialized services such as tests, therapies, and clinical processes that need risk based interactions can also receive filtered leads from the cognitive intelligence platform 102 for potential clients.
In various embodiments, the cognitive intelligence platform 102 is configured to perform conversational analysis in a general setting. The topics covered in the general setting is driven by the combination of agents (e.g., cognitive agent 110) selected by a user. In some embodiments, the cognitive intelligence platform 102 uses conversational analysis to identify the intent of the user (e.g., find data, ask a question, search for facts, find references, and find products) and a respective micro-theory in which the intent is logical.
For example, the cognitive intelligence platform 102 applies conversational analysis to decode what the user is asking or stated, where the question or statement is in free form language (e.g., natural language). Prior to determining and sharing knowledge (e.g., with the user or the knowledge cloud 106), using conversational analysis, the cognitive intelligence platform 102 identifies an intent of the user and overall conversational focus.
The cognitive intelligence platform 102 responds to a statement or question according to the conversational focus and steers away from another detected conversational focus so as to focus on a goal defined by the cognitive agent 110. Given an example statement of a user, “I want to fly out tomorrow,” the cognitive intelligence platform 102 uses conversational analysis to determine an intent of the statement. Is the user aspiring to be bird-like or does he want to travel? In the former case, the micro-theory is that of human emotions whereas in the latter case, the micro-theory is the world of travel. Answers are provided to the statement depending on the micro-theory in which the intent logically falls.
The cognitive intelligence platform 102 utilize a combination of linguistics, artificial intelligence, and decision trees to decode what a user is asking or stating. The discussion includes methods and system design considerations and results from an existing embodiment. Additional details related to conversational analysis are discussed next.
For purposes of this discussion, the concept of analyzing conversational context as part of conversational analysis is now described. To analyze conversational context, the following steps are taken: 1) obtain text (e.g., receive a question) and perform translations; 2) understand concepts, entities, intents, and micro-theory; 3) relate and search; 4) ascertain the existence of related concepts; 5) logically frame concepts or needs; 6) understand the questions that can be answered from available data; and 7) answer the question. Each of the foregoing steps is discussed next, in turn.
In various embodiments, the cognitive intelligence platform 102 (
The example text below is used to described methods in accordance with various embodiments herein:
The cognitive intelligence platform 102 analyzes the example text above to detect structural elements within the example text (e.g., paragraphs, sentences, and phrases). In some embodiments, the example text is compared to other sources of text such as dictionaries, and other general fact databases (e.g., Wikipedia) to detect synonyms and common phrases present within the example text.
In step 2, the cognitive intelligence platform 102 parses the text to ascertain concepts, entities, intents, and micro-theories. An example output after the cognitive intelligence platform 102 initially parses the text is shown below, where concepts, and entities are shown in bold.
For example, the cognitive intelligence platform 102 ascertains that Cambridge is a university—which is a full understanding of the concept. The cognitive intelligence platform (e.g., the cognitive agent 110) understands what humans do in Cambridge, and an example is described below in which the cognitive intelligence platform 102 performs steps to understand a concept.
For example, in the context of the above example, the cognitive agent 110 understands the following concepts and relationships:
The cognitive agent 110 also assimilates other understandings to enhance the concepts, such as:
The statements (1)-(7) are not picked at random. Instead the cognitive agent 110 dynamically constructs the statements (1)-(7) from logic or logical inferences based on the example text above. Formally, the example statements (1)-(7) are captured as follows:
Next, in step 3, the cognitive agent 110 relates various entities and topics and follows the progression of topics in the example text. Relating includes the cognitive agent 110 understanding the different instances of Hardy are all the same person, and the instances of Hardy are different from the instances of Littlewood. The cognitive agent 110 also understands that the instances Hardy and Littlewood share some similarities—e.g., both are mathematicians and they did some work together at Cambridge on Number Theory. The ability to track this across the example text is referred to as following the topic progression with a context.
Next, in Step 4, the cognitive agent 110 asserts non-existent concepts or relations to form new knowledge. Step 4 is an optional step for analyzing conversational context. Step 4 enhances the degree to which relationships are understood or different parts of the example text are understood together. If two concepts appear to be separate—e.g., a relationship cannot be graphically drawn or logically expressed between enough sets of concepts—there is a barrier to understanding. The barriers are overcome by expressing additional relationships. The additional relationships can be discovered using strategies like adding common sense or general knowledge sources (e.g., using the common sense data 208) or adding in other sources including a lexical variant database, a dictionary, and a thesaurus.
One example of concept progression from the example text is as follows: the cognitive agent 110 ascertains the phrase “theorems that Ramanujan said he had discovered” is related to the phrase “his results”, which is related to “Ramanujan's work is in number theory, a branch of mathematics that deals with the subtle laws and relationships that govern numbers.”
In Step 5, the cognitive agent 110 determines missing parameters—which can include for example, missing entities, missing elements, and missing nodes—in the logical framework (e.g., with a respective micro-theory). The cognitive agent 110 determines sources of data that can inform the missing parameters. Step 5 can also include the cognitive agent 110 adding common sense reasoning and finding logical paths to solutions.
With regards to the example text, some common sense concepts include:
With regards to the example text, some passage concepts include:
Within the micro-theory of the passage analysis, the cognitive agent 110 understands and catalogs available paths to answer questions. In Step 5, the cognitive agent 110 makes the case that the concepts (12)-(20) are expressed together.
Step 6: Understand the Questions that can be Answered from Available Data
In Step 6, the cognitive agent 110 parses sub-intents and entities. Given the example text, the following questions are answerable from the cognitive agent's developed understanding of the example text, where the understanding was developed using information and context ascertained from the example text as well as the common sense data 208 (
Based on the information that is understood by the cognitive agent 110, the questions (21)-(23) can be answered.
By using an exploration method such as random walks, the cognitive agent 110 makes a determination as the paths that are plausible and reachable with the context (e.g., micro-theory) of the example text. Upon explorations, the cognitive agent 110 catalogs a set of meaningful questions. The set of meaningful questions are not asked, but instead explored based on the cognitive agent's understanding of the example text.
Given the example text, an example of exploration that yields a positive result is: “a situation X that caused Ramanujan's position.” In contrast, an example of exploration that causes irrelevant results is: “a situation Y that caused Cambridge.” The cognitive agent 110 is able to deduce that the latter exploration is meaningless, in the context of a micro-theory, because situations do not cause universities. Thus the cognitive agent 110 is able to deduce, there are no answers to Y, but there are answers to X.
In Step 7, the cognitive agent 110 provides a precise answer to a question. For an example question such as: “What situation causally contributed to Ramanujan's position at Cambridge?” the cognitive agent 110 generates a precise answer using the example reasoning:
In order to generate the above reasoning statements (24)-(26), the cognitive agent 110 utilizes a solver or prover in the context of the example text's micro-theory—and associated facts, logical entities, relations, and assertions. As an additional example, the cognitive agent 110 uses a reasoning library that is optimized for drawing the example conclusions above within the fact, knowledge, and inference space (e.g., work space) that the cognitive agent 110 maintains.
By implementing the steps 1-7, the cognitive agent 110 analyzes conversational context. The described method for analyzing conversation context can also be used for recommending items in conversations streams. A conversational stream is defined herein as a technical discussion focused on specific topics. As related to described examples herein, the specific topics relate to health (e.g., diabetes). Throughout the lifetime of a conversational stream, a cognitive agent 110 collect information over may channels such as chat, voice, specialized applications, web browsers, contact centers, and the like.
By implementing the methods to analyze conversational context, the cognitive agent 110 can recommend a variety of topics and items throughout the lifetime of the conversational stream. Examples of items that can be recommended by the cognitive agent 110 include: surveys, topics of interest, local events, devices or gadgets, dynamically adapted health assessments, nutritional tips, reminders from a health events calendar, and the like.
