142 results about "Patient record" patented technology

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 computer system, computer program product and method for determining a probability of attaining a PK-PD target associated with efficacy for a patient that includes a processor(s) determining that a trigger event related to a patient, wherein the patient record comprises an order for a current drug regimen, has occurred in an electronic health record (EHR) system communicatively coupled to the one or more processors. The processor obtains descriptive information relating to a patient. The processor selects a pharmacokinetic model. The processor applies the pharmacokinetic model and utilizes the information relating to the patient to determine, for each of the one or more drug therapies, a probability of attaining a PK-PD target associated with efficacy for the patient with the infection. The processor generates rankings for the drug therapies and determines if the current drug regimen comprises a probability above a threshold. The processor generates a new order.

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.

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.

Systems, methods and interfaces are provided for processing natural language queries. A machine learning-powered search interface may process complex natural language queries across specialized domains. The method may utilize a transformer-based natural language processing model that analyzes user intent by examining both current and historical user inputs. A graph data structure may be used to represent interconnected domains like patient records, insurance claims, and business metrics enables dynamic information retrieval. The system may use an A* search algorithm to navigate domain nodes and identify relevant answers. An abbreviation expansion mechanism resolves technical shorthand by referencing domain-specific mapping databases, ensuring precise interpretation of user queries. The method may identify keywords, traces optimal answer pathways, and adapt by updating graph relationships and abbreviation mappings. By generating contextually rich natural language summaries, the system delivers precise, comprehensive responses through an intuitive search interface.

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.

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.

The invention relates to the field of medication information recording, in particular to a clinical medication data recording method and system. The method comprises the following steps: firstly, acquiring clinical medication information of each patient in different dimensions and disease time of the patient, quantifying the clinical medication information, acquiring a medication information coding value of each patient in each dimension, acquiring disease similarity of each patient according to the difference of the medication information coding values of each patient in the same dimension, and determining the disease similarity of each patient according to the disease similarity. Clustering the patients based on the difference of the disease similarity of the patients, and obtaining patient record priorities according to the difference of the disease similarity of the patients in a cluster, the overall level of the disease similarity, the number of the patients and the difference of disease time; according to the distribution of the medicine use information coding values of the same dimension of all the patients, dimension record priorities are obtained; and in combination with the patient record priority and the dimension record priority, recording clinical medication information of the patient. The efficiency of recording clinical medication data of a large number of patients can be improved.

A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

Described herein is a cohort engine that generates a cohort of patients with respect to a selected key patient record. The cohort engine receives, via a first user interface (UI), a key patient record. A patient-cohort generator is launched to determine an initial set of patient records that are to be included in the patient cohort. A second UI is configured to provide the initial set of patient records, where the second UI comprises at least three panes. Upon receiving, via the second UI, an input corresponding to at least one identifier being modified, an equation (i.e., query) is generated based on a combination of identifiers included in the second pane. An updated set of patient records is provided in a third pane of the second UI in response to the at least one identifier being modified.

A method and apparatus for a care management platform and a user device interacting during the providing of real-time provider intelligence information are described. The method includes receiving, from the user device, a request for patient records relevant to a patient, the request comprising a patient identifier (ID), a care provider identifier (ID), and a care provider context. The method also includes querying a patient data store for patient records based on the patient ID, care provider ID, and care provider context, and performing two or more different types of filtering operations of the returned results to generate a final reduced set of patient records. The method can also include transmitting, to the user device, a response comprising at least a subset of the final reduced set of patient records.

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.

A community electronic medical record provides access to a patient's records. A unity server of the present invention allows a user to enter a query requesting the information that a medical provider needs such as past history, allergies, billing information, and other medical data that may be related to the patient. The unity server queries at least one database, preferably many, to access the patient's records relevant to the provider's query. The unity server retrieves information from multiple sources to provide increased information for treatment of the patient.

