20 results about "Referral system" patented technology

The invention discloses a hierarchical chronic disease management and referral system based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the system comprises the steps: setting upstream and downstream referral paths composed of diagnosis and treatment institutions, binding responsible doctors corresponding to chronic diseases for the diagnosis and treatment institutions of each level, and forming a referral relation mapping table; predicting a chronic disease dynamic risk based on the multi-source and multi-modal data of the target crowd, classifying the target crowd, generating a chronic disease management suggestion for each type of crowd, and pushing the chronic disease management suggestion to a chronic disease doctor for contract signing management; generating referral suggestions based on the chronic disease dynamic risk changes of each type of people and pushing the referral suggestions to chronic disease doctors, and the chronic disease doctors determining the referral suggestions, setting referral information based on upstream and downstream referral paths and a referral relation mapping table and synchronously notifying target diagnosis and treatment institutions and patients; performance excitation is performed on whether a chronic disease doctor completes contract signing of a chronic disease patient, implementation of graded referral treatment and medical cost saving, so that the medical service precision can be improved, the diagnosis and treatment efficiency is improved, and medical cost throttling is promoted.

An embodiment herein provides a system and method for generating hyper-personalized care pathways. The system includes a data ingestion module configured to receive and process computer executable data from one or more data sources. The system includes a profile generation module operatively coupled to the data ingestion module. The profile generation module is configured to synthesize the computer executable data into a hyper-personalized computer executable profile by applying a machine learning algorithm and predictive analytics. The profile generation module is configured to generate a multi-dimensional computer executable representation of current health status and predicted future healthcare needs of the subject. The system includes a pathway generator module that is configured to determine a set of next best actions for the subject based on the hyper-personalized computer executable profile.

Systems, methods, and computer products for providing referrals are disclosed. A user accesses a referral service website or application and creates a user account. The user is verified to provide referrals. The verification is sent to a communications unit, which sends a message to the user requesting the user complete a single step to provide a new referral for a business for a category of goods or services wherein the single step comprises providing a response selected from not more than two available response options. The user account is updated to include the new referral.

An automated referral system and method for generating and managing referrals. The system receives user criteria and identifies relevant search results. It then generates and transmits referral requests to recipients corresponding to the search results. The system performs two-factor authentication before transmitting requests. Read receipts are received from recipients acknowledging the referral requests. The system then displays a subset of the search results to the user, based on which recipients have acknowledged the requests via read receipts. The display may include an interactive map showing locations of the results. This method enhances reliability and user trust in the referral process by incorporating confirmation steps and secure authentication while providing a user-friendly interface for reviewing curated search results.

The present invention aims to provide a program that allows each user to easily identify candidates who meet their desired referral criteria (candidate criteria). [Solution] The program of the present invention causes a referral system comprising at least a memory unit and a processing unit to execute a personnel memory function that stores user information, candidate information, and referrer information in the memory unit; a condition memory function that stores candidate conditions desired by one user, which are included in the user information of each user, in the memory unit; and a specification function that, if a candidate who meets the candidate conditions exists and a referrer who can introduce that candidate has a connection with the one user, identifies one or more such candidates as one or more suitable candidates to introduce to the one user.

The present invention discloses an artificial intelligence-based hierarchical chronic disease management and referral system, which belongs to the field of smart medical technology and comprises: setting upstream and downstream referral paths composed of diagnosis and treatment institutions, and binding the responsible doctors corresponding to chronic diseases for diagnosis and treatment institutions at each level to form a referral relationship mapping table; predicting the dynamic risk of chronic diseases based on multi-source and multi-modal data of the target population and classifying the target population, generating chronic disease management suggestions for each type of population and pushing them to chronic disease doctors for contract management; generating referral suggestions based on the dynamic risk changes of chronic diseases in each type of population and pushing them to chronic disease doctors, the chronic disease doctors determine the referral suggestions and set referral information based on the upstream and downstream referral paths and the referral relationship mapping table and simultaneously notify the target diagnosis and treatment institutions and patients; and providing performance incentives to chronic disease doctors based on whether they have completed the contract signing of chronic disease patients, implemented hierarchical referrals, and saved medical expenses. This can improve the accuracy of medical services, improve the efficiency of diagnosis and treatment, and promote medical cost saving.