Accordingly, the cognitive intelligence platform 102 provides a platform that codifies and takes into consideration a set of allowed actions and a set of desired outcomes. The cognitive intelligence platform 102 relates actions, the sequences of subsequent actions (and reactions), desired sub-outcomes, and outcomes, in a way that is transparent and logical (e.g., explainable). The cognitive intelligence platform 102 can plot a next best action sequence and a planning basis (e.g., health care plan template, or a financial goal achievement template), also in a manner that is explainable. The cognitive intelligence platform 102 can utilize a critical thinking engine 108 and a natural language database 122 (e.g., a linguistics and natural language understanding system) to relate conversation material to actions.
For purposes of this discussion, several examples are discussed in which conversational analysis is applied within the field of durational and whole-health management for a user. The discussed embodiments holistically address the care needs and well-being of the user during the course of his life. The methods and systems described herein can also be used in fields outside of whole-health management, including: phone companies that benefits from a cognitive agent; hospital systems or physicians groups that want to coach and educate patients; entities interested in user behavior and the outcome of physician-consumer interactions in terms of a progress of disease or risk management; entities that provide specialized services (e.g., test, therapies, clinical processes) to filter leads; and sellers, merchants, stores and big box retailers that want to understand which product to sell.
The service provider data 202 is data provided by the service provider 112 (described in
Common sense data 208 is data that has been identified as “common sense”, and can include rules that govern a respective concept and used as glue to understand other concepts.
Domain data 210 is data that is specific to a certain domain or subject area. The source of the domain data 210 can include digital libraries. In the healthcare industry, for example, the domain data 210 can include data specific to the various specialties within healthcare such as, obstetrics, anesthesiology, and dermatology, to name a few examples. In the example described herein, the evidence-based guidelines 212 include systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances.
Curated advice 214 includes advice from experts in a subject matter. The curated advice 214 can include peer-reviewed subject matter, and expert opinions. Subject matter ontology data 216 includes a set of concepts and categories in a subject matter or domain, where the set of concepts and categories capture properties and relationships between the concepts and categories.
In particular,
Initially, the user asks a general question, which is treated by the cognitive agent 110 as an “originating question.” The originating question is classified into any number of potential questions (“pursuable questions”) that are pursued during the course of a subsequent conversation. In some embodiments, the pursuable questions are identified based on a subject matter domain or goal. In some embodiments, classification techniques are used to analyze language (e.g., such as those outlined in HPS ID20180901-01_method for conversational analysis). Any known text classification technique can be used to analyze language and the originating question. For example, in line 402, the user enters an originating question about a subject matter (e.g., blood sugar) such as: “Is a blood sugar of 90 normal”? I
In response to receiving an originating question, the cognitive intelligence platform 102 (e.g., the cognitive agent 110 operating in conjunction with the critical thinking engine 108) performs a first round of analysis (e.g., which includes conversational analysis) of the originating question and, in response to the first round of analysis, creates a workspace and determines a first set of follow up questions.
In various embodiments, the cognitive agent 110 may go through several rounds of analysis executing within the workspace, where a round of analysis includes: identifying parameters, retrieving answers, and consolidating the answers. The created workspace can represent a space where the cognitive agent 110 gathers data and information during the processes of answering the originating question. In various embodiments, each originating question corresponds to a respective workspace. The conversation orchestrator 124 can assess data present within the workspace and query the cognitive agent 110 to determine if additional data or analysis should be performed.
In particular, the first round of analysis is performed at different levels, including analyzing natural language of the text, and analyzing what specifically is being asked about the subject matter (e.g., analyzing conversational context). The first round of analysis is not based solely on a subject matter category within which the originating question is classified. For example, the cognitive intelligence platform 102 does not simply retrieve a predefined list of questions in response to a question that falls within a particular subject matter, e.g., blood sugar. That is, the cognitive intelligence platform 102 does not provide the same list of questions for all questions related to the particular subject matter. Instead, for example, the cognitive intelligence platform 102 creates dynamically formulated questions, curated based on the first round of analysis of the originating question.
In particular, during the first round of analysis, the cognitive agent 110 parses aspects of the originating question into associated parameters. The parameters represent variables useful for answering the originating question. For example, the question “is a blood sugar of 90 normal” may be parsed and associated parameters may include, an age of the inquirer, the source of the value 90 (e.g., in home test or a clinical test), a weight of the inquirer, and a digestive state of the user when the test was taken (e.g., fasting or recently eaten). The parameters identify possible variables that can impact, inform, or direct an answer to the originating question.
For purposes of the example illustrated in
The follow up questions are based on the identified parameters, which in turn are based on the specifics of the originating question (e.g., related to an identified micro-theory). Thus the first set of follow-up questions identified in response to, if a blood sugar is normal, will be different from a second set of follow up questions identified in response to a question about how to maintain a steady blood sugar.
After identifying the first set of follow up questions, in this example first round of analysis, the cognitive intelligence platform 102 determines which follow up question can be answered using available data and which follow-up question to present to the user. As described over the next few paragraphs, eventually, the first set of follow-up questions is reduced to a subset (“a second set of follow-up questions”) that includes the follow-up questions to present to the user.
In various embodiments, available data is sourced from various locations, including a user account, the knowledge cloud 106, and other sources. Other sources can include a service that supplies identifying information of the user, where the information can include demographics or other characteristics of the user (e.g., a medical condition, a lifestyle). For example, the service can include a doctor's office or a physical therapist's office.
Another example of available data includes the user account. For example, the cognitive intelligence platform 102 determines if the user asking the originating question, is identified. A user can be identified if the user is logged into an account associated with the cognitive intelligence platform 102. User information from the account is a source of available data. The available data is inserted into the workspace of the cognitive agent 110 as a first data.
Another example of available data includes the data stored within the knowledge cloud 106. For example, the available data includes the service provider data 202 (
Follow up questions presented to the user (the second set of follow-up questions) are asked using natural language and are specifically formulated (“dynamically formulated question”) to elicit a response that will inform or fulfill an identified parameter. Each dynamically formulated question can target one parameter at a time. When answers are received from the user in response to a dynamically formulated question, the cognitive intelligence platform 102 inserts the answer into the workspace. In some embodiments, each of the answers received from the user and in response to a dynamically formulated question, is stored in a list of facts. Thus the list of facts include information specifically received from the user, and the list of facts is referred to herein as the second data.
With regards to the second set of follow-up questions (or any set of follow-up questions), the cognitive intelligence platform 102 calculates a relevance index, where the relevance index provides a ranking of the questions in the second set of follow-up questions. The ranking provides values indicative of how relevant a respective follow-up question is to the originating question. To calculate the relevance index, the cognitive intelligence platform 102 can use conversations analysis techniques described in HPS ID20180901-01_method. In some embodiments, the first set or second set of follow up questions is presented to the user in the form of the microsurvey 116.
In this first round of analysis, the cognitive intelligence platform 102 consolidates the first and second data in the workspace and determines if additional parameters need to be identified, or if sufficient information is present in the workspace to answer the originating question. In some embodiments, the cognitive agent 110 (
For a complex originating question, the cognitive intelligence platform 102 can go through several rounds of analysis. For example, in a first round of analysis the cognitive intelligence platform 102 parses the originating question. In a subsequent round of analysis, the cognitive intelligence platform 102 can create a sub question, which is subsequently parsed into parameters in the subsequent round of analysis. The cognitive intelligence platform 102 is smart enough to figure out when all information is present to answer an originating question without explicitly programming or pre-programming the sequence of parameters that need to be asked about.
In some embodiments, the cognitive agent 110 is configured to process two or more conflicting pieces of information or streams of logic. That is, the cognitive agent 110, for a given originating question can create a first chain of logic and a second chain of logic that leads to different answers. The cognitive agent 110 has the capability to assess each chain of logic and provide only one answer. That is, the cognitive agent 110 has the ability to process conflicting information received during a round of analysis.
