20 results about "Clinical document" patented technology

The invention provides a disease course record generation method and system based on a mobile terminal and multi-modal data, and the method comprises the following steps: firstly constructing a directional collection channel and a ward round session associated with a specific patient through the signal interaction between the mobile terminal and a sickbed; in the session, multi-modal data streams with timestamps, such as voice, images and operation instructions, are obtained in real time, and audio semantic anchor points and visual semantic anchor points are extracted from the multi-modal data streams. And constructing an image-text semantic unit by calculating and associating the audio and video anchor points of which the semantic similarity exceeds a threshold. And taking the unit as a current observation state, and verifying the legality of the state transition through a finite-state machine model. And based on the image-text semantic units passing the verification, template slot filling and term standardization are carried out, and a structured disease course record data packet is generated and written back to the HIS system. The method has the effect of improving the working efficiency and quality of clinical documents.

This invention relates to the field of smart healthcare technology and discloses a method and system for verifying clinical nursing information based on speech recognition. The method includes the following steps: an edge device acquires a nursing item script; responds to a voice wake-up command, driving the terminal to read the script item by item; collects real-time voice recordings and performs beamforming noise reduction, using semantic vectors to accurately extract assessment information and fill in the slots; intelligently identifies missing items and triggers dynamic follow-up questions; summarizes and generates structured confirmation information for repetition; if a modification command is detected, a pointer callback is triggered to jump to re-record, and after confirmation, the information is encrypted and uploaded to the cloud. This invention, through a cloud-edge-device collaborative architecture and breakpoint protection technology, achieves contactless entry and closed-loop verification of nursing records. This solution supports interrupted resume transmission and non-linear logic jumps, effectively solving the pain points of easily interfered clinical operations and cumbersome manual recording. While ensuring aseptic operation, it significantly improves the efficiency and accuracy of clinical document generation.

A system for an automated medical data processing based on patient-related data, including a processor of a healthcare processing server node configured to host at least Artificial Intelligence (AI) and machine learning (ML) modules and connected to at least one medical records cloud-based database and to at least one patient entity node over a network; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire user account creation and input data from at least one patient entity node using an OCR module; analyze patient intake data derived from the input data by an AI module configured to analyze the intake data; process user insurance data by an insurance AI module configured to generate an insurance verification verdict; acquire recommended lab test and triage data of the user and ingest the lab test and the triage data into an AI module configured to analyze the lab tests; receive treatment and medication suggestions and generate a feature vector based on the treatment and medication suggestions; provide the feature vector to an ML module configured to generate at least one clinical outcome model; derive clinical documentation data from the patient intake data and from the at least one medical records cloud-based database and apply NPL processing to the clinical documentation data; and acquire revenue cycle data from the clinical documentation data and ingest the revenue cycle data into an AI module configured to generate billing parameters.

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.

Some embodiments of the present disclosure provide a framework for using unsupervised artificial intelligence to automatically abstract and align clinical facets. The approach of the present application may be shown to reduce human involvement and, accordingly, enhance privacy compliance. Aspects of the present application relate to a process of self-learning from the data available. Accordingly, aspects of the present application may be shown to be resilient to the appearance of new concepts and facets in future data. Additionally, aspects of the present application may be shown to adapt well when presented with different languages, different styles of documentation and different clinical domains. Aspects of the present application relate to processing unstructured, non-fielded data, such as clinical notes, admission and discharge summaries, surgical notes, lab reports and imaging reports. These notes may be considered to contain hidden insights in the clinical domain. Additionally, these notes may be considered to contain data that may not be captured elsewhere in a readily usable way. Aspects of the present application may be shown to support analysis of large size populations at a relatively low incremental cost.

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 system for training medical providers in obesity management and obesity-related comorbidities includes a user device presenting a conversational interface to a medical provider, an application server managing user sessions, a database server storing user data including course completion records and clinical performance audits, and a learning management system server delivering educational modules based on established competencies. An artificial intelligence server processes queries from the medical provider and provides personalized medical advice using a large language model trained on obesity-related medical content including obesity physiology, pharmacotherapy, and patient management. The artificial intelligence server receives outcome information related to patient health metrics following the personalized medical advice and retrains the large language model based on the outcome information. A performance audit system monitors clinical documentation, prescribing patterns, and patient outcomes of the medical provider to ensure ongoing compliance and performance improvements.

