41 results about "Clinician educator" patented technology

The present invention relates to a communication system and method designed for patients in clinical settings who face challenges using standard communication devices. This system includes a mobile application and associated devices that establish a secure, asynchronous communication channel between clinicians, patients, and their support networks. Authorized users can record and send voice messages and other media, which are automatically played on the patient's device without requiring active operation. The system is tailored for ease of use, accommodating patients with severe physical or cognitive limitations through hands-free operation and simple commands. It adapts to the patient's condition, pausing messages during rest or medical procedures and resuming when appropriate. The system also features cross-linguistic messaging, translating and synthesizing voice messages across languages while maintaining the speaker's voice. Additional features include support for rich media, automatic transcription, and AI-driven content analysis. Secure, encrypted channels ensure privacy, complying with healthcare regulations. This invention enhances emotional connection and support for isolated patients, acting as a virtual visitation tool.

In accordance with at least one aspect of this disclosure, an artificial intelligence driven bi-directional medical assistant is provided. The assistant comprises an input module configured to recognize, in real time, spoken language and convert the spoken language to a computer readable form to generate a patient embedding. The computer readable form includes, in certain embodiments, a mathematical vector associated with the patient embedding, and the spoken language includes a conversation between a clinician and a patient during a patient visit.

Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.

A system and method for a clinical education transition coordinator / preceptor system configured for cloud-based clinical assessment, training, and competency management of users. The system may include a shared marketplace or consortium, including resources relevant to specific geographic clinical education markets. The system may include a clinical education transition coordinator / preceptor application configured for training individuals in order to reskill and upskill licensed clinicians as well as for providing clinical site support for clinical students during clinical rotations.

A method for detecting diversion may include identifying an activity pattern associated with a clinician as being an infrequent activity pattern that occurs below a threshold frequency. Whether the infrequent activity pattern corresponds to an anomalous behavior may be determined based at least on one or more data models. The infrequent activity pattern may include a series of transaction records, which may be matched to the reference transaction values included in each of the one or more data models. An investigative workflow may be triggered in response to the infrequent activity pattern being determined to correspond to the anomalous behavior. Related methods and articles of manufacture are also disclosed.

A computer-implemented whole-health intelligence system and method providing personalized clinical and integrative health guidance based on multimodal data including medical history, lifestyle behaviors, psychosocial factors, environmental inputs, and biological metrics. The system constructs a hybrid digital health twin combining graph-based physiological relationships and vectorized embeddings, then generates personalized recommendations via generative AI models and clinical rule-based logic. Safety gating logic evaluates risk patterns and escalates medically significant findings to clinician review. The system supports longitudinal health optimization, annual precision-health evaluations, home-care coordination, and clinician co-pilot review workflows.

The invention belongs to the technical field of medicine, discloses a skull rapid growth marker and application thereof, and provides a skull rapid growth marker based on a pressure / dura mater FAK-Twist1 pathway and an application method of the marker in child skull defect repair. By detecting the expression level of the marker, the growth and development state of the pediatric skull can be accurately judged, and a scientific basis is provided for clinical doctors to select proper skull repair opportunities, so that the success rate and safety of pediatric skull defect repair operations are improved, and postoperative complications are reduced. The rapid skull growth markers determined by the invention are closely related to the growth and development mechanism of the child skull based on a pressure / dura mater FAK-Twist1 pathway, and the growth state of the skull can be accurately reflected by detecting the expression level of the markers, so that a reliable diagnostic basis is provided for opportunity selection of a child skull defect repair operation.

A distributed system for collaboration of the operations of a single or multiple healthcare provider entities, and more particularly, a distributed healthcare system and method that operates to provide unification of communications, siloed and fragmented data harmonization, remoteness and decentralization exacerbating the need for a unique solution to address new age healthcare needs, authentication for security and privacy and real-time data sharing.

A method and system identifies interactions of a user with one or more medical devices, determines, based on the one or more interactions and a predetermined set of rules, a compliance score associated with the first user, in response to the compliance score not satisfying a threshold compliance score, and reducing an access level of the user to at least one of the one or more medical devices and generates a training program associated with the at least one medical device and the one or more interactions, and automatically, without user involvement, sends a training package associated with the training program to the user and notifies the user to complete the training program using the training package. The system and method also generates a new shift schedule for clinicians responsive to a first shift schedule not satisfying criteria and the clinicians not satisfying respective performance scores.