Additionally, at any given time, the cognitive agent 110 has the ability to share its reasoning (chain of logic) to the user. If the user does not agree with an aspect of the reasoning, the user can provide that feedback which results in affecting change in a way the critical thinking engine 108 analyzed future questions and problems.
Subsequent to determining enough information is present in the workspace to answer the originating question, the cognitive agent 110 answers the question, and additionally can suggest a recommendation or a recommendation (e.g., line 418). The cognitive agent 110 suggests the reference or the recommendation based on the context and questions being discussed in the conversation (e.g., conversation 400). The reference or recommendation serves as additional handout material to the user and is provided for informational purposes. The reference or recommendation often educates the user about the overall topic related to the originating question.
In the example illustrated in
The user 401 enters his answer in line 408: “It was an in-home test,” which the cognitive agent 110 further analyzes to determine additional parameters: e.g., a digestive state, where the additional parameter and a corresponding dynamically formulated question as an additional second set of follow-up questions. Accordingly, the cognitive agent 110 poses the additional dynamically formulated question in lines 410 and 412: “One other question . . . ” and “How long before you took that in-home glucose test did you have a meal?” The user provides additional information in response “it was about an hour” (line 414).
The cognitive agent 110 consolidates all the received responses using the critical thinking engine 108 and the knowledge cloud 106 and determines an answer to the initial question posed in line 402 and proceeds to follow up with a final question to verify the user's initial question was answered. For example, in line 416, the cognitive agent 110 responds: “It looks like the results of your test are at the upper end of the normal range of values for a glucose test given that you had a meal around an hour before the test.” The cognitive agent 110 provides additional information (e.g., provided as a link): “Here is something you could refer,” (line 418), and follows up with a question “Did that answer your question?” (line 420).
As described above, due to the natural language database 108, in various embodiments, the cognitive agent 110 is able to analyze and respond to questions and statements made by a user 401 in natural language. That is, the user 401 is not restricted to using certain phrases in order for the cognitive agent 110 to understand what a user 401 is saying. Any phrasing, similar to how the user would speak naturally can be input by the user and the cognitive agent 110 has the ability to understand the user.
In one embodiment, the cognitive intelligence platform 102 identified parameters for an originating question based on a knowledge graph illustrated in
Next, the method includes prompting the user to build his profile (block 604). In various embodiments, building his profile includes displaying a GUI asking the user to enter in additional information, such as age, weight, height, and health concerns. In various embodiments, the steps of building a user profile is progressive, where building the user profile takes place over time. In some embodiments, the process of building the user profile is presented as a game. Where a user is presented with a ladder approach to create a “star profile”. Aspects of a graphical user interface presented during the profile building step are additionally discussed in
The method contemplates the build profile (block 604) method step is optional. For example, the user may complete building his profile at this method step 604, the user may complete his profile at a later time, or the cognitive intelligence platform 102 builds the user profile over time as more data about the user is received and processed. For example, the user is prompted to build his profile, however, the user fails to enter in information or skips the step. The method proceeds to prompting a user to complete a microsurvey (block 606). In some embodiments, the cognitive agent 110 uses answers received in response to the microsurvey to build the profile of the user. Overall, the data collected through the user registration process is stored and used later as available data to inform answers to missing parameters.
Next, the cognitive agent 110 proceeds to scheduling a service (block 608). The service can be scheduled such that it aligns with a health plan of the user or a protocol that results in a therapeutic goal. Next, the cognitive agent 110 proceeds to reaching agreement on a care plan (block 610).
Next the method determine if the user provided data to build a profile (decision block 706). If the user did not provide data to build the profile, the method proceeds to building profile based on first and second data (block 708). If the user provided data to build the profile, the method proceeds to block 710.
At block 710, the method 700 proceeds to receiving an originating question about a specific subject matter, where the originating question is entered using natural language, and next the method proceeds to performing a round of analysis (block 712). Next, the method determines if sufficient data is present to answer originating questions (decision block 714). If no, the method proceeds to block 712 and the method performs another round of analysis. If yes, the method proceeds to setting goals (block 716), then tracking progress (block 718), and then providing updates in a news feed (block 720).
In
In
The screen shot 803 displays the same GUI as in the screen shot 801, however, the user has scrolled down the menu, such that additional menu items below Health Plans & Assessments (element 812) are shown. The additional menu items include Reports (element 814), Health Team (element 816), and Purchases and Services (Element 818). Furthermore, additional menu items include Add your Health Team (element 820) and Read about improving your A1C levels (element 822).
For purposes of the example in
In
Each of these elements 852, 864, 866, 868, and 870 can display additional sub-elements depending on a selection of the user. For example, as shown in the screen shot 851, Know How YOUR Body Works (element 852) includes additional sub-elements: Diabetes Personal Assessment (854); and Functional Changes (856). Additional sub-elements under Functional Changes (856) include: Blood Sugar Processing (858) and Manageable Risks (860). Finally, the sub-element Manageable Risks (860) includes an additional sub-element Complications (862). For purposes of this example, the user selects the Diabetes Personal Assessment (854) and the screen shot 853 shows a GUI (872) associated with the Diabetes Personal Assessment.
The Diabetes Personal Assessment includes questions such as “Approximately what year was your Diabetes diagnosed” and corresponding elements a user can select to answer including “Year” and “Can't remember” (element 874). Additional questions include “Is your Diabetes Type 1 or Type 2” and corresponding answers selectable by a user include “Type 1,” “Type 2,” and “Not sure” (element 876). Another question includes “Do you take medication to manage your blood sugar” and corresponding answers selectable by a user include “Yes” and “No” (element 878). An additional question asks “Do you have a healthcare professional that works with you to manage your Diabetes” and corresponding answers selectable by the user include “Yes” and “No” (element 880).
In various embodiments, the cognitive intelligence platform 102 collects information about the user based on responses provided by the user or questions asked by the user as the user interacts with the GUI. For example, as the user views the screen shot 851, if the user asks if diabetes is curable, this question provides information about the user such as a level of education of the user.
In
In
Accordingly, the cognitive agent 110 can answer a library of questions and provide content for many questions a user has as it related to diabetes. The information provided for purposes of educating a user is based on an overall health plan of the user, which is based on meta data analysis of interactions with the user, and an analysis of the education level of the user.
Next, the cognitive agent 110 says “Thank you. I look forward to helping you meet your health goals!” (element 910). At this point, the cognitive agent 110 can probe the user for additional data by offering a health assessment survey (e.g., a microsurvey) (element 914). The cognitive agent 110 prompts the user to fill out the health assessment by stating: “To help further personalize your health improvement experience, I would like to start by getting to know you and your health priorities. The assessment will take about 10 minutes. Let's get started!” (element 912).
In
In the screen shot 918, the cognitive agent acknowledges the user's completion of the health assessment survey (element 920) and provides additional resources to the user (element 922). In element 920, the cognitive agent states: “Congrats on taking the first step toward better health! Based upon your interest, I have some recommended health improvement initiatives for you to consider,” and presents the health improvement initiatives. In the example conversational stream, the user gets curious about a particular aspect of his health and states: “While I finished my health assessment, it made me remember that a doctor I saw before moving here told me that my blood sugar test was higher than normal.” (element 924). After receiving the statement in element 924, the cognitive agent 110 treats the statement as an originating question and undergoes an initial round of analysis (and additional rounds of analysis as needed) as described above.
The cognitive agent 110 presents an answer as shown in screen shot 926. For example, the cognitive agent 110 states: “You mentioned in your health assessment that you have been diagnosed with Diabetes, and my health plan can help assure your overall compliance” (element 928). The cognitive agent further adds: “The following provides you a view of our health plan which builds upon your level of understanding as well as additional recommendations to assist in monitoring your blood sugar levels” (element 930). The cognitive agent 110 provides the user with the option to view his Diabetes Health Plan (element 932).
