63 results about "Patient record" patented technology

Systems and methods for de-identifying patient data are disclosed herein. In some embodiments, a method for de-identifying patient data includes receiving a patient record including one or more identifiers. The method can include generating a first de-identified record from the patient record using a first de-identification process. The first de-identification process can be configured to produce a first re-identification risk score. The method can further include receiving a request from a data recipient to access the first de-identified record. The method can also include generating a second de-identified record from the first de-identified record by using a second de-identification process. The second de-identification process can be configured to produce a second re-identification risk score lower than the first re-identification risk score.

A system and a method for automatically assisting physicians during patient encounters includes transcribing and diarizing physician-patient interactions, extracting clinical concepts, and combining data with patient history data to provide suggestions to the physicians. A speech and gesture recognition module captures audio from patient-doctor interactions and transcribes the audio into text in real-time. A natural language processing module diarizes the transcribed text to attribute speech. A clinical concept extraction module identifies and extracts clinical concepts from transcription. A clinical recommendation module integrates real-time data with historical patient records and analyzes the combined data set to generate evidence-based suggestions to the physician. A physician's own historical data is validated to generate a unique profile for each physician. Interactions with the physician are analyzed to understand decision-making patterns. The physician's profile is updated and future physician decisions are forecast based on past behavior.

Described herein are methods of managing eosinophilic esophagitis (EoE) in a patient, wherein the patient records the number of episodes of dysphagia on a patient report outcome (PRO) questionnaire prior to and during the course of treatment. While on treatment, the number of episodes of dysphagia are reduced, as reported on the PRO questionnaire. In some embodiments, the esophageal eosinophils are counted before or after treatment, or both. In some embodiments, esophageal eosinophils counts are not correlated with a reduction in the episodes of dysphagia.

Systems and methods for de-identifying patient data are disclosed herein. In some embodiments, a method for de-identifying patient data includes receiving a patient record including one or more identifiers. The method can include generating a first de-identified record from the patient record using a first de-identification process. The first de-identification process can be configured to produce a first re-identification risk score. The method can further include receiving a request from a data recipient to access the first de-identified record. The method can also include generating a second de-identified record from the first de-identified record by using a second de-identification process. The second de-identification process can be configured to produce a second re-identification risk score lower than the first re-identification risk score.

The invention provides a construction method of a coronary heart disease risk prediction model based on deep learning, and the method comprises the following steps: S1, obtaining examination data of patients recorded in a hospital, and respectively obtaining 12-lead electrocardiogram data of a coronary heart disease patient and a non-coronary heart disease patient in an HL7 format according to a coronary angiography examination result; s2, preprocessing the acquired electrocardiogram data; s3, dividing the preprocessed electrocardiogram data into a training set, a verification set and a test set; constructing a ResNet-LSTM integrated model, inputting features extracted by the ResNet neural network into LSTM for classification, and inputting electrocardiogram data into the model for training and verification by adopting ten-fold cross validation to obtain a trained model and a verification result; s4, optimizing parameters of the trained model based on a verification result; and S5, performing coronary heart disease risk prediction on the optimized integrated model by using test set data. The space and time information of the electrocardiogram can be fully captured, and more accurate coronary heart disease risk prediction is realized.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.

A viewing support system in which an information processing terminal on which an electronic patient record is displayed and an electronic patient record management server which acquires information pertaining to a sticky note attached to the electronic patient record are connected by a network, wherein the electronic patient record management server includes: a sticky note management unit which manages sticky note information indicating the position of a sticky note attached to an electronic patient record, and displays a sticky note created at the position of the sticky note specified by the sticky note information such that the sticky note can be viewed by another user different from the user who created the sticky note; and a supplementary patient record generation unit which generates a supplementary patient record summarizing the electronic patient record and the sticky note information on the basis of the electronic patient record and the sticky note information.

Described is a system for receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, processing data corresponding to the disease specific treatment algorithm using a Large Language Model (LLM), receiving one or more CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, receiving an update to a patient record of the patient from the first EHR system, based on the update to the patient record and the one or more CQL models, triggering a Clinical Decision Support (CDS) hook to generate an alert, and causing transmission of the alert for a medical practitioner associated with the first EHR system.