The invention discloses a medical history collection system supporting clinical thinking path recognition, and belongs to the technical field of medical information. The system comprises a hypothesis agent cluster management module used for initializing an agent cluster composed of a plurality of hypothesis agents based on a clinical knowledge graph, each hypothesis agent corresponding to a candidate disease and comprising static knowledge related to the candidate disease; the doctor-patient interaction interface module is used for receiving the initial clinical evidence and analyzing the initial clinical evidence into an initial clinical evidence state vector; the dynamic evidence processing and state evolution engine is used for iteratively driving the state evolution of the agent cluster; and the thinking path generation and visualization module is used for generating a clinical thinking path according to the state evolution process of the agent cluster. The technical problem that the dynamic and nonlinear diagnosis thinking process of clinicians cannot be effectively captured and restored in the prior art is solved.

A computerized system for personalized medical education, compliance verification, and clinical workflow automation uses sensor-based identification and multi-modal monitoring to verify user presence, engagement, and comprehension during delivery of educational modules and required disclosures. Artificial intelligence models interpret electronic medical record data to select and sequence patient-specific content, determine procedure-specific and jurisdiction-specific compliance requirements, and adapt presentation in real time through device-level control actions including pausing, prompting, repeating, and module recalibration when noncompliance is detected. The system further supports conducting interactive clinical encounters in lieu of or in conjunction with clinician visits, including structured intake, decision support, and generation of audit-ready, billable encounter documentation under clinician oversight and safety guardrails. The system generates time-stamped interaction logs, medical decision-making and time-on-task summaries, and electronic medical record-ready outputs with provenance and compliance verification, suitable for reimbursement and audit defensibility across multiple specialities.

The present invention discloses a system and method for managing treatment plans and clinical documentation for substance use disorder (SUD) treatment. The system includes user devices associated with clients, clinicians, organization representatives, and supervisors. The system connects to a database storing client data, clinician data, assessment scores, and clinical documentation such as SOAP notes and progress notes. A computing device with artificial intelligence (AI) modules executes various program modules. A data collection module guides clinicians through predefined questions to gather information for generating SOAP and clinical progress notes. A secondary data collection module collects clarification from the counselor without requiring client input. A review module allows counselors to review, edit, approve, and submit documentation. A treatment plan generation module creates or updates plans based on collected data and prior scores. The system improves documentation efficiency, quality, and compliance, while supporting accurate and personalized treatment planning.

A safety receipt layer is interposed between an electronic health record (EHR) system and clinical decision support intervention (DSI) engines, including AI-assisted DSIs. For each DSI episode, the layer constructs a canonical clinical context envelope, evaluates a policy graph, and computes a permit outcome (permit, guarded permit, override, deny) that is enforced in a permit-before-action, fail-closed configuration. A time-of-check-to-time-of-use latch binds policy evaluation and any anchoring precondition to rendering and EHR write-back so outputs cannot be finalized without an affirmative permit or recorded override. The layer emits a structured, machine-verifiable safety receipt encoding policy identifiers, the context envelope or a cryptographic digest of a canonicalized field list, the permit outcome, requested and effective behavior modes, and clinician response. Receipts are anchored in an append-only verifiable log with maximum-merge-delay signed heads and inclusion and consistency proofs, enabling verification, role-based views, monitoring, and mappings to health IT transparency or certification requirements.

This disclosure relates to method and tool assisting clinicians in making real time decisions for resuscitation modalities in neonatal shock syndromes. The method includes receiving medical data corresponding to a neonate diagnosed with shock. The method further includes extracting one or more features from the medical data. The one or more features are indicative of perfusion status, possible etiology, type, and severity of shock, in the neonate. Further, the method includes selecting at least one machine learning model (ML) from a plurality of ML models based on the one or more features. Further, the method may include predicting via at least one ML model, an optimal treatment modality for the neonate. The method further includes assisting a clinician to provide the optimal treatment modality to the neonate based on predicting.

A method and non-transitory computer-readable medium for generating cognitive assessment scores from patient speech using an artificial-intelligence (“AI”) agent is provided. The method and computer-readable medium include initiating a patient audio communication session, conducting one or more oral cognitive assessments including narrative recall, engaging in a context-dependent discussion phase, assessing responses, and generating scores from audiometric, lexical, prosodic, and semantic-coherence features using multi-modal machine learning architectures. Audio data is processed by a backend analysis server using automatic speech recognition to generate a time-aligned transcript, and the backend analysis server uses a feature-embedding model to extract prosodic, acoustic, lexical, and semantic-coherence features from the audio and corresponding transcript. The backend analysis server uses machine-learning classification to generate cognitive assessment scores from extracted features. Assessment scores, feature data, and session records may be transmitted to clinician-accessible locations for review. The method enables automated, scalable, and reproducible speech-based patient cognitive assessments on patient-accessible devices.