The user responds “That would be great, how do we get started” (element 934). The cognitive agent 110 receives the user's response as another originated question and undergoes an initial round of analysis (and additional rounds of analysis as needed) as described above. In the example screen shot 926, the cognitive agent 110 determines additional information is needed and prompts the user for additional information.
The feed provides an interface where the user accesses a personal log of activities the user is involved in. The personal log is searchable. For example, if the user reads an article recommended by the cognitive agent 110 and highlights passages, the highlighted passages are accessible through the search. Additionally, the cognitive agent 110 can initiate a conversational stream focused on subject matter related to the highlighted passages.
The feed provides an interface to celebrate mini achievements and successes in the user's personal goals (e.g., therapeutic or wellness goals). In the feed, the cognitive agent 110 is still available (ribbon 1204) to help search, guide, or steer the user toward a therapeutic or wellness goal.
Accordingly, the cognitive intelligence platform provides several core features including:
As noted above, the computing device 1400 also includes the storage device 1440, which can comprise a single disk or a collection of disks (e.g., hard drives), and includes a storage management module that manages one or more partitions within the storage device 1440. In some embodiments, storage device 1440 can include flash memory, semiconductor (solid-state) memory or the like. The computing device 1400 can also include a Random-Access Memory (RAM) 1420 and a Read-Only Memory (ROM) 1422. The ROM 1422 can store programs, utilities or processes to be executed in a non-volatile manner. The RAM 1420 can provide volatile data storage, and stores instructions related to the operation of processes and applications executing on the computing device.
In the method as shown in
In response to the user-generated natural language medical information query, the artificial intelligence-based diagnostic conversation agent selects a diagnostic fact variable set relevant to generating a medical advice query answer for the user-generated natural language medical information query by classifying the user-generated natural language medical information query into one of a set of domain-directed medical query classifications associated with respective diagnostic fact variable sets (
In response to the user-specific medical fact variable values, the artificial intelligence-based diagnostic conversation agent generates a medical advice query answer in response to the user-generated natural language medical information query (
In some embodiments, compiling user-specific medical fact variable values (
In some embodiments, compiling user-specific medical fact variable values (
In some embodiments, compiling user-specific medical fact variable values (
In some embodiments, compiling user-specific medical fact variable values (
In some embodiments, generating the medical advice query answer (
In some embodiments, generating the medical advice query answer (
In some embodiments, selecting a diagnostic fact variable set relevant to generating a medical advice query answer for the user-generated natural language medical information query by classifying the user-generated natural language medical information query into one of a set of domain-directed medical query classifications associated with respective diagnostic fact variable sets (
In some embodiments, the method (1500) for answering a user-generated natural language medical information query based on a diagnostic conversational template is implemented as a computer program product in a computer-readable medium.
In some embodiments, the system and method 1500 shown in
In the method as shown in
In response to the user-generated natural language query, the artificial intelligence-based conversation agent selects a fact variable set relevant to generating a query answer for the user-generated natural language query by classifying the user-generated natural language query into one of a set of domain-directed query classifications associated with respective fact variable sets (
In response to the user-specific fact variable values, the artificial intelligence-based conversation agent generates a query answer in response to the user-generated natural language query (
In some embodiments, compiling user-specific fact variable values (
In some embodiments, compiling user-specific fact variable values (
In some embodiments, compiling user-specific fact variable values (
In some embodiments, generating the query answer (
In some embodiments, generating the advice query answer (
In some embodiments, selecting a fact variable set relevant to generating a query answer for the user-generated natural language query by classifying the user-generated natural language query into one of a set of domain-directed query classifications associated with respective fact variable sets (
In some embodiments, the method (1600) for answering a user-generated natural language query based on a conversational template is implemented as a computer program product in a computer-readable medium.
In some embodiments, the system and method shown in
In the cognitive intelligence platform 102, a cognitive agent 110 is configured for receiving a user-generated natural language query at an artificial intelligence-based conversation agent from a user interface on a user device 104 (
A critical thinking engine 108 is configured for, responsive to content of the user-generated natural language query, selecting a fact variable set relevant to generating a query answer for the user-generated natural language query by classifying the user-generated natural language query into one of a set of domain-directed query classifications associated with respective fact variable sets (
Included is a knowledge cloud 106 that compiles user-specific fact variable values for one or more respective fact variables of the fact variable set (
Responsive to the fact variable values, the cognitive agent 110 is further configured for generating the query answer in response to the user-generated natural language query (
In some embodiments, the system and method 1600 shown in
The method 1700 involves receiving a user-generated natural language medical information query from a medical conversational user interface at an artificial intelligence-based medical conversation cognitive agent (block 1702). In some embodiments, receiving a user-generated natural language medical information query from a medical conversational user interface at an artificial intelligence-based medical conversation cognitive agent (block 1702) is performed by a cognitive agent that is a part of the cognitive intelligence platform and is configured for this purpose. In some embodiments, the artificial intelligence-based diagnostic conversation agent is the conversation agent 110 of
The method 1700 further includes extracting a medical question from a user of the medical conversational user interface from the user-generated natural language medical information query (block 1704). In some embodiments, extracting a medical question from a user of the medical conversational user interface from the user-generated natural language medical information query (block 1704) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1700 includes compiling a medical conversation language sample (block 1706). In some embodiments, compiling a medical conversation language sample (block 1706) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1700 involves extracting internal medical concepts and medical data entities from the medical conversation language sample (block 1708). In some embodiments, extracting internal medical concepts and medical data entities from the medical conversation language sample (block 1708) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1700 involves inferring a therapeutic intent of the user from the internal medical concepts and the medical data entities (block 1710). In some embodiments, inferring a therapeutic intent of the user from the internal medical concepts and the medical data entities (block 1710) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1700 includes generating a therapeutic paradigm logical framework 1800 for interpreting of the medical question (block 1712). In some embodiments, generating a therapeutic paradigm logical framework 1800 for interpreting of the medical question (block 1712) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
Each of the medical logical progression paths 1804 can include one or more medical logical linkages 1808 from the medical question 1806 to a therapeutic path-specific answer 1810.
The medical logical linkages 1808 can include the internal medical concepts 1812 and external therapeutic paradigm concepts 1814 derived from a store of medical subject matter ontology data 1816. In some embodiments, the store of subject matter ontology data 1816 is contained in a knowledge cloud. In some embodiments, the knowledge cloud is the knowledge cloud 102 of
The method 1700 shown in
The method 1700 involves answering the medical question by following the likely medical information path to the likely path-dependent medical information answer (block 1716). In some embodiments, answering the medical question by following the likely medical information path to the likely path-dependent medical information answer (block 1716) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1700 can further include relating medical inference groups of the internal medical concepts. In some embodiments, relating medical inference groups of the internal medical concepts is performed by a critical thinking engine further configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
In some embodiments, the method 1700 of
The method 1900 involves receiving a user-generated natural language query at an artificial intelligence-based conversation agent (block 1902). In some embodiments, receiving a user-generated natural language query from a conversational user interface at an artificial intelligence-based conversation cognitive agent (block 1902) is performed by a cognitive agent that is a part of the cognitive intelligence platform and is configured for this purpose. In some embodiments, the artificial intelligence-based conversation agent is the conversation agent 110 of
The method 1900 further includes extracting a question from a user of the conversational user interface from the user-generated natural language query (block 1904). In some embodiments, extracting a question from a user of the conversational user interface from the user-generated natural language query (block 1904) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1900 includes compiling a language sample (block 1906). In some embodiments, compiling a language sample (block 1906) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1900 involves extracting internal concepts and entities from the language sample (block 1908). In some embodiments, extracting internal concepts and entities from the language sample (block 1908) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1900 involves inferring an intent of the user from the internal concepts and the entities (block 1910). In some embodiments, inferring an intent of the user from the internal concepts and the entities (block 1910) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1900 includes generating a logical framework 2000 for interpreting of the question (block 1912). In some embodiments, generating a logical framework 2000 for interpreting of the question (block 1912) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
Each of the paths 2004 can include one or more linkages 2008 from the question 2006 to a path-specific answer 2010.
