64 results about "Patient identifier" patented technology

Systems and methods for automatically populating a post-operative report of a surgical procedure are disclosed. A system may include at least one processor configured to implement a method including receiving an identifier of a patient, an identifier of a healthcare provider, and surgical footage of a surgical procedure performed on the patient. The method may include analyzing frames of the surgical footage to identify phases of the surgical procedure based on interactions between medical instruments and biological structures and, based on the interactions, associate a name with each phase. The method may include determining a beginning of each phase and associating a time marker with the beginning of each phase. The method may include populating a post-operative report with the patient identifier, the names of the phases, and time markers associated with the phases in a manner that enables the health care provider to alter the post-operative report.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The present disclosure provides a method for performing a treatment plan, wherein the method comprises: receiving first patient data, wherein the first patient data includes at least a first patient identifier associated with the first patient and a first treatment plan; receiving second patient data, wherein the second patient data includes a second patient identifier associated with the second patient and a second treatment plan; receiving first measurement data associated with a first performance level of the first treatment plan by the first patient; receiving second measurement data associated with a second performance level of the second treatment plan by the second patient; determining differential data, wherein the determining is based on a contrast of the first or the second measurement data or first or second patient data; and generating, based on the differential data, an instruction to modify an operating state of the treatment apparatus.

Systems and methods for providing secure access to prescribed medications are disclosed. A database includes information regarding prescribed medications for patients, unique patient identifiers, each associated with a respective portion of the information regarding one or more of the prescribed medications and unique secondary user identifiers. Sets of unique identifiers are received, each including a first and second unique identifier. For each received set, the first and second unique identifiers are compared against the unique patient and secondary identifiers respectively to determine if a match exist. If so, access is permitted to the associated portions of the information to a designated remote device associated with the respective set of unique identifiers.

The invention provides a hemodialysis room whole-process information management system based on active learning, and relates to the technical field of data processing, and the system comprises a feature construction module which is used for carrying out the structural processing of original dialysis information data, and extracting a plurality of feature factors related to the current physical state of a patient; the risk identification module is used for constructing a training sample group, performing supervised training by introducing a label, and generating a risk judgment model; the early warning generation module is used for generating a risk probability result and generating early warning information associated with the current patient identifier when the risk probability result reaches a preset risk threshold value; the scheduling module is used for dynamically adjusting the treatment scheduling sequence of the current patient according to the early warning information and recording the actual intervention effect after the treatment scheduling sequence is adjusted; the feedback learning module is used for endowing each feature factor with a weight value according to an actual intervention feedback result and feeding back the weight value to the risk identification module; according to the invention, autonomy and accuracy of information management are improved.

A method for operational workflow transparency and end-to-end events tracking, the method includes at an event producer system, generating events, corresponding key business attributes, provider industry standard identifiers, patient identifiers, and patient information, and publishing the events and the key business attributes to a tracking system. At a tracking system, receiving and validating the events from the event producer system, generating a universally unique lexicographically sortable identifier (ULID) for an append-only log data structure for the respective event, generating a search index to enable search capabilities for the append-only log data structure, and temporally appending the respective event into the append-only log data structure for the corresponding prior-authorization case.

Disclosed herein are techniques for deidentifying personally identifiable information (Pll). A method includes training a large language model (LLM) on a set of clinical records to generate a clinical analysis; fine tuning the LLM on a set of Pll training data to identify Pll using machine learning-based name entity recognition; receiving from a client device a clinical analysis request and a patient identifier; providing, to the LLM the clinical analysis request and one or more patient records associated with the patient identifier, causing the LLM to generate a clinical analysis, identify one or more Pll instances in the clinical analysis, deidentify at least one of the one or more Pll instances to generate a deidentified clinical analysis; receiving from the LLM a deidentified clinical analysis; and transmitting the deidentified clinical analysis to the client device.