The invention discloses a traditional Chinese medicine clinical data automatic processing system based on a medical big language model multi-agent, and relates to the technical field of medical data treatment. The system comprises a task understanding agent, a clinical data set treatment tool library and a data treatment agent which are in communication connection and cooperate to realize clinical data automatic processing in a traditional Chinese medicine clinical curative effect evaluation scene; the task understanding agent accurately recognizes traditional Chinese medicine clinical curative effect evaluation tasks and data requirements through training data set extension, knowledge distillation weighted fusion and expert preference alignment training; the clinical data set governance tool library comprises five Chinese and western medicine exclusive modules such as text structuring; and the data governance agent automatically generates a governance flow and schedules a tool library to execute through the exclusive fine tuning data set and Lora fine tuning. Full-process automatic processing of traditional Chinese medicine clinical data is realized, the task understanding accuracy and the data governance suitability are improved, and the scientific research efficiency of clinicians is improved.

A cloud-based, query management and education system enables provider organizations to query and educate clinicians on how to clarify clinical documentation, resulting in more accurate reimbursement, public reporting, research and policy decisions. Query authors create compliant queries from stored templates which may be supplemented by attaching and / or annotating selected documentation from the patient record. Clinician responses to such queries automatically generate addenda in the relevant patient record. All events around a query are tracked and reportable through a graphical performance scorecards and reporting dashboards.

A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and / or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and / or isolating medication accessed by the clinician.

A clinician-bounded closed-loop control system for therapeutic medical devices in regulated clinical environments is disclosed. The system receives physiologic monitoring signals and therapeutic-device telemetry and computes a patient state representation including confidence indicators. The patient state is evaluated relative to clinician-authored order sets defining autonomy levels and multi-dimensional physiologic and device corridors. When permitted, the system generates candidate therapeutic-device control commands and validates admissibility using device-specific safety grammars enforcing semantic bounds and transition constraints. Candidate commands are classified by risk, with high-risk actions gated on clinician authorization. Authorized commands are dispatched via a validation and execution interface that receives acknowledgements and post-dispatch telemetry. Conformance is evaluated using persistence criteria to distinguish transient excursions from sustained deviation. Upon sustained deviation or execution failure, governed corrective actions are initiated. Governance enforcement provides auditability through execution authorization artifacts and ledger-gated completion while preserving clinician authority and regulatory accountability.

A method for detecting diversion may include identifying an activity pattern associated with a clinician as being an infrequent activity pattern that occurs below a threshold frequency. Whether the infrequent activity pattern corresponds to an anomalous behavior may be determined based at least on one or more data models. The infrequent activity pattern may include a series of transaction records, which may be matched to the reference transaction values included in each of the one or more data models. An investigative workflow may be triggered in response to the infrequent activity pattern being determined to correspond to the anomalous behavior. Related methods and articles of manufacture are also disclosed.

Comprehensive and high-quality disease dictionaries are invaluable resources for tasks such as building ontologies, automated relation extraction, text summarization, question answering etc. Such curated resources are useful to clinicians, researchers, and various Biomedical Natural Language Processing tasks. However, these are manually curated and are labor and time intensive, and additionally suffer from lower recall and coverage is also less. Present disclosure provides systems and methods for augmenting rare disease dictionaries, wherein the system retrieves (new) rare diseases terms from medical literature that are related to the given dictionary terms (seed terms) and recommends new terms (or NPs) in a ranked order. This method is useful for rare diseases dictionary augmentation as a significant fraction of the top recommendations are new synonym candidates for dictionary augmentation. The method uses syntactic and semantic similarity measures in combination with efficient nearest neighbor search for efficient retrieval.

Systems and methods for behavioral and mental healthcare management. A system is comprised of a web-based platform and a device configured for an experiential virtual therapy session, such as a virtual reality (VR) and / or an augmented reality (AR) device. The therapy session is experienced by the patient alone or is simultaneously experienced by the patient and a healthcare practitioner such that the practitioner can directly observe the patient's reactions within the context of the simulation and provide real-time feedback during the session to help the patient develop coping skills. The system implements novel analytical approaches for clinicians to quantify and manage multidimensional aspects of mental health to improve the health and well-being of the patient for measurable behavioral health.