The linkages 2008 can include the internal concepts 2012 and external concepts 2014 derived from a store of subject matter ontology data 2016. In some embodiments, the store of subject matter ontology data 2016 is contained in a knowledge cloud. In some embodiments, the knowledge cloud is the knowledge cloud 102 of
The method 1900 shown in
The method 1900 involves answering the question by following the likely path to the likely path-dependent answer (block 1916). In some embodiments, answering the question by following the likely path to the likely path-dependent answer (block 1916) is performed by a critical thinking engine configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
The method 1900 can further include relating inference groups of the internal concepts. In some embodiments, relating inference groups of the internal concepts is performed by a critical thinking engine further configured for this purpose. In some embodiments, the critical thinking engine is the critical thinking engine 108 of
In some embodiments, the method 1900 of
In some embodiments, the method 2100 involves receiving segments of a medical information natural language conversation stream at an artificial intelligence-based health information conversation agent from a medical information conversation user interface (block 2102). In some embodiments the user interface is on the mobile device 104 of
In some embodiments, the method 2100 further involves defining a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user medical information profile in response to medical information content of a user medical information profile associated with the medical information natural language conversation stream (block 2104). In some embodiments, defining a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user medical information profile in response to medical information content of a user medical information profile associated with the medical information natural language conversation stream (block 2104) is performed on a processor of a computer. In some embodiments, defining a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user medical information profile in response to medical information content of a user medical information profile associated with the medical information natural language conversation stream (block 2104) is performed by a critical thinking engine configured for this purpose.
In some embodiments, defining a desired clinical management outcome objective relevant to health management criteria and related health management data attributes of the user medical information profile in response to medical information content of a user medical information profile associated with the medical information natural language conversation stream (block 2104) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2100 further involves identifying a set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective (block 2106). In some embodiments, identifying a set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective (block 2106) is performed on a processor of a computer. In some embodiments, identifying a set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective (block 2106) is performed by a critical thinking engine configured for this purpose. In some embodiments, identifying a set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective (block 2106) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2100 further involves selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108). In some embodiments, selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108) is based on a set of factors including the likelihood of patient compliance with the a recommendation for the a medical intervention and a statistical likelihood that the action will materially advance the clinical management outcome objective. In some embodiments, selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108) is based on a set of factors comprising likelihood total expected cost expectation associated with the recommendation for the a medical intervention likely to advance the clinical management outcome objective. In some embodiments, selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108) is performed on a processor of a computer. In some embodiments, selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108) is performed by a critical thinking engine configured for this purpose. In some embodiments, selecting from among the set of potential therapeutic interventions correlated to advancement of the clinical management outcome objective a medical intervention likely to advance the clinical management outcome objective (block 2108) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2100 further involves presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110). In some embodiments, the stimulation can be a motivation. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) includes presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a cost-benefit analysis comparing likely results of performance of the action likely to advance the clinical management outcome objective and likely results of non-performance of the action likely to advance the clinical management outcome objective. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) includes presenting to the user in the medical information natural language conversation stream a conversation stream reinforcing the recommendation after expiration of a delay period. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) includes presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining reasons for selection of the clinical management outcome objective. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) includes notifying third party service providers of the clinical management outcome objective and the recommendation. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) is performed on a processor of a computer. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) is performed by a cognitive agent configured for this purpose. In some embodiments, presenting in the medical information natural language conversation stream a therapeutic advice conversation stream segment designed to stimulate execution of the medical intervention likely to advance the clinical management outcome objective (block 2110) is Steps 7 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2100 further involves presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a correlation between the medical intervention likely to advance the clinical management outcome objective and achievement of the clinical management outcome objective (block 2112). In some embodiments, presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a correlation between the medical intervention likely to advance the clinical management outcome objective and achievement of the clinical management outcome objective (block 2112) is performed on a processor of a computer. In some embodiments, presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a correlation between the medical intervention likely to advance the clinical management outcome objective and achievement of the clinical management outcome objective (block 2112) is performed by a critical thinking engine configured for this purpose. In some embodiments, presenting to the user in the medical information natural language conversation stream a therapeutic advice conversation stream segment explaining a correlation between the medical intervention likely to advance the clinical management outcome objective and achievement of the clinical management outcome objective (block 2112) is Steps 7 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2200 involves receiving segments of a natural language conversation stream at an artificial intelligence-based health information conversation agent from a conversation user interface (block 2202). In some embodiments the user interface is on the mobile device 104 of
In some embodiments, the method 2200 further involves defining a desired user outcome objective relevant to health management criteria and related health management data attributes of the user profile in response to content of a user profile associated with the natural language conversation stream (block 2204). In some embodiments, defining a desired user outcome objective relevant to health management criteria and related health management data attributes of the user profile in response to content of a user profile associated with the natural language conversation stream (block 2204) is performed on a processor of a computer. In some embodiments, defining a desired user outcome objective relevant to health management criteria and related health management data attributes of the user profile in response to content of a user profile associated with the natural language conversation stream (block 2204) is performed by a critical thinking engine configured for this purpose.
In some embodiments, defining a desired user outcome objective relevant to health management criteria and related health management data attributes of the user profile in response to content of a user profile associated with the natural language conversation stream (block 2204) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2200 further involves identifying a set of potential actions correlated to advancement of the user outcome objective (block 2206). In some embodiments, identifying a set of potential actions correlated to advancement of the user outcome objective (block 2206) is performed on a processor of a computer. In some embodiments, identifying a set of potential actions correlated to advancement of the user outcome objective (block 2206) is performed by a critical thinking engine configured for this purpose. In some embodiments, identifying a set of potential actions correlated to advancement of the user outcome objective (block 2206) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2200 further involves selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208). In some embodiments, selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208) is based on a set of factors including the likelihood of patient compliance with the a recommendation for the an action and a statistical likelihood that the action will materially advance the user outcome objective. In some embodiments, selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208) is based on a set of factors comprising likelihood total expected cost expectation associated with the recommendation for the an action likely to advance the user outcome objective. In some embodiments, selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208) is performed on a processor of a computer. In some embodiments, selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208) is performed by a critical thinking engine configured for this purpose. In some embodiments, selecting from among the set of potential actions correlated to advancement of the user outcome objective an action likely to advance the user outcome objective (block 2208) is accomplished through one or more of Steps 2-6 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2200 further involves presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210). In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) includes presenting to the user in the natural language conversation stream a conversation stream segment explaining a cost-benefit analysis comparing likely results of performance of the action likely to advance the user outcome objective and likely results of non-performance of the action likely to advance the user outcome objective. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) includes presenting to the user in the natural language conversation stream a conversation stream reinforcing the recommendation after expiration of a delay period. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) includes presenting to the user in the natural language conversation stream a conversation stream segment explaining reasons for selection of the user outcome objective. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) includes notifying third party service providers of the user outcome objective and the recommendation. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) is performed on a processor of a computer. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) is performed by a cognitive agent configured for this purpose. In some embodiments, presenting in the natural language conversation stream a conversation stream segment designed to motivate performance of the action likely to advance the user outcome objective (block 2210) is Steps 7 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
In some embodiments, the method 2200 further involves presenting to the user in the natural language conversation stream a conversation stream segment explaining a correlation between the action likely to advance the user outcome objective and achievement of the user outcome objective (block 2212). In some embodiments, presenting to the user in the natural language conversation stream a conversation stream segment explaining a correlation between the action likely to advance the user outcome objective and achievement of the user outcome objective (block 2212) is performed on a processor of a computer. In some embodiments, presenting to the user in the natural language conversation stream a conversation stream segment explaining a correlation between the action likely to advance the user outcome objective and achievement of the user outcome objective (block 2212) is performed by a critical thinking engine configured for this purpose. In some embodiments, presenting to the user in the natural language conversation stream a conversation stream segment explaining a correlation between the action likely to advance the user outcome objective and achievement of the user outcome objective (block 2212) is Steps 7 as earlier discussed in the context of “Analyzing Conversational Context As Part of Conversational Analysis”.