A medical record robot based on an AI voice chip SMCU and a Chinese language model comprises a voice collector used for collecting doctor-patient dialogue data, the AI voice chip SMCU, an edge calculation box integrated with the Chinese language model and a data center, and the AI voice chip SMCU is connected with the voice collector and the edge calculation box; the voice collector, the AI voice chip SMCU, the edge computing box and the interaction device are all connected with the power supply module. The Chinese language model comprises MedSegNet, Input Embedding, Nx, Bert-BiLSTM-CRF (Conditional Random Access Function), a GCN (Graphics Convolutional Network) and GraphRAG (Graphics Register Architecture). The system can automatically record conversations between doctors and patients, extract and analyze structured information in the conversations, generate electronic medical records in real time, query past medical histories of the patients recorded in the system, and give corresponding treatment opinions for the doctors to refer to and modify. The busy work of writing medical records by doctors is greatly liberated, and the diagnosis and treatment efficiency is improved.

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.

Systems and methods for selecting a treatment regimen for a particular patient are provided. The treatment regimen is selected using a first data store including a first plurality of patient records for a first plurality of patients, and a second data store including efficacy rates of a plurality of treatment regimens against a plurality of infections and infection-causing pathogens. The system, or a user of the system, identifies a first infection of the particular patient and a first treatment regimen to be prescribed to treat the first infection. A second plurality of patient records is generated by identifying, in the first plurality of patient records of the first data store, patient records associated with diagnosis or treatment of the first infection. The second plurality of patient records are appended with efficacy rates for the first infection from the second data store. Systems and methods of the present disclosure can use these appended records to generate a dynamic model configured to determine a likelihood estimation that the first treatment regimen is an appropriate treatment regimen to treat the first infection.

Disclosed embodiments include computer-implemented methods and systems that can efficiently generate highly relevant recommended search terms to clinicians. A hybrid collaborative filtering model recommends search terms for a specific patient to the clinician. The model draws on information from patients' clinical encounters and the searches that were performed during the clinical encounters. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. One variation of the model uses only the most recent ICD codes assigned to the patient. Another variation uses all ICD codes. Comprehensive experiments of embodiments of the methods and systems have demonstrate high levels of performance.

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.

In some embodiments thereof, the present invention discloses systems and methods for expeditiously designing and fabricating properly-fitted dental prostheses. A customizable arch-shaped apparatus approximating dentition is employed intraorally to register positional and relational characteristics between maxillary and mandibular arches, including the vertical dimension of occlusion. The apparatus is configured to allow accurate interface with oral anatomy via its constituent material(s) being pliable and moldable preferably but not necessarily when heated. Representations of dentition may be integrated on the apparatus to aid proper positioning. Intraoral scan data detailing precise arch dimensions is either referenced from pre-existing patient records or procured through digital oral scanning of the apparatus in situ. The registered positional relationships and acquired scan data are jointly utilized to virtually model and subsequently manufacture bridgework, veneers or dentures; the apparatus allowing dentition to be modeled and fabricated in a singular office visit prior to a final, confirmatory try-in where minor adjustments may ensue.

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.

A digital healthcare ecosystem to transform patient care with real-time queue management, including: an appointment and laboratory test booking module configured to allow patients to easily book and schedule doctor consultations and diagnostic tests; a healthcare provider module configured to manage appointments, patient records, billing, internal laboratory and pharmacy integration, and operational analytics, with the healthcare provider module configured to manage dashboard features for physicians, hospital administrators, and laboratory technicians to monitor and update patient status; a real-time queue management module coupled to the healthcare provider module and the appointment and laboratory test booking module, wherein the real-time queue management module is configured to assign a digital token number to patients upon booking once accepted by the healthcare provider module, wherein the real-time queue management module is configured to update the queue position of patients using time-stamped data; a patient interface configured to allow patients to view real-time queue updates, book appointments, and access lab reports using the digital token number; a notification system configured to send notifications to patients and physicians regarding appointment reminders, queue progress, and follow-up instructions; a cloud-based server configured to securely store patient data and enable seamless communication between the patient interface and the healthcare provider module; a display unit configured to show estimated waiting times and notify patients when it is their turn; and a dynamic scheduling module configured to take into account physician availability, patient preferences, and historical data to minimize waiting times and avoid overbooking.

A system and method may monitor a stream of messages from plural different sources of medical records to identify a new or updated record of the medical records, identifying one or more suspect records from a system of record (SOR) database. The one or more suspect records are identified as being potential matches to the new or updated record that is identified. The system and method can determine that the one or more suspect records match the new or updated record, and can obtain a person entity profile related to the new or updated record from a