The invention discloses a high-precision real-time urodynamic monitoring method and system, and belongs to the technical field of medical monitoring. A urine flow sensor is arranged at the downstream of a catheterization assembly to collect urine flow and generate an electric signal; the electric signals are transmitted to a micro-control unit for digital processing, and the instantaneous flow velocity of urine, the accumulated urine volume per hour and the total urine volume of 24 hours are calculated in real time through an integral flow velocity algorithm; a calculation result is output to a local display screen, and real-time data viewing and historical curve graph and statistical report display are supported; meanwhile, the urine volume data are packaged according to timestamps and patient identifications and uploaded to a hospital nursing information system through a standardized interface protocol; if it is detected that the urine volume is abnormally lower than a set threshold value or is completely in a urine-free state, the system automatically triggers an alarm and pushes the alarm to the nursing terminal; by means of the method, automation, informatization and intelligentization of urine volume monitoring can be achieved, and the method is suitable for continuous nursing requirements of ICU and other critical areas.

The invention discloses a medical system structured data acquisition method based on a web crawler technology, and relates to the technical field of data processing, a patient information reading module obtains a patient identifier from a pre-prepared list file, and provides the patient identifier to a network request and redirection processing module; the network request and redirection processing module constructs a network request link and sets request header information according to the patient identifier in combination with an interface specification of a hospital information system, and performs redirection processing to generate response data; the data classification analysis module performs condition matching and extraction on the response data based on a preset rule to obtain medical data; the time screening and keyword matching module extracts key data in the medical data according to a preset condition; and the structured write-in module writes the target fields in the key data into a target storage medium one by one for storage according to a preset field mapping rule. According to the invention, integration of multi-source and dispersed medical data is realized.

The invention relates to the technical field of multi-source medical data integration, in particular to an integration method and system for integrating multi-source medical data sets. Comprising the following steps: S1, collecting multi-source medical data into an LIMS system, and associating the multi-source medical data according to a patient identifier; s2, extracting historical patient diagnosis symptoms according to the patient identifier to establish a patient symptom model, inputting associated medical data after association according to the historical patient diagnosis symptoms, and outputting a symptom related report through the patient symptom model; the medical knowledge graph is extracted from the multi-source medical data, accuracy analysis is carried out on disease related reports, correction is carried out according to different types of disease related reports, the accuracy of diagnosis reports is greatly improved, then the corrected reports are integrated into a disease diagnosis model, training and optimization are carried out through a machine learning algorithm, and the accuracy of the diagnosis reports is improved. The model can learn a large amount of medical knowledge and diagnosis modes.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for creating source-specific, persistent patient identifiers for healthcare service providers. One method includes accessing a record of healthcare data, wherein the record includes patient identifying information (PII) associated with one or more persons to whom the healthcare data pertains. The portions of PII included in the accessed record of healthcare data are extracted from the accessed record and encrypted. Based on one or more business rules, one or more hashed tokens are created by applying one or more hashing functions to the extracted portions of PII. A source-specific identifier is received, the source-specific identifier having been encoded in a manner specific to an organization associated with the computer system and having been encoded with reference to the one or more hashed tokens. An association is stored between the source-specific identifier and the accessed record of healthcare data.

The invention relates to a multi-mode ophthalmology auxiliary diagnosis and nursing follow-up visit system which comprises a multi-mode ophthalmology basic data collection module used for obtaining a patient identifier when a patient inquires for the first time, obtaining new multi-mode ophthalmology basic data of the patient in each disease course stage according to the patient identifier, and sending the new multi-mode ophthalmology basic data to a follow-up visit module; determining auxiliary information of the new multi-mode ophthalmology basic data, and storing the new multi-mode ophthalmology basic data and the auxiliary information; the whole-course auxiliary diagnosis module is used for determining an auxiliary diagnosis decision according to the multi-mode ophthalmology basic data and the auxiliary information; the whole-course nursing follow-up visit module is used for determining a follow-up visit level and generating a nursing follow-up visit scheme according to the multi-mode ophthalmology basic data and auxiliary information; and the output module is used for outputting a nursing follow-up scheme and / or an auxiliary diagnosis decision according to the follow-up level, so that the auxiliary diagnosis nursing follow-up of the whole disease course stage can be realized, and the auxiliary accuracy of each disease course stage is ensured.