Other organizations 2302 may be associated with nodes 116 representing services provided by the distributed hyperledger fabric network 2300. For example, organization 2302-4 is associated with an ordering node 116-4 that ensures that the one or more rules implemented by each of the nodes 116-1, 116-2, and/or 116-3 involved in a transaction are satisfied and/or there is consensus among the nodes 116-1, 116-2, and/or 116-3 prior to approving performance of the transaction and addition of the transaction into the hyperledger 118. Using the ordering node 116-4 enhances consistency and security of the hyperledger 118 by controlling what is allowed to be added to the hyperledger 118.
Each node 116 may implement various rules 2306 which may be installed into the hyperledger 118. In some embodiments, the rules 2306 may be included in each respective copy of the hyperledger 118 that is distributed between the various nodes 116. In some embodiments, a hyperledger 118 on one node (e.g., 116-1) may have a first subset of the rules 2306 installed and another node (e.g., 116-2) may have a second subset of the rules 2306 installed where at least one rule in the first subset is different than a rule in the second subset. The rules 2306 may be implemented as computer executable instructions (e.g., software modules).
The rules 2306 may be self-executing at certain frequencies. For example, after a period of time expires, a rule 2306 may determine whether certain information (e.g., authorizing credential) of an entity (e.g., medical personnel entity) registered as a node 116 needs to be updated in the hyperledger 118 and provide a notification to a computing device 2310 used by that entity. In other embodiments, the rules 2306 may be self-executing based on certain conditions occurring. For example, when an authorizing credential of an entity expires in the hyperledger 118, the rule 2306 may trigger a notification to be sent to the computing device 2310 of that entity. In other instances, the rules 2306-2 may be triggered when a request to perform a transaction on the hyperledger 118 is received.
In general, the rules 2306 may specify when updates to the hyperledger 118 are to be provided. The rules 2306 may be analytics-based in that they monitor states or conditions of information in the hyperledger 118, the computing devices 2310, and/or the nodes 116, and determine when hyperledger 118 updates should be provided. For example, the rules 2306 may specify that updates to the hyperledger 118 are to be provided based on any combination of geofencing (e.g., geolocation of the computing devices 2310 associated with particular nodes 116), state rules for when to dispense product like lenses, controlled substances, or other medication, adherence to dispensing guidelines, number of refills allowed for a prescription, authorizing credentials of medical personnel being valid, and so forth.
Each organization 2322 may include a computing device 2310 used by an entity associated with that organization 2322. The computing device 2310 may include one or more memories, processors, and/or network interfaces. The computing device 2310 may be similar to any computing device described with respect to
Each organization 2302 may include a membership service provider (MSP) 2304 that is responsible for issuing identities and authenticating credentials 2312 to computing devices 2310 associated with entities. As described herein, when a computing device 2308 requests to perform a transaction, such as registering as a node 116 on the distributed hyperledger fabric network 2300, the computing device 2310 may provide certain information pertaining to the entity. For example, for a medical personnel entity the information may include at least an identity of the medical personnel entity, an authorizing credential, a date the authorizing credential was last updated, an address of a place of work of the medical personnel entity, gender, race, and so forth. For a patient entity, the information may include at least the patient's identity, social security number, driver's license number, address, medical records, allergies, medicine allergies, familial medical history, and so forth.
The nodes 116 may communicate with each other to determine if a consensus is reached as to whether to allow the transaction to be allowed. Further, one or more of the rules 2306 may be applied to determine whether to allow the transaction to be performed. When the consensus protocol and/or rules 2306 are satisfied, the ordering node 116 may order the transaction to be performed and a record of the transaction is added to the hyperledger 118.
Each block 2400 includes a signature 2406 and one or more transactions 2408. The signature 2406 may be the identity of the entity that requested the transaction to be performed. In some embodiments, a block 2400 storing transactions 2408 may be added to the hyperledger 118 after it is determined that the one or more rules 2306 and/or consensus between the nodes 116 are satisfied. The transactions 2408 in a given request may be grouped and added as a block 2400 to the hyperledger 118, or different transactions from different requests may be grouped and added as a block 2400 if the transactions are related or involve particular nodes 116.
In some embodiments, the transactions 2408 relating to registering an entity may store identifying information pertaining to an entity, such as an identity, address, social security number, driver's license number, and so forth. These transactions 2408 may store authorizing credentials, such as license numbers (NPIs) for physicians, license numbers for pharmacists, license numbers for a pharmacies to dispense medicine, and so forth.
The transactions 2408 relating to medical transactions may store documents created during a consultation between a patient entity and a medical personnel entity. For example, the transactions 2408 may store a prescription (e.g., prescription ID, type of medicine, directions for taking the medicine, number of refills, date of prescription, etc.) written for a patient by a physician, doctor notes, updated medical record with treatment administered (e.g., vaccination shot, strep test, etc.), diagnosis, and so forth. Such transactions may include information identifying the patient (e.g., patient ID, name, social security number, etc.), as well as information pertaining to the medical personnel involved (e.g., name, physician ID, authorizing credential, location of place of work of the physician, etc.).
The transactions 2408 relating to medical transactions may also store records of what transpired during a transaction 2408. For example, a medical transaction may pertain to a patient requesting a prescription be filled and/or medicine dispensed at a pharmacy. If the medical transaction is determined to be performed based on the one or more rules 2306 and/or the consensus protocol being satisfied, then the medical transaction may be performed and the prescription is filled/medicine is dispensed. The record of the medical transaction may be stored in a block 2400 in the hyperledger and include information indicating that the prescription written by a particular physician was filled for a particular patient entity at a particular pharmacy by a particular pharmacist and a particular medicine was dispensed.
In one scenario, the rules 2306 may specify that the authorizing credential of a node 2500-1 representing medical personnel entity has to be updated every X period of time (2501-1) in the hyperledger 118. For example, a physician's 2502 medical license has to be updated every 3 years, and a pharmacist's 2504 license has to be updated every 5 years. The rules 2306 may analyze the information pertaining to the medical personnel 2500-1 in the transactions 2408 stored in the hyperledger 118 and may determine that the period of time for updating the authorizing credential has expired or is about to expire. As a result, the node executing the rules 2306 may cause a notification to be presented on the computing device 2310 used by the medical personnel that instructs the medical personnel to update their authorizing credential. The updated authorizing credentials may be stored on the hyperledger 118.
In one scenario, the rules 2306 may specify that the hyperledger 118 is updated when a medical transaction, such as when the prescription is written (2501-2) by a physician 2504 at a physician's office/clinic 2500-2, is performed. The rules 2306 may determine whether the physician and/or the physician's office/clinic 2500-2 have valid authorizing credentials to allow the prescription to be written and the medical transaction to be stored on the hyperledger 118. The prescription written by the physician 2504 for the patient may be stored on the hyperledger 118.
In one scenario, the rules 2306 may specify that the hyperledger 118 is updated when a medical transaction, such as when the prescription is filled/medicine is dispensed (2501-3) by a pharmacy/optical/behavioral facility 2500-3, is performed. The rules 2306 may determine whether the pharmacist 2504, optometrist, psychiatrist, etc. and/or the pharmacy/optical/behavioral facility 2500-3 have valid authorizing credentials to allow the prescription to be filled/the medicine to be dispensed. A record of the prescription being filled/medicine being dispensed by a pharmacist for the patient at a pharmacy may be stored on the hyperledger 118. In addition, if an insurance provider entity paid for some or all of the cost of the medicine, a record of information pertaining to the insurance provider's involvement may be stored on the hyperledger 118.