The invention belongs to the technical field of biomedical information, and relates to a multi-behavior quantification method and system for autism screening and application. According to the system, normal form presentation is adopted to induce facial expressions and upper limb actions of a subject, facial micro-expressions and limb dynamic change characteristics of the subject are captured through the data acquisition module, and the facial expressions and limb behavior characteristics of the subject are automatically acquired and analyzed through the data analysis module; according to the invention, the method achieves the quantification of facial expressions and limb movement features, outputs autism risk scores and classification judgment results of children through an output module, provides visual and comprehensive child behavior performance and autism risk information for clinicians, and improves the objectivity and ecological effectiveness of screening.

Methods and systems for computer-assisted contour revision. An image slice may be selected from a medical image. The image slice may include an initial contour of a target anatomical structure in the medical image. At least a portion of the image slice and the initial contour may be displayed on a graphical user interface (GUI). Upon determining that the initial contour requires revision, a revised contour may be generated. A first input may be received from a user to the GUI to indicate a first point of revision. The medical image, the first input, and the initial contour may be input into a trained deep neural network that automatically extracts learned image characteristics. The extracted learned image characteristics may be processed using one or more deep-learning segmentation algorithms of the trained deep neural network. The revised contour may be automatically generated using the processed extracted learned image characteristics.

A machine learning educational content recommender system that provides educational clinical content is disclosed. An educational content recommender system may receive input about a specific case and about a specific clinician. Using this data, the educational content recommender system may recommend educational clinical content for the specific clinician to use for a specific case. Feedback from the specific clinician may be used to train the educational content recommender system. This feedback may include either or both explicit and implicit feedback items.

The application relates to a high-function autism screening method based on a clinician-child dialogue, and relates to the technical field of natural language processing and medical auxiliary diagnosis. The application aims to solve the problem that existing screening relies on expensive equipment or manual features and is difficult to capture subtle abnormalities in children's language. The implementation steps are as follows: obtaining a doctor-child question and answer dialogue text; adopting a sliding window to combine multiple continuous question and answer pairs into an input sequence; using a pre-trained BERT model to extract word-level deep semantic representation; dynamically weighting the contribution of each word by an adaptive word aggregation module to obtain sequence-level aggregated features; performing paragraph-level classification; averaging the prediction results of all paragraphs of the same child to obtain individual screening results. The application realizes low-cost and high-efficiency early screening of HFA by analyzing the semantic, syntactic and pragmatic abnormalities in the dialogue language, and highlights fine-grained language clues with diagnostic value.

Methods and apparatus for managing a concurrent break are disclosed. In an exemplary method, one or more first clinicians are selected to take a concurrent break. The method identifies a total pool of patients that will need to be covered and selects one or more second clinicians that each have a set of break coverage attributes. The method assign one or more of the total pool of patients to each of the selected second clinicians while complying with their respective break coverage attributes. Once all patients are covered, the selected first clinicians are notified to go on the concurrent break and the selected second clinicians are notified of the patients that they are assigned to cover during the concurrent break.

The elastic band ligation device enables a clinician to deploy a plurality of ligation bands without assistance of an assistant and without an external aspirator. The ligation strap may be loaded to the distal end of the ligation device by a removable loading device, and a pusher may be provided to advance the ligation strap from the loading device onto the ligation device. A loading kit may be provided to continuously load two, three, or more ligation bands onto the same ligation device during a single procedure. Related methods are also disclosed.

Embodiments described herein provide a healthcare ecosystem with a computing device that collects different types (e.g., multi-modality) of subject data and develops patient digital twins as a predictive model configured to predict experimental outcomes of the subject. The system is capable of notifying a clinician of subject attributes for preparing a microphysiology system, sometimes referred to as' Patient Bionic Twin '(PBT), having attributes or diseases derived from predicted cells that can be used for experimental testing of predicted cells generated by a predictive model of PDT.

This invention provides a collaborative reasoning diagnostic assistance method and system, relating to the field of large-scale model technology. First, a knowledge base of clinical cases of birth defects in children, containing medical reasoning rules, is constructed. Then, multimodal diagnostic information of a target child is received and decomposed to obtain the decomposed results. The decomposed results of the target child's multimodal diagnostic information are dynamically correlated and reasoned with the case data and medical reasoning rules in the knowledge base of clinical cases of birth defects using a teaching large-scale model, generating a structured differential diagnosis opinion, including the defect type, medical evidence chain, and feature matching. The structured differential diagnosis opinion is pushed to the clinician's terminal, feedback is collected, and the reasoning parameters of the teaching large-scale model and the knowledge base rules are collaboratively adjusted. This invention can comprehensively utilize multimodal information, improve diagnostic accuracy and reliability, and can be continuously optimized and improved.