The invention belongs to but is not limited to the technical field of medical information, and particularly relates to a medical big data intelligent analysis system and method based on a multi-stage health information platform and high-performance hardware, and the system comprises a multi-stage collaborative data platform, a high-performance hardware support layer, an intelligent analysis engine module, a scenarized application module and a full-link safety protection module. The multi-level collaborative data platform integrates electronic health archives, electronic medical records and an inspection database, and supports collaborative sharing of health information and medical record data. A HuaKunAT800 server cluster is adopted in the high-performance hardware supporting layer, and an Atlas910B2GPU card and a 25GE high-speed network are configured; the intelligent analysis engine module is based on DEEPSEEK localized deployment, adopts an AI model library, and covers disease risk prediction, image intelligent diagnosis and personalized medication recommendation functions; the scenarized application module comprises an inspection and inspection mutual recognition optimization system, a two-way referral system and a basic level integrated platform; the full-link security protection module integrates TLS encryption, block chain evidence storage and dynamic authority control technologies.

Systems and methods are disclosed herein for a telemedicine platform for location-based provider screening and referral. The systems and methods can include a computing device that can receive patient data, such as patient biographical information and medical information, and image data, such as photographs of a spot on the patient's skin, from a patient computing device. The computing device can determine the geolocation of the patient computing device and assign the patient to a medical referral site based on the geolocation. The computing device can send a notification to a medical provider's computing device indicating that the patient's uploaded data is ready to be reviewed and provide access to the patient data. The provider can input case information via the provider's computing device to send to the computing device. The computing device can generate a case summary report with the patient data, image data, and case information and send the case summary report to the referral site.

Disclosed, is an automated referral system for generating and managing referrals for services or products. According to certain examples, the system operates by receiving user-defined criteria from a client device, such as location, budget, and service type. It filters a database to identify a relevant subset of search results, which are then evaluated against a dynamically adjusted threshold to ensure relevance and adequacy. A referral request is generated based on the user criteria and the identified subset. The system ensures security through robust user authentication, potentially including two-factor authentication, before displaying the search results on the user's device and securely transmitting the referral request to appropriate recipients. The referral requests are encrypted for security. The system's dynamic adjustment of the threshold based on contextual factors and its capability to handle iterative user inputs for refining search criteria significantly enhance the user experience and the precision of the referral process.

An automated referral system and method for generating and managing referrals. The system receives user criteria and identifies relevant search results. It then generates and transmits referral requests to recipients corresponding to the search results. The system performs two-factor authentication before transmitting requests. Read receipts are received from recipients acknowledging the referral requests. The system then displays a subset of the search results to the user, based on which recipients have acknowledged the requests via read receipts. The display may include an interactive map showing locations of the results. This method enhances reliability and user trust in the referral process by incorporating confirmation steps and secure authentication while providing a user-friendly interface for reviewing curated search results.

Systems and methods for generating a referral-based database of exchangeable resumes utilizing blockchain smart contracts to digitally mimic human trust emotions and relationships are provided. The systems and method include at least one processor, for validating existing blockchain to permit writing the next sequential block, for executing smart contract chaincode that defines the user relational transaction profile and that creates the non-relational transaction details data storage medium locations written to the next sequential block, for directing the writing of the user relational transaction profile and the writing of the data storage medium locations of the non-relational transaction details to the next sequential block, and for directing the writing of the non-relational transaction details data to the at least one storage medium for storing the non-relational transaction details data accessible by permissioned platform users.

The present invention relates to a patient-centered integrated system for managing multiple healthcare services and networks. The system is configured to receive appointment requests from patient user groups, transmit appointment requests to relevant physicians, establish consultations between relevant physicians and patients, whether virtual or in-person, and transmit and receive medical documents between relevant physicians and patients, and to provide means for clinics, hospitals, and / or healthcare provider networks (HCPNs) linked to relevant physicians to manage appointments, virtual waiting rooms, payment collection, referral systems, and service order transmission within the network.

A healthcare delivery system that embodies methods of empowering collaborative multidisciplinary healthcare and the clinics that support them for providing everyday care to patients with chronic conditions, through in part by eliminating the current referral system to multiple specialists and providing collaborative multidisciplinary healthcare. A healthcare delivery that upends the traditional model where primary care physicians, who have an essentially blind first visit with a patient, try to develop plan of coordinated care in a very fragmented manner by referring patients to multiple specialists as needed. The present invention takes unconventional ordering of combined steps to deliver improved healthcare drastically differently from the traditional model.

The present invention provides a method for constructing a multi-disease referral system, including: S1. Obtaining training samples of fundus images for training, and labeling the fundus images with all diseases corresponding to each sample as positive labels to obtain a training data set; S2. Inputting the training samples in the training data set into the multi-disease referral system to be trained, wherein the multi-disease referral system includes multiple disease branch networks, and each disease branch network includes a classifier corresponding to a disease and an activation layer for processing the output of the classifier; the multi-disease referral system further includes a normalization layer for calculating the outputs of the classifiers of all disease branch networks; S3. Updating the weights of the multiple disease branch networks according to the loss function of the training samples until convergence; wherein the loss function includes the losses of all disease branch networks plus the multi-label regression loss of the merged outputs of the classifiers of all disease branch networks.