In one scenario, the rules 2306 may specify that the hyperledger 118 is updated when a medical transaction, such as when a patient 2500-3 meets with medical personnel 2500-1, is performed. The rules 2306 may determine whether a geolocation of a computing device of the patient 2500-3 and/or a geolocation of the computing device of the medical personnel are within a threshold distance from each other (e.g., using geofencing), and the rules may determine when the pharmacist 2504, optometrist, psychiatrist, etc. and/or the pharmacy/optical/behavioral facility 2500-3 have valid authorizing credentials to allow the prescription to be filled/the medicine to be dispensed. A record of the meeting between the patient and the medical personnel, including doctor's notes, prescriptions, diagnosis, location of meeting, identity of the medical personnel, identity of the patient, and so forth may be stored on the hyperledger 118.
In some embodiments, the method 2600 may involve receiving (2602), from a computing device 2310-1, a request to perform a transaction 2408-1 using the hyperledger 118, where the transaction 2408-1 pertains to registering a medical personnel entity associated with the computing device 2310-1 as a node 116-1 of the one or more nodes. The one or more nodes may represent a set of entities in a healthcare ecosystem, where the set of entities include one or more medical personnel, patients, medical facilities, insurance providers, professional associations, and government agencies. The request may include an authorizing credential (e.g., medical license number, national provider identifier (NPI)) and other information (e.g., name, specialty of practice, years of practice, address, location of work, gender, age, race, languages spoken, etc.) pertaining to the medical personnel entity.
The method 2600 may also involve determining (2604), based on one or more rules 2306, whether to allow the transaction 2408-1 to be performed in view of the request. The rules 2306 may specify when updates to the hyperledger 118 are to be provided and/or when to allow a transaction to be performed that updates the hyperledger 118. The rules 2306 may be analytics-based in that they monitor states or conditions of information in the hyperledger 118, the computing devices 2310, and/or the nodes 116, and determine when hyperledger 118 updates should be provided by the medical personnel entities using the computing devices 2310. For example, the rules 2306 may specify that updates to the hyperledger 118 are to be provided based on any combination of geofencing (e.g., geolocation of the computing devices 2310 associated with particular nodes 116), state rules for when to dispense product like lenses, controlled substances, or other medication, adherence to dispensing guidelines, number of refills allowed for a prescription, authorizing credentials of medical personnel being valid, and so forth. In one example, the rules 2306 may specify allowing the medical personnel entity to register as a node 116-1 if the medical personnel entity is unique (no other node includes identical information as the medical personnel entity) and/or the other nodes in the distributed hyperledger fabric network 2300 agree to allow the node in the hyperledger fabric network 2300.
In some embodiments, determining, based on the one or more rules 2306, whether to allow the transaction 2408-1 to be performed in view of the request may include transmitting a request to a computing device of a professional association or a government agency associated with the authorizing credential to validate the authorizing credential. If the professional association or government agency verifies that the authorizing credential is valid and issued to the medical personnel entity requesting registration, then the transaction may be allowed to be performed. If the professional association or government agency indicates that the authorizing credential is invalid, then the registration may be denied and the medical personnel entity may be notified to update their authorizing credential (assuming they ever had a valid authorizing credential issued to them).
Responsive to determining to allow the transaction 2408-1 to be performed, the method 2600 may also involve registering (2606) the node 116-1 for the medical personnel entity by associating (2608) the medical personnel entity with the node 116-1, updating (2610) the hyperledger 118 by adding a block 2400-1 to the hyperledger 118, where the block 2400-1 stores the transaction 2408-1 including the authorizing credential pertaining to the medical personnel entity, and storing (2612) the hyperledger 118 at the node 116-1 for the medical personnel entity.
In some embodiments, the method 2600 may also include receiving, from a computing device 2310-1 associated with the medical personnel entity, a request to perform a medical transaction 2408-4 between the medical personnel entity and a patient entity, where the patient entity is associated with a patient node 116-2, and a copy of the hyperledger 118 is stored at the patient node 116-2. The medical transaction 2408-4 may include at least one of a medical personnel entity creating or updating a prescription for the patient entity or the medical personnel entity filling or refilling the prescription for the patient entity. The method 2600 may include identifying the block 2400-1 in the hyperledger 118 storing the transaction 2408-1 including the authorizing credential pertaining to the medical personnel entity, and determining whether to allow the medical transaction 2408-4 to be performed based on at least the authorizing credential of the medical personnel entity. Responsive to determining to allow the medical transaction 2408-4 to be performed, the method 2600 may include updating the hyperledger 118 with the medical transaction 2408-4. For example, a block 2400-3 may be added to the hyperledger 118 that stores the medical transaction 2408-4 or the medical transaction 2408-4 may be grouped with another transaction and stored on the same block with the another transaction.
In some embodiments, the method 2700 may involve receiving (2702), from a second computing device 2310-2, a request to perform a second transaction 2408-2 using the hyperledger 118, where the second transaction 2408-2 pertains to registering a patient entity associated with the second computing device 2310-2 as a second node 116-2 of the one or more nodes. The second request includes personal information pertaining to the patient entity.
The method 2700 may also involve determining (2704), based on one or more rules 2306, whether to allow the second transaction 2408-2 to be performed in view of the second request. The rules 2306 may specify when updates to the hyperledger 118 are to be provided and/or when to allow a transaction to be performed that updates the hyperledger 118. In one example, the rules 2306 may specify allowing the medical personnel entity to register as a node 116-1 if the medical personnel entity is unique (no other node includes identical information as the medical personnel entity) and/or the other nodes in the distributed hyperledger fabric network 2300 agree to allow the node 116-2 in the hyperledger fabric network 2300.
Responsive to determining to allow the second transaction 2408-3 to be performed, the method 2700 may also involve registering (2706) the second node 116-2 for the patient entity by associating (2708) the patient entity with the second node 116-2, updating (2710) the hyperledger 118 by adding a second block 2400-2 to the hyperledger 118, where the second block 2400-2 stores the second transaction 2408-3 including the personal information pertaining to the patient entity, and storing (2612) the hyperledger 118 at the second node 116-2 for the patient entity.
A computing device of the pharmacy/optical/behavioral entity 2500-3 may send (2802) the request to perform a medical transaction to the hyperledger 118 stored on the nodes 116 of the cognitive intelligence platform 102. Authenticating credentials of the pharmacy/optical/behavioral entity 2500-3, as well as the authenticating credentials of the patient entity 2500-4 may be sent to the hyperledger 118. The request may trigger one or more rules 2306 to execute on one or more nodes 116. One rule 2306 may determine whether the authenticating credentials of the patient entity 2500-4 are verified for the identity of the patient entity 2500-4 making the request. Another rule 2306 may determine whether the authenticating credentials of the pharmacy/optical/behavioral entity 2500-3 are verified for the identity of the pharmacy/optical/behavioral entity 2500-3 making the request. Another rule 2306 may determine whether the pharmacy/optical/behavioral entity 2500-3 has a valid authorizing credential stored in the hyperledger 118. Another rule 2306 may determine whether the pharmacist or optometrist that is filling the prescription or dispensing the lenses has a valid authorizing credential in the hyperledger 118. Another rule 2306 may determine whether the physician who wrote the prescription requested to be filled has a valid authorizing credential at the time the prescription was written and/or when the request to fill the prescription is made using the hyperledger 118. Another rule 2306 may determine whether there are any refills remaining for the prescription written by a physician for the patient entity 2500-4 in the hyperledger 118. Another rule 2306 may determine whether the prescription has expired in the hyperledger 118.