A system and method of property collection management and architecture that can organize patient data while maintaining context is presented. The present disclosure provides for a database system implementing a ‘Property Collection’ as a collection of ‘property name’ and ‘property value’ pairs, which are used to describe a ‘data entity.’ Further, the present disclosure can provide for historical access to data to track data changes. The present disclosure solves the technological problem of accessing data having differing data for the same person. The immutable electronic health record data storage system solves the aforementioned technological problem by providing a blockchain, transaction blockchain API, serialization module, and a deserialization module, among others that can format, process, and store data in a Global Patient Record (GPR).

To provide a photographing support device capable of efficiently collecting photographs of an affected part while suppressing a load on a doctor.SOLUTION: The medical image processing apparatus includes a registration unit that registers a disease name of an imaging target, an acquisition unit that acquires medical information of a patient recorded in an electronic medical record, a determination unit that determines whether the disease name of the patient acquired by the acquisition unit matches the disease name of the imaging target registered by the registration unit, and a notification unit that performs notification for prompting a user to image an affected part in a case where the determination unit determines that the disease name of the patient matches the disease name of the imaging target.SELECTED DRAWING: Figure 2

Systems and methods herein provide for access control to information in electronic patient records. One method includes receiving a request from an entity for access to one or more of a plurality of electronic patient records, the records having been machine learned to identify patient information in the electronic patient records including personally identifiable information and protected health information. The method also includes determining a level of access of the entity, retrieving, from a database, the one or more electronic patient records requested by the entity, applying a rule to the retrieved one or more electronic patient records based on the determined level of access of the entity to mask, encrypt, show, etc. one or more elements in the one or more electronic patient records. In response to applying the rule to the retrieved one or more electronic patient records, the electronic patient records are transferred to the entity.

The invention provides a document classification method and device based on a large model, an intelligent agent and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing, large models and the like. The method can be applied to scenes of enterprise internal document management based on artificial intelligence, patient record management of medical institutions, news classification, social media topic classification, spam filtering, medical literature classification, legal document classification and the like. According to the specific implementation scheme, an input document is obtained; determining the document type of the input document, wherein the document type is divided into a streaming document and a format document; generating corresponding target prompt word information according to the document type; document classification is conducted on the input document based on the target prompt word information through the multi-modal large model, the category of the input document is obtained, and the category of the input document refers to the semantic category of the input document. The prediction performance of the document classification system can be improved, and the prediction time can be shortened.

The present invention relates to a computer implemented method for data cleansing. In the method, a data pre-processing module of a first computing instance (3) obtains an at least two-dimensional un-shuffled data structure (10). The data structure (10) comprises healthcare patient data. The data structure (10) comprises data on a plurality of patient records. Each patient record comprises one or more fields wherein each field has a field type and wherein the patient records have a pre-shuffled order. The data pre-processing module of the first computing device shuffles fields of a first field type by moving fields having the first field types from one patient record to another patient record. Fields with the first field type have a post-shuffled order after the shuffling thereby creating a shuffled data structure(lO). A communications module transmits (5) the shuffled data structure (10) comprising the healthcare patient data over a communications network (7) from the first computing instance to a second computing instance.

A system for managing liver disease, the system comprising an electronic medical record system comprising a plurality of patient records. The system comprising a patient management platform configured to access the electronic medical record (EMR) system to retrieve medical records of one or more patients, analyze the medical records to selectively identify data related to a liver condition, interface with one or more data analysis platforms configured to receive and analyze the data related to a liver condition. Based on analysis by the patient management interface and / or the data analysis platforms, a display is generated comprising: specific liver-related patient attributes and conditions, and at least one of: a liver health prognosis or a recommended treatment for addressing the liver condition corresponding to the one or more patients. The liver condition can comprise one or more of: cancer, cirrhosis, liver failure, fatty liver disease, steatosis, ischemia, and / or hepatitis.

A method that includes receiving patient-generated event data over a network from a patient device associated with a patient having an active digital therapy prescription for treating an underlying disease or disorder. The patient-generated event data is encrypted by the patient device and includes at least one timestamped event related to the active digital therapy prescription. In response to receiving the patient-generated event data, the method includes decrypting, anonymizing, and storing the anonymized patient-generated event data on memory hardware. The method further includes receiving a patient record request over the network from a healthcare provider (HCP) system that requests the patient-generated event data and includes an authentication token. In response to receiving the patient record request, the method includes retrieving and encrypting the anonymized patient-generated event data from the memory hardware using the authentication token. The method also includes transmitting the encrypted patient-generated event data to the HCP system.