The embodiment of the invention discloses an auxiliary decision support information generation system and an auxiliary decision support information generation method. A specific embodiment of the system comprises a server, each heterogeneous data source and at least one terminal, the server is configured to execute the following processing: acquiring a heart and kidney metabolic syndrome patient information table which comprises each patient identifier; creating and updating a heart and kidney metabolic syndrome knowledge graph; for each patient identifier, generating a standardized diagnosis and treatment event record information set; generating an initial diagnosis and treatment event space-time diagram; determining each generated initial diagnosis and treatment event time-space diagram as an initial diagnosis and treatment event time-space diagram set, and storing the initial diagnosis and treatment event time-space diagram set; generating a diagnosis and treatment event space-time diagram corresponding to the treatment data; generating auxiliary decision support information; and sending the auxiliary decision support information to the terminal. According to the embodiment, scheduling resources of the medicine taking robot are reduced.

The application relates to the technical field of electrocardiosignal processing, and discloses a monitoring electrocardio data analysis method based on online monitoring, wherein the method comprises the following steps: performing multistage filtering pretreatment on an original electrocardio signal; detecting a QRS complex and extracting an RR interval sequence; extracting time domain statistical features, frequency domain features and wavelet coefficients to form an electrocardio feature vector; inputting the electrocardio feature vector and an electrocardio signal segment into an atrial fibrillation detection model containing a signal feature learning branch and a sequence feature learning branch, and outputting an atrial fibrillation detection result; and generating an alarm information based on the detection result and pushing the alarm information to a medical terminal. Through atrial fibrillation load statistics and stroke risk grade evaluation, the single abnormality detection result is expanded into a continuous risk quantization index, a coherent analysis link from abnormality identification to risk evaluation is provided for clinical doctors, complete data storage is associated with a patient identifier and a time stamp, all analysis processes can be traced back, and the needs of clinical audit and department review are met.

The invention discloses a time axis key node automatic acquisition system for chest pain diagnosis and treatment, and relates to the technical field of information and communication of medical data, and the time axis key node automatic acquisition system comprises the following steps: pre-storing an interval template containing a starting or ending anchor point rule; acquiring an event stream which comes from one or more medical information systems and carries a chest pain patient identifier and a source system timestamp in real time; creating and maintaining an active interval instance in a memory for the chest pain patient; inferring a candidate end event; and identifying the time reference drift of the source system, and adding a credibility mark to the time-consuming data. Automatic and real-time monitoring of time consumption in the diagnosis and treatment process is achieved, the system supports starting and ending anchor point matching, automatically recognizes time reference drift and adds credibility marks, and the reliability of time consumption data in a real and complex environment is guaranteed on the premise of not depending on strict time synchronization of a hospital-wide system.

The embodiment of the invention provides an operation scheduling method and a medical data processing system, which are used for improving the efficiency and convenience of arranging an operation for each operating room. The method provided by the embodiment of the invention comprises the following steps: in response to a selection operation on any patient operation information in a patient operation information display component and an interaction operation between the selected patient operation information and an operating room information display component, generating a first interaction instruction, the patient operation information display assembly and the operating room information display assembly are displayed on a preset interactive interface, and the patient operation information display assembly is used for displaying a surgeon, a patient department and at least one of a patient identifier and an operation identifier. The operating room information display assembly is used for displaying the number of operations in an operating room, the total duration of the operations and visual operation progress information; according to the first interaction instruction, the selected patient operation information is switched from the patient operation information display assembly to the operating room information display assembly.

A method to verify documentation, such as healthcare documentation, in real-time is described. An image of a first document and demographic information is received from a patient. Optical character recognition (OCR) or QR code, barcode, or machine readable zone (MRZ) reading is performed on the image of the first document and a patient identifier is extracted from the first document. The patient identifier is compared to content in a database to identify a second document related to the first document that shares the patient identifier with the first document. The first document and the second document are verified as being associated with the patient based on the comparison and predefined business rules. OCR errors are identified in the first document and corrected using content from the second document. The method occurs prior to and allows the patient to pay for a healthcare appointment prior to attending the appointment.