If the various rules 2306 are satisfied, the medical transaction may be permitted to be performed and a record of the medical transaction may be stored in the hyperledger 118. In some embodiments, prior to allowing the medical transaction to be performed, the nodes may communicate with each other to form a consensus before the medical transaction is allowed to be performed and the hyperledger 118 is updated. Once the medical transaction is approved, a notification may be sent (2806) to the pharmacy/optical/behavioral entity 2500-3 indicating the same. The pharmacy/optical/behavioral entity 2500-3 may perform the approved medical transaction by filling the prescription and/or dispensing medicine/lenses. The pharmacy/optical/behavioral entity 2500-3 may transmit a notification that is presented on an application executing on the computing device associated with the patient entity 2500-4. The notification may indicate that the prescription has been filled and/or the medicine/lenses are dispensed.
In some embodiments, the method 2900 may involve receiving (2902), from a computing device 2310-1 associated with a medical personnel entity, a request to perform the medical transaction 2408-4 between the medical personnel entity and a patient entity, where the medical personnel entity is associated with a medical personnel node 116-1 and the patient entity is associated with a patient node 116-2. The medical personnel node 116-1 and the patient node 116-2 may be included in the distributed network of nodes 2300 representing entities in a healthcare ecosystem. A respective copy of the hyperledger 118 may be maintained at both the medical personnel node 116-1 and the patient node 116-2. The medical transaction may include the medical personnel entity creating or updating a prescription for the patient entity.
Responsive to determining to allow the medical transaction 2408-4 to be performed, the method 2900 may also involve updating (2908) the respective copy of the hyperledger 118 with the medical transaction 2408-4 at the medical personnel node 116-1 and the patient node 116-2. For example, the one or more rules 2306 may be satisfied and a consensus may be reached between nodes 116, thereby resulting in the ordering node 116-4 ordering the medical transaction 2408-4 to be added to the hyperledger 118.
In some embodiments, the method 2900 may also involve (2904) identifying a transaction 2408-1 in the hyperledger 118 that stores an authorizing credential of the medical personnel entity. The method 2900 may also involve determining (2906) whether to allow the medical transaction to 2408-4 be performed based on at least the authorizing credential of the medical personnel entity. In some embodiments, the method 2900 may include determining whether to allow the medical transaction 2408-4 to be performed based on at least the authorizing credential of the medical personnel entity further includes determining whether the authorizing credential is valid (e.g., has not expired). In some embodiments, the method 2900 may include determining that the authorizing credential of the medical personnel entity is expired or is about to expire, and transmitting, based on one or more rules 2306, a notification to the computing device 2310-1 associated with the medical personnel entity that instructs the medical personnel entity to renew the authorizing credential.
In some embodiments, the method 2900 may include identifying another transaction 2408-3 in the hyperledger 118 that stores information pertaining to the patient entity. The method 2900 may include determining whether to allow the medical transaction 2408-4 to be performed based on at least the authorizing credential of the medical personnel entity and the information (e.g., identity, allergies, medicine allergies, medical history, familial medical history, age, gender, race, etc.) pertaining to the patient entity. Responsive to determining to allow the medical transaction 2408-4 to be performed, the method 2900 may include updating the respective copy of the hyperledger 118 with the medical transaction 2408-4 at the medical personnel node 116-1 and the patient node 116-2.
In some embodiments, the method 3000 may involve identifying (3002), by a patient computing device 104, a medical facility computing device that is within a threshold distance of a geolocation of the patient computing device 104, where the medical facility computing device is associated with a medical facility entity and the patient computing device is associated with a patient entity. In some embodiments, the geolocation for the computing devices may be determined using the IP address of each device. The threshold distance may be any suitable distance (e.g., 20 feet, 50 feet, 100 feet, 200 feet, 1000 feet, a mile, etc.) that enables distinguishing a closer medical facility entity from a farther away medical facility entity. The medical transaction may be filling or refilling a prescription for the patient entity. The medical facility entity may be a pharmacy or optometrist.
The method 3000 may involve transmitting (3004), from the patient computing device 104, a request to perform a medical transaction 2408-5 using the hyperledger 118 to the medical facility computing device, where the request includes an authenticating credential associated with the patient entity. The authenticating credential may have been provided to the patient computing device 104 when the patient entity registered as a node 116-2 in the distributed hyperledger fabric network 2300.
A determination may be made whether to allow performance of the medical transaction 2408-5 based on one or more rules 2306 that specify allowing the medical transaction 2408-5 to be added to the hyperledger 118 when at least the authenticating credential in the hyperledger 118 is verified. Other rules 2306 may specify allowing the medical transaction 2408-5 when the authenticating credentials of the medical personnel entity involved in the medical transaction 2408-5 are verified for the identity of the medical personnel entity in the hyperledger 118, when the authorizing credential stored in the hyperledger 118 are verified for the medical personnel entity, when the authorizing credential of the pharmacist or optometrist that is filling the prescription or dispensing the lenses is verified in the hyperledger 118, when there are refills remaining for the prescription written by a physician for the patient entity in the hyperledger 118, when the prescription has not expired in the hyperledger 118, and so forth.
In some embodiments, the rules 2306 may specify notifying the medical personnel entity to update the authorizing credential in the hyperledger at set intervals of time (e.g., every 3 years for a physician). The rules 2306 may specify allowing the medical transaction 2408-5 to be added to the hyperledger 118 when at least an authorizing credential of the medical facility entity in the hyperledger 118 is verified, where the medical facility entity is a pharmacy. The rules 2306 may specify allowing the medical transaction 2408-5 to be added to the hyperledger 118 when the prescription for the patient entity is identified at another medical transaction 2408-4 in the hyperledger 118, and/or when there is at least one remaining refill for the prescription identified at the another medical transaction 2408-4 in the hyperledger 118.
The method 3000 may involve receiving (3006) a notification from the medical facility computing device that indicates the medical transaction 2408-4 has been performed when the rules 2306 are satisfied and/or a consensus is reached between nodes 116 in the distributed hyperledger fabric network 2300. The hyperledger 118 may be updated with the addition of a block including transaction 2408-5 or the transaction 2408-5 may be grouped with another medical transaction (e.g., 2408-4). For example, the medical transactions 2408-4 and 2408-5 may be grouped based on the same patient entity and/or medical personnel entity being involved in the medical transactions 2408-4 and 2408-5.
The various aspects, embodiments, implementations or features of the described embodiments can be used separately or in any combination. Various aspects of the described embodiments can be implemented by software, hardware or a combination of hardware and software. The described embodiments can also be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data which can thereafter be read by a computer system. Examples of the computer readable medium include read-only memory, random-access memory, CD-ROMs, DVDs, magnetic tape, hard disk drives, solid-state drives, and optical data storage devices. The computer readable medium can also be distributed over network-coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
Consistent with the above disclosure, the examples of systems and method enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.
The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.
The above discussion is meant to be illustrative of the principles and various embodiments of the present disclosure. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.
This application is a continuation of U.S. application Ser. No. 17/611,476 filed Nov. 15, 2021 and titled System and Method For Using a Blockchain To Manage Medical Transactions Between Entities In A Healthcare Ecosystem,” which is a U.S. National Phase Entry of PCT Application Serial No. PCT/US2020/033284 filed May 15, 2020 and titled “System and Method for Using a Blockchain to Manage Medical Transactions Between Entities in a Healthcare Ecosystem.” The PCT application is a continuation-in-part of U.S. application Ser. No. 16/593,491 filed Oct. 4, 2019, which issued as U.S. Pat. No. 11,263,405 on Mar. 1, 2022. The PCT application also claims priority to and the benefit of U.S. Provisional Application Ser. No. 62/849,075 filed May 16, 2019. All of the above listed applications are incorporated by reference herein as if reproduced in full below.
| Number | Date | Country | |
|---|---|---|---|
| 62849075 | May 2019 | US |
| Number | Date | Country | |
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| Parent | 17611476 | Nov 2021 | US |
| Child | 18670458 | US |
| Number | Date | Country | |
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| Parent | 16593491 | Oct 2019 | US |
| Child | 17611476 | US |