Systems and methods for creating a referral-based database of resumes of candidates using blockchain smart contracts are provided. The systems and method include providing at least one smart contract blockchain profile, the at least one smart contract blockchain profile corresponding to a unique user profile containing qualifications and experiences of at least one candidate, storing the at least one smart contract blockchain profile in a shared ledger of a database, accessing, via user interface, the at least one smart contract blockchain profile via the shared ledger of the database, implementing a scoring match value of the at least one smart contract blockchain profile against a posted qualifications and experiences requirement, implementing a pricing value to gain access to the least one smart contract blockchain profile, and allowing access of the at least one smart contract blockchain profile based on an accepted pricing value.

The invention discloses an AI-fused auxiliary LAM diagnosis system for lymphangioleiomyomatosis and a reading medium of the AI-fused auxiliary LAM diagnosis system. The above LAM diagnosis system is installed on a server, the LAM diagnosis system comprises a multi-modal data acquisition and preprocessing module responsible for collecting various types of data, and an AI diagnosis model construction module which is in data transmission connection with the multi-modal data acquisition and preprocessing module and is used for identifying relevant features of auxiliary lymphangioleiomyomatosis LAM, the dynamic risk assessment decision support module is in data transmission connection with the AI diagnosis model building module; and the intelligent referral system module is in data transmission connection with the AI diagnosis model building module and recommends an optimal referral path. The method has the advantages that the accuracy and efficiency of LAM diagnosis are improved, and uneven medical resource distribution and lack of personalized diagnosis are avoided.

Disclosed, is an automated referral system for generating and managing referrals for services or products. According to certain examples, the system operates by receiving user-defined criteria from a client device, such as location, budget, and service type. It filters a database to identify a relevant subset of search results, which are then evaluated against a dynamically adjusted threshold to ensure relevance and adequacy. A referral request is generated based on the user criteria and the identified subset. The system ensures security through robust user authentication, potentially including two-factor authentication, before displaying the search results on the user's device and securely transmitting the referral request to appropriate recipients. The referral requests are encrypted for security. The system's dynamic adjustment of the threshold based on contextual factors and its capability to handle iterative user inputs for refining search criteria significantly enhance the user experience and the precision of the referral process.

Systems and methods for creating a referral-based database of resumes of candidates using blockchain smart contracts are provided. The systems and method include providing at least one smart contract blockchain profile, the at least one smart contract blockchain profile corresponding to a unique user profile containing qualifications and experiences of at least one candidate, storing the at least one smart contract blockchain profile in a shared ledger of a database, accessing, via user interface, the at least one smart contract blockchain profile via the shared ledger of the database, implementing a scoring match value of the at least one smart contract blockchain profile against a posted qualifications and experiences requirement, implementing a pricing value to gain access to the least one smart contract blockchain profile, and allowing access of the at least one smart contract blockchain profile based on an accepted pricing value.

Disclosed, is an automated referral system for generating and managing referrals for services or products. According to certain examples, the system operates by receiving user-defined criteria from a client device, such as location, budget, and service type. It filters a database to identify a relevant subset of search results, which are then evaluated against a dynamically adjusted threshold to ensure relevance and adequacy. A referral request is generated based on the user criteria and the identified subset. The system ensures security through robust user authentication, potentially including two-factor authentication, before displaying the search results on the user's device and securely transmitting the referral request to appropriate recipients. The referral requests are encrypted for security. The system's dynamic adjustment of the threshold based on contextual factors and its capability to handle iterative user inputs for refining search criteria significantly enhance the user experience and the precision of the referral process.

The invention relates to the technical field of cardiovascular disease screening and intelligent medical treatment, in particular to an AI electrocardio-ultrasound referral integrated grassroots HCM early screening system which comprises a data acquisition module, an AI analysis engine, a referral management module, a terminal interaction module and a cloud database. The data acquisition module synchronously acquires dual-mode data of electrocardiosignals and ultrasonic images, and difference interference of basic-level equipment is eliminated through standardized preprocessing; an AI analysis engine adopts an improved attention mechanism CNN network, time sequence electrocardio features and space ultrasonic features are fused, a dynamic risk assessment model is constructed, and the AI analysis engine adapts to screening crowds of different age groups and basic medical histories; the referral management module automatically generates a structured report based on an early screening result and is linked with a superior hospital diagnosis and treatment system to realize referral information real-time pushing and state tracking; and the cloud database supports data encryption storage and model iteration updating. The system does not need to depend on advanced professional doctors, and can quickly complete HCM early screening, risk grading and referral connection in primary medical institutions.