28 results about "Clinician educator" patented technology

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 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.

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 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.

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.

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.

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.

A human-in-the-loop clinical override and accountability governance system enforces explicit clinician evaluation of AI-assisted clinical outputs prior to workflow execution. The system generates cryptographically protected accountability records binding clinician judgment to AI-assisted outputs, improving governance, safety, and trust without autonomously diagnosing conditions or prescribing treatment actions.

The invention relates to the technical field of artificial intelligence and psychological diagnosis, and particularly discloses a mental disorder auxiliary diagnosis method based on natural language inquiry, which adopts a multi-source fusion mode, combines natural language inquiry and scale data, improves the objectivity and accuracy of diagnosis, is automatic and efficient, remarkably shortens the diagnosis time of doctors, and reduces manual dependence. Interpretability is achieved, a diagnosis evidence source is output, and trust of clinicians on results is improved; and expandability is realized, and different scales and novel inquiry question libraries can be accessed to adapt to different crowds.

A process of generating data carrying information on a treatment recommendation for a clinician including presenting a set of questions to a subject at a computer implemented user interface, each question having a defined loading of one of a set of behavioural factors, receiving responses to the set of questions from the subject at the computer implemented user interface, quantifying each response to represent a scale of the response, combining the response with a mapping of the item to its respective behavioural factor to generate data carrying information representing the scale of response and mapping of the response to the behavioural factor, combining the quantified responses for each behavioural factor to generate data carrying information on combined responses for each behavioural factor in the set, and applying a computer implemented analytical process to characterise the subject using the behavioural factors to generate data carrying information on a characterisation of the subject by behavioural factor.

An AI keystone system for medical decision support generates governed, auditable clinical insights. A medical data governance (MDG) engine ingests heterogeneous patient-related data from clinical systems, medical devices, and clinician inputs, validates and normalizes the data under governance rules, and emits structured, context-constrained representations for identified patients and episodes. One or more intermediary components collect and relay real-time device data so downstream processing operates only on governed data. A reasoning layer with artificial-intelligence models, including at least one large language model, cooperates with modular AI components to process clinician queries and generate candidate clinical outputs. A cryptographic governance subsystem evaluates candidate outputs against patient-safety rules, regulatory mandates, institution-specific policies, and stored consent artefacts using machine-verifiable constraints, enforces dual-factor clinician authentication, and maintains immutable, hash-chained audit trails with trusted timestamps that gate workflow progression. The system enables defensible use of AI-assisted clinical insights across hospital, clinic, and virtual-care environments.

A clinical AI quality, bias, and equity monitoring governance system computes stratified performance metrics for AI-assisted clinical workflows and detects equity signals across defined cohorts. The system supports governance review, mitigation tracking, and regulatory-ready reporting while preserving clinician authority.

Digital Healthcare Systems and Methods A digital healthcare system comprises: a clinician-facing application, a patient-facing application, and a server-based application. The patient-facing application is configured to: receive in a first user input, first patient data, and send the first patient data to the server-based application. The server-based application is configured: upon receiving the first patient data, to trigger the first patient data to be parsed through a global knowledge-graph, KG, comprising a plurality of sub- KGs, each sub-KG relating a different health condition; receive a first clinical insight as output from the global-KG; and send the first clinical insight to the clinician-facing application. The clinician-based application is configured to: display a first update, in response to receiving the first clinical insight.

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 invention discloses a drug abortion success rate prediction system and method based on a random forest, particularly relates to the technical field of medical data analysis, and aims to solve the problem that clinical doctors depend on experience to judge the success rate of drug abortion and lack scientific and quantitative prediction tools. The method comprises the following steps: firstly, selecting a target population which requires drug abortion and has no contraindication, and collecting clinical data of the target population; making a binarization coding rule, coding the collected clinical data of the target population item by item, and constructing a feature vector of the target population; based on the feature vector of the target population, predicting whether the drug abortion of the patient succeeds or not through a random forest model; then collecting the drug abortion outcome of the target population; and finally, merging the drug abortion outcome and the clinical data-data model of the patient to obtain a predicted value of the drug abortion outcome of the patient. According to the invention, the evaluation system for the adaptive population of the drug-induced abortion is established, the adaptive population of the drug-induced abortion is defined, and the failure rate of the drug-induced abortion is reduced.

A scheduling system is provided that may be used in the medical or other industry. The scheduling system can facilitate creating, applying for, and scheduling shifts. The scheduling system can be configured to assist employers and clinicians in identifying recommended pay rates for shifts. These recommended pay rates can be generated using artificial intelligence based on previous shifts and clinicians who were scheduled for those shifts.

Techniques described herein leverage recent advances in artificial intelligence to enable generating new analyses and insights based on comprehensive data sets that include treatment fidelity data related to patients. These new analyses and insights may be used to then provide better clinician feedback and / or better customization of treatment plans for patients, thereby improving outcomes for patients while also improving the ability of clinicians to provide effective care.