This disclosure presents an auxiliary decision support information generation system and method. One specific implementation of the system includes: a server, various heterogeneous data sources, and at least one terminal; the server is configured to perform the following processes: acquiring a patient information table for cardiorenal metabolic syndrome, wherein the patient information table includes individual patient identifiers; creating and updating a knowledge graph for cardiorenal metabolic syndrome; generating a standardized set of medical event records for each patient identifier; generating an initial spatiotemporal graph of medical events; determining the generated initial spatiotemporal graphs of medical events as an initial spatiotemporal graph set, and storing the initial spatiotemporal graph set; generating a spatiotemporal graph of medical events corresponding to the patient's medical data; generating auxiliary decision support information; and sending the auxiliary decision support information to the terminal. This implementation reduces the scheduling resources of the medication dispensing robot.

Systems and methods for integrating analyte monitoring system data and electronic medical record system data are described. The methods include associating a patient's analyte data identified with an external patient identifier associated with at least one healthcare practice. The patient's analyte data identified with the external patient identifier may be transferred from a first cloud server to a second cloud server. The analyte data may be stored in a folder associated with the at least one healthcare practice in the second cloud server. The analyte data may also be merged with electronic medical records of the patient in the second cloud server. The merged data may then be transferred to a database for further analysis.

This disclosure relates to a method, apparatus, storage medium, and electronic device for processing medical events, belonging to the field of medical big data processing technology. The method includes: acquiring raw medical data corresponding to the current patient based on the current patient's patient identifier; performing structured extraction on the raw medical data to obtain current event entities included in the raw medical data; determining the current event axis of the current event entity based on its event category; mapping the current event entity onto the current event axis based on its current time node to obtain a target event axis; and displaying the target event axis so that medical personnel can analyze the current patient's treatment process based on the target event axis. This disclosure enables the visualization of medical events included in raw medical information.

Systems, apparatus, instructions, and methods for enterprise reading with search and streaming of information from local and remote sources are disclosed. An example apparatus includes a viewer to generate a request for patient content and to display at least a subset of the patient content according to a hanging protocol. The example apparatus includes a search service to query a plurality of sources for the patient content based on a patient identifier. The example query is to return meta data to generate at least one token encapsulating a location of identified patient content and an indication of access to the identified patient content. The example apparatus includes a streaming service to obtain the identified patient content using the at least one token and to provide the identified patient content to the viewer for display.

The invention discloses an ophthalmology intelligent auxiliary diagnosis method and system based on heterogeneous data collaboration, and the method comprises the steps: synchronously obtaining an ophthalmology multi-mode heterogeneous data set of a patient, including an OCT image, a fundus photograph and a visual field inspection report; the OCT image is subjected to retina layered structure segmentation, and an RNFL thickness distribution diagram is extracted; performing optic disc region positioning and blood vessel tree segmentation on the fundus photography to generate a CDR and a blood vessel curvature quantized value; mode deviation probability graph coding is carried out on the visual field inspection report, and a quadrant sensitivity missing matrix is output; an ophthalmic multimodal heterogeneous data set is associated through a patient unique identifier, and a RNFL thickness distribution map of OCT is mapped to an optic disc coordinate system of fundus photography by using feature-driven non-rigid registration. According to the invention, an ophthalmology multi-modal data cooperation barrier is broken through, an intelligent decision-making system with structure-function mutual verification is constructed, and the diagnosis reliability and clinical interpretability are significantly improved.

The invention discloses an infusion information management system, which relates to the technical field of medical informatization equipment and comprises a task information access module, a label data generation module, a dual-channel acquisition module, a context association processing module, a synchronous locking control module, a transaction state judgment module and an execution record collection module. According to the method, a cross time locking domain and a transaction serialization mechanism are introduced into the system, so that unified constraint of multi-terminal data in a time domain and a logic domain is realized; meanwhile, scanning, position and equipment identity information are subjected to multi-dimensional binding through a context association structure, atomic-scale correspondence of an infusion label and a patient identifier at the infusion execution moment is achieved, and false matching caused by time sequence drift or concurrent operation is avoided; according to the invention, the safety of infusion behaviors and the consistency of system-level information can be obviously improved.

The invention discloses a pathological image multi-task analysis method and system integrating medical priori knowledge, and the method comprises the steps: obtaining pathological image blocks of a plurality of patients, the pathological image blocks being associated with corresponding space coordinates, pathological labels and patient identifiers; respectively extracting visual features of each pathological image block; based on the space coordinates, the pathological labels and the visual features, constructing a graph structure fused with medical priori knowledge; taking the visual features as initial embedding of graph nodes of the graph structure, and inputting the visual features into a graph neural network for message passing so as to obtain updated graph node feature representation; and based on the updated graph node feature representation, executing multi-task learning so as to realize image block-level pathological classification and patient-level survival risk prediction. By constructing a medical priori knowledge graph structure fusing space coordinates, pathological labels and visual features and executing end-to-end multi-task learning, collaborative optimization of image block-level pathological classification and patient-level survival risk prediction is realized.

The invention discloses a general medical department doctor-patient information interaction method and system, and particularly relates to the technical field of information interaction.The method comprises the steps that multi-mode health information such as texts, voice and images is collected through a patient end, and an original data packet is formed through packaging according to a timestamp and a patient identifier; according to the method, semantic analysis and entity extraction are carried out on texts and voices, symptoms, parts and duration are mapped to structured health information vectors, association is carried out in combination with image features, and multi-modal unified coding representation is formed; calculating a symptom fluctuation index and a behavior consistency index, inputting the indexes into a predefined model to generate a comprehensive health judgment coefficient for intelligently judging key concern contents, generating a doctor end abstract report and a diagnosis and treatment reference prompt based on the information, and synchronously updating feedback to a patient end to realize dynamic optimization of doctor-patient information interaction; the problems that traditional doctor-patient information is scattered, and key health information is difficult to obtain quickly are effectively solved.

Described is a system comprising an input module receiving a first data set indicative of at least one patient condition for each of a plurality of patients obtained during a predetermined time period, a comparison module comparing each of the at least one patient condition to at least one filter criteria, a filter module selecting a patient to include in a second data set if the at least one patient condition of the patient satisfies the at least one filter criteria, a report module generating a report based on the second data set, wherein the report includes at least one patient identifier for each patient in the second data set and at least one descriptor of the at least one patient condition for each patient in the second data set, and a classification module storing at least one classification value for the at least one patient condition.

A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.

A medical device data back-association system, apparatuses, and methods are disclosed. In an example embodiment, an electronic medical record memory receives, while an infusion pump is administering a medication to a patient, an identifier message including at least two of a device identifier of the infusion pump, a patient identifier of the patient, and a medication order identifier of a medication being administer by the infusion pump. The memory creates an association between an electronic medical record (“EMR”) of the patient and the infusion pump using the identifier message. The memory next receives, after the association between the EMR of the patient and the infusion pump, infusion pump data from the infusion pump. The infusion pump data includes the device identifier. The memory then stores the infusion pump data to the EMR of the patient based on the created association between the EMR of the patient and the infusion pump.

This disclosure relates to a method for securely storing and retrieving medical data. The method includes: - obtaining medical data in a secure environment, the medical data including patient attribute data and patient identifier data, wherein the patient identifier data indicates at least one patient corresponding to the patient attribute data; - generating de-identified medical data in the secure environment by replacing the patient identifier data in the medical data with non-patient identifier coded identifiers; - generating a re-identification database in the secure environment indicating the correspondence between the non-patient identifier coded identifiers and the de-identified medical data; - generating an encrypted re-identification database by applying at least one symmetric and / or asymmetric encryption method to the re-identification database in the secure environment; - storing the encrypted re-identification database and the de-identified medical data on cloud storage outside the secure environment.