Aspects provide a method for context-based clinical knowledge extraction and automatic transmission of clinical documents. A text-based representation of a document having a clinical context is obtained and an identifier which uniquely identifies the clinical context of the document is determined. An executable coding graph is identified from a plurality of executable coding graphs based on the identifier of the clinical context. The executable coding graph is indicative of a procedure for coding the document according to the clinical context and comprises a network of branch nodes interconnected with a plurality of coding nodes thereby forming a directed acyclic graph. The executable coding graph is executed on the text based representation of the document thereby generating a structured set of clinical information linked to the document enabling the automatic transmission of a clinical document based on the data extracted from the clinical document.

This invention discloses an intelligent management method and system applicable to drug clinical trials, relating to the field of drug clinical trial management technology. It includes collecting raw project activity records containing information such as trial site number and subject identification from the drug clinical trial execution system to form a collection. This collection undergoes multi-dimensional structured processing to generate a unified project view comprising subject time-series activity logs, trial site operational status snapshots, clinical document association networks, and key event sequences. Compliance modeling, effectiveness assessment, compliance checks, and logical verification are performed on the four types of views respectively. After generating corresponding specialized results, these are input into an intelligent analysis model for fusion and deduction, ultimately outputting a comprehensive project report including project risk warnings, process deviation location, and resource gap alerts. This invention achieves multi-dimensional integration and specialized fusion analysis of drug clinical trial data, optimizing the data analysis and application methods of clinical trial management.

A computer-implemented method for digitizing clinical documents comprises receiving a clinical document and a policy type. The method further comprises de-skewing and removing noise from the page. The method further comprises generating extracted text from the processed page using OCR, analyzing the extracted text to generate a classification label for the page using a multinomial classifier model comprising an LSTM neural network, which has been trained to recognize different classes of page based on similarities to a corpus of historical pages that have been previously classified. The method further comprises recognizing a specified keyword within the extracted text, using an NER that is trained using policy data relating to a policy having the policy type, and displaying a visual representation of the clinical document with a visual indicator of the presence of the specified keyword on the page, and a visual indicator of the classification label.

An automated system and method for medical billing charge capture is disclosed. The system integrates with electronic health records and specialty clinical systems to extract structured and unstructured clinical data, including documentation of patient care activities. A natural language processing engine analyzes unstructured text to identify relevant clinical attributes, which are combined with structured data and evaluated by a rules-based decision engine. The decision engine applies predefined billing rules and insight conditions to predict and validate appropriate billing codes. The system operates in a cloud-hosted, modular architecture, allowing secure, scalable deployment. Validated billing codes are stored with supporting documentation and transmitted to a billing system through secure protocols. A compliance dashboard provides real-time alerts, operational metrics, and audit reports. The system eliminates the need for manual clinician charge entry, improves compliance with payer requirements, reduces errors, and enhances efficiency by automating charge capture based on clinical documentation and predefined billing rules.

A clinical documentation improvement (CDI) smart scoring method may include predicting, via per-condition diagnosis machine learning (ML) models and based on clinical evidence received by a system, a probability that a medical condition is under-documented and, via per-condition documentation ML models and based on documentation received by the system, a probability that a medical condition is over-documented. The under- and over-documentation scores are combined in view of special indicators and queryability factors, which can also be evaluated using ML query prediction models, to generate an initial CDI score. This CDI score can be further adjusted, if necessary or desired, to account for factors such as length of stay, payer, patient location, CDI review timing, etc. The final CDI score can be used to prioritize patient cases for review by CDI specialists to quickly and efficiently identify meaningful CDI opportunities.