A system for predictive monitoring of patient deterioration by temporal modeling of electronic health records, the system comprising: a housing defining an enclosed volume; a plurality of input interface units arranged on the housing and configured to receive electronic health data including physiological signals, laboratory measurements, and clinical observations from external medical devices; an analog-to-digital converter unit electrically connected to the input interface units and configured to convert received signals into digital data streams; a signal conditioning unit operationally connected to the analog-to-digital converter unit and comprising filtering, normalization, and scaling circuits configured to standardize heterogeneous data into a uniform data representation;a temporal buffer unit consisting of a plurality of storage registers arranged in a sequential configuration, wherein the temporal buffer unit is configured to store time-indexed data samples over a predefined observation interval to generate a temporally ordered data structure; a synchronization unit configured to generate clock signals and align incoming data streams into synchronized timeframes before storage in the temporal buffer unit; a matrix transformation processor electrically coupled to the temporal buffer unit, wherein the matrix transformation processor comprises parallel arithmetic circuits, including multipliers, adders, and accumulators, arranged to perform weighted transformations on the temporally ordered data structure to generate temporal feature representations;a storage unit with non-volatile memory elements configured to store weighting parameters corresponding to the temporal characteristics of physiological data; a sequence evaluation unit operationally connected to the matrix transformation processor, comprising comparator circuits, gradient calculation circuits, and threshold evaluation registers configured to determine temporal deviations, rate-of-change characteristics, and cumulative variations indicative of a deterioration in the patient's condition; a decision output unit configured to generate a deterioration prediction signal based on the outputs of the sequence evaluation unit, the decision output unit further comprising a display unit, an audible alarm unit, and a communication unit for transmitting alarm signals;and an energy management unit configured to supply regulated electrical power to all units within the enclosure, wherein the system is configured to continuously process temporally ordered electronic health data to generate predictive indicators of deterioration in patient condition in real time.

Various aspects described herein relate to systems and methods for merging records from multiple database systems. A method may include comparing, by one or more processors, two or more records of a plurality of records. The plurality of records may be from a plurality of database systems. Each of the plurality of records may be a patient record or an employee record. The method may also include determining, by the one or more processors, that a threshold match exists between the two or more records based upon a set of threshold matching criteria. The method may also include merging, by the one or more processors, the two or more records, based upon determining that the threshold match exists, into a person database entity. A unique entity identifier may be associated with the person database entity.

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.

This invention relates to the field of medical information and discloses a method for the digital archiving and management of historical radiology images. The method includes the following steps: acquiring and digitizing historical radiology film images and associated text information; generating enhanced digital archives with standardized metadata and establishing patient associations with existing image archiving and communication systems; adaptively generating spatially standardized mapping representations at different levels based on evaluation results; performing context-aware depth comparison and quantitative analysis of historical and current images during clinical retrieval; and generating structured reports based on the results of the depth comparison and quantitative analysis. By using intelligent graph matching and Bayesian inference with the deep semantic features of historical images and information from the existing system, high-precision and automated association between historical images and current patient records is achieved. Furthermore, by constructing hierarchical spatial mapping representations, the transformation of historical images from static archives into computable and quantifiable intelligent assets is realized.

The present invention relates to the field of medication information recording, and specifically to a clinical medication data recording method and system. The method first obtains clinical medication information of different dimensions and the patient's illness time for each patient, and quantifies the clinical medication information to obtain the medication information coding value of each dimension for each patient. Based on the difference in medication information coding value of the same dimension for each patient, the symptom similarity of each patient is obtained. Based on the difference in the symptom similarity of the patients, the patients are clustered, and the patient record priority is obtained based on the difference in symptom similarity of the patients in the cluster and the overall level of symptom similarity, the number of patients, and the difference in illness time; the dimensional record priority is obtained based on the distribution of medication information coding values ​​of the same dimension for all patients; and the patient record priority and dimensional record priority are combined to record the patient's clinical medication information. The present invention can improve the efficiency of clinical medication data recording for a large number of patients.

In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Systems and methods for a virtual sonography team are described. An ultrasound system includes an ultrasound scanner that is configured to generate ultrasound data based on reflections of ultrasound signals transmitted by the ultrasound scanner and communicate the ultrasound data over a communication network to at least one display device and an archiver. An ultrasound machine is coupled to the ultrasound scanner and is configured to determine examination data for the ultrasound examination and communicate the examination data to the archiver over the communication network. The display device is configured to generate an ultrasound image based on the ultrasound data as part of an ultrasound examination and communicate the ultrasound image to the archiver for aggregation with the examination data into a patient record of the ultrasound examination.

