Patent Application
20230253124
In general, the present invention relates to the field of software systems used in TeleHealth, health IT, patient education, counseling, etc.
Software systems powered by computational linguistic algorithms are engaging users more meaningfully. Thus, enabling conversational information retrieval using interactive user interfaces (such as chatbots, voicebots, web and mobile apps, etc.). Such systems have gained popularity in healthcare where users are increasingly using such conversational tools. A few examples where such systems are used are for educating users about medical topics, for an explanation of medical documents, for users to provide information to their providers, and payors, during TeleHealth consults, and in other health IT applications. Generally, conversational systems are coupled to a backend database from which information is retrieved and presented to the user, powered by semantic mapping, keyword search, etc., to understand the user's input. However, several scenarios are not efficiently handled by conversational systems. Scenarios such as, when a user's input is complex or when a sensitive medical topic is encountered, such scenarios should be answered by health experts. A few examples of such questions are “Am I going to get cancer?” or “I am having side effects; can I take another medication?” etc. Additionally, users will echo the same question in a variety of different ways. In such cases, the conversational system is unable to process the user's request further. This leaves the user confused and additionally the user's query is not answered. Several present-day automated conversational systems functions in this manner and even fewer conversational systems are trained to understand medical information.
This method describes the process of understanding medically relevant user inputs and providing a complete resolution to a scenario in the healthcare domain by providing automatic continuity of the conversation by engaging the user with health experts, additionally providing health experts functionality to restore user and system conversation.
The field of computational linguistics has evolved significantly in the past decade. Computer software-based systems are leveraging this advancement, especially in healthcare. Such systems use artificial intelligence (AI) based conversationality, which is designed to provide information to users and collect information from users. Such systems use an AI core and natural language processing (NLP), deep learning, and language understanding/processing algorithms to accomplish their aims. Conversational AI systems designed specifically for the healthcare domain are lesser in number as compared to other domains.
A practical problem that is often encountered in conversational systems used for TeleHealth consults and in other health IT applications is bringing complete resolution to the user's questions. In the absence of a resolution, users abandon their session leading to undesired outcomes.
Our invention solves this problem by providing accurate and complete information to the user by automatically forwarding certain scenarios to human experts, encountered in TeleHealth and other health applications. This method enables the continuity of conversation between a user, conversational system, and health experts, for scenarios when the conversational system is unable to provide a resolution to the user's medical conversations. The embodiments of this invention implement methods to intelligently understand the user's input, determine if accurate responses are available, and in the absence of which, automatically transfer the conversation to health experts (human agents). Scenarios where this invention is valuable, are when the users ask complex questions, when the questions indicate medical severity and complexity or when answers are unavailable, or when a question should be specifically handled by a health expert.
As medical conversations contain a plethora of medical (technical) terms, the meaning of input must be carefully understood, as even a slight change in the sequence of words may change the meaning of the input, thus leading to an unresolved scenario.
In our method, a Natural Language Processing process starts by analyzing the users' inputs by identifying the sentiment and underlying semantics. Such inputs are then validated for accuracy and a severity/complexity threshold score is generated. If the threshold score is above an arbitrary value, then a database match is executed. The database used is a medical scenario database which will help determine further processing of the user's input.
This medical scenario database comprises clusters of medical scenarios that should be handled by a health expert. If the user's input is mapped to a cluster with a pre-defined level of confidence that indicates the need for health expert intervention, then the conversation is automatically transferred to the next available health expert. The user continues their conversation with the health expert, which when terminated is redirected back to the automated system. Additionally, the health expert may also restore the conversation between the user and the system.
Multiple methods are used to build the medical scenario database cluster by analyzing a plurality of medical information from a variety of sources. By applying techniques such as medical text analytics, ontologies, etc., we identify medical topics such as severity, complexity, certain diagnosis types, treatment options, immediate support, mental health issues, etc., that require health expert(human) intervention. This method uses the intellectual property (U.S. Ser. No. 10/754,882B2) granted to the authors of this patent, which enables converting any medical document into a knowledgegraph using a machine-assisted approach. This approach greatly provides the functionality to ingest and analyze any medical document for downstream processing such as creating this medical scenario database.
In summary, the invention enables automated conversation continuation between a user, system, and health experts. This leads to a complete resolution of users' medical questions increasing user/patient satisfaction.
Our invention finds applicability in medical conversational systems. Such systems commonly use chatbots, voicebots, mobile and web apps, etc., for user interactions. Several user scenarios cannot be handled by such systems or have a medical complexity/severity associated with, that which should be handled by health experts. Our invention solves this problem by enabling continuity of conversation between a user, conversational system, and health experts.
The systems defined in the patent by Henry (US20190012390A1) refer to providing a live agent with more information about a conversation and the patent assigned to Conway (U.S. Ser. No. 10/194,029B2) is for analyzing data from online forums using computational linguistics. A patent granted to Nishant (July 2017) describes querying disconnected websites using conversational approaches and patent issues to Whitecotten in (U.S. Ser. No. 10/694,038B2) describe routing voice calls using context analysis in call centers. A patent was issued to Gholap et at (U.S. Ser. No. 10/754,882B2), also the authors of this patent describe a method that converts any medical document into a knowledgegraph. Such is used to generate the medical scenario database cluster.
The following table lists other such references
In summary, as of this writing, no prior art has been identified that performs continuation of medical conversations between a user, conversational system, and health experts.