Portable data acquisition device for real-time creation of clinical documentation, comprehensive a multi-channel sensor unit for the simultaneous acquisition of voice data, motion data and context-related environmental parameters, a processing unit with an AI-supported generation unit that performs a context-adaptive semantic analysis of the captured data and transforms it into structured clinical documentation datasets, a local cache for the temporary storage of the captured raw data and generated content, as well as an interface unit for transferring the generated documentation to external clinical systems, furthermore, the processing unit is designed to ensure improved accuracy and consistency of the automatically generated clinical documentation in real time through time-synchronized data fusion from multiple sensor channels.

A method, computer program product, and computing system for receiving audio consultation information from a first microphone system, thereby defining a first audio stream. Audio consultation information can be received from a second microphone system, thereby defining a second audio stream. Speech activity can be detected in one or more portions of the first audio stream, thereby defining one or more speech portions of the first audio stream. Speech activity can be detected in one or more portions of the second audio stream, thereby defining one or more speech portions of the second audio stream. The first audio stream and the second audio stream can be aligned based at least in part on the one or more speech portions of the first audio stream and the one or more speech portions of the second audio stream.

A system is provided.SOLUTION: This system is provided with a means for receiving inputted medical information, a means for preparing the draft of a clinical document by using a generation AI based on the received medical information, a means for displaying, correcting and confirming the prepared clinical document and a means for storing the confirmed clinical document in a database.SELECTED DRAWING: Figure 1

This invention discloses an intelligent management method and system applicable to drug clinical trials, relating to the field of drug clinical trial management technology. It includes collecting raw project activity records containing information such as trial site number and subject identification from the drug clinical trial execution system to form a collection. This collection undergoes multi-dimensional structured processing to generate a unified project view comprising subject time-series activity logs, trial site operational status snapshots, clinical document association networks, and key event sequences. Compliance modeling, effectiveness assessment, compliance checks, and logical verification are performed on the four types of views respectively. After generating corresponding specialized results, these are input into an intelligent analysis model for fusion and deduction, ultimately outputting a comprehensive project report including project risk warnings, process deviation location, and resource gap alerts. This invention achieves multi-dimensional integration and specialized fusion analysis of drug clinical trial data, optimizing the data analysis and application methods of clinical trial management.

A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.

Example solutions for augmenting text inputs for analyzing clinical documents include: identifying a clinical named entity within text content of a clinical input document; adding an anchor tag to the text content, the anchor tag including an entity ID and a clinical attribute associated with the clinical named entity, thereby generating an enhanced input document; and submitting a first query prompt and the enhanced input document to a generative artificial intelligence (GAI) model, the first query prompt including task text and anchoring markup text, the task text includes instructions of a task to be performed by the GAI model on the enhanced input document, the anchoring markup text includes a template of the anchor tag and instruction to add a reference anchor tag to output generated by the GAI model, where the reference anchor tag is to include the entity ID and the clinical attribute of the anchor tag.

The application provides a kind of course record generation method and system based on mobile terminal and multi-modal data, which comprises the following steps: first, through the signal interaction of mobile terminal and sickbed, directional acquisition channel and ward round conversation associated with specific patient are constructed.In the conversation, time-stamped multi-modal data stream such as voice, image and operation instruction is obtained in real time, and audio semantic anchor points and visual semantic anchor points are extracted from them respectively.Through calculation and correlation of audio and video anchor points with semantic similarity exceeding threshold, a graph-text semantic unit is constructed.The unit is taken as the current observation state, and the legality of this state transition is verified by finite state machine model.Based on the verified graph-text semantic unit, template slot filling and term standardization are carried out, structured course record data package is generated and written back to HIS system.The application has the effect of improving the efficiency and quality of clinical documentation.

The method (1300) includes receiving input data (102) including unstructured text representing one or more sequences of terms (104). For each respective sequence of terms, the method includes generating a corresponding Line of Therapy (LoT) pseudo-label (122) indicating whether the respective sequence of terms includes LoT information, generating a corresponding LoT indicator (132) predicting whether the respective sequence of terms includes LoT information, and determining a corresponding LoT-directed loss (242) based on the corresponding LoT pseudo-label and the corresponding LoT indicator. The method also includes fine-tuning a pre-trained Transformer model (300) based on the LoT-directed loss determined for the one or more sequences of terms.