In an illustrative embodiment, a system for providing automation and virtual assistance to medical claims processing includes a predictive analytics platform configured to receive patient information for a medical claim from a claims processing system, cross-reference the patient information with stored data to identify a patient record, apply the patient information to machine learning classifier(s) to estimate a likelihood of match between the patient information and the patient record, provide patient record information to the claims system, receive claims data from the claims system, access, from a data universe, requirements corresponding to a payer corresponding to the medical claim, the requirements having been generated through training machine learning classifier(s) with claims data corresponding to claims denied by the payer, verify the claims data in view of the requirements, and provide an indication of missing claims information and / or invalid claims information to the claims system.

The invention provides an internal threat identification and early warning method based on behavior analysis, and the method comprises the steps: analyzing the matching degree of the record of a query object unassociated patient and the retrieval range exceeding the responsibility authority for a classified behavior feature subset, and carrying out the comparison through a pre-established field responsibility authority library, if the query time is concentrated in a non-working period or the retrieval time presents a periodic rule, judging that the behavior deviates from an expected range, and determining a preliminary abnormal behavior mark; and for the deep behavior analysis result, analyzing the similarity between the rare disease category related to the query content and the specific medical history focused by the retrieval content, and if the retrieval range exceeds the responsibility authority and the query behavior lacks the business context, determining the classification label of the illegal behavior to obtain the illegal behavior judgment conclusion.

The disclosure provides systems, methods and machine-readable programs for isolation of data. In some implementations, this is performed on a healthcare information system (HCIS). It will be noted, however, that the disclosed embodiments can be used for different fields of endeavor, and for data other than medical patient data. After capturing data elements, such as patient records, the system automatically reviews and can extract the data elements in an isolated location, generates and stores reports, encrypts the reports, and sends them to multiple designated workstations and devices throughout a network at regular intervals to ensure that the most recent patient data is captured. After a compromising event, such as a system outage or a cyberattack, the updated patent data can be accessed locally by way of a locally installed client program.

The invention provides a personalized rehabilitation scheme intelligent generation method and system, and relates to the technical field of rehabilitation treatment, and the method comprises the steps: collecting multi-modal data of a patient, including skeleton joint point data, motion sequence data, physiological data and electronic medical record data, and carrying out the fusion to generate a structured file; calculating a first motion quality score based on the articulation point skeletal data; calculating a second exercise quality score based on the physiological data and the exercise sequence data; fusing the two types of scores through a modulation sorting average method to generate a comprehensive exercise evaluation report; then, in combination with patient archives and expert resources, a multi-objective optimization model with the maximum rehabilitation effect and the minimum resource cost as objectives is constructed, an optimization solver is used for solving, and a personalized rehabilitation scheme is generated; and finally, the personalized rehabilitation scheme is deployed to a remote rehabilitation platform, and closed-loop optimization control is realized by combining real-time data dynamic adjustment.

Embodiments can enable coordinated access to electronic medical records (EMRs) by mobile devices in communication with an EMR coordination server over a communications network, thereby integrating the EMR access into the physician's workflow. The EMR coordination server can receive a schedule request for a physician via the mobile device, and can identify appointments associated with the physician. Each appointment can be associated with a patient, an EMR server identified as having the patient's records, and a network location and EMR schema. The physician can select an appointment to access associated fields. The fields can be populated with fresh data from the identified EMR server, accessed by its network location, and mapped according to its EMR schema. Field data can be updated by physician dictation (or otherwise) and dictated audio can be automatically transcribed. Embodiments can operate even in context of multiple EMR servers having multiple EMR schemas and / or network locations.

A communication that is associated with a subject is accessed via an interface. One or more clinical concepts are determined based on the communication by using natural language processing and an ontological knowledge graph, where the ontological knowledge graph defines semantics, constraints, and relationships between a plurality of terms. An electronic health record is queried using the one or more clinical concepts. In response to the query, a set of records corresponding to the one or more clinical concepts from the electronic health record is received. An output is generated based on the set of records by using a generative artificial intelligence (GenAI) model, where the output comprises a summary of the set of records, a trend, or a categorization of information of the set of records. The interface is updated with the output.

Described is a system for receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, processing data corresponding to the disease specific treatment algorithm using a Large Language Model (LLM), receiving one or more CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, receiving an update to a patient record of the patient from the first EHR system, based on the update to the patient record and the one or more COL models, triggering a Clinical Decision Support (CDS) hook to generate an alert, and causing transmission of the alert for a medical practitioner associated with the first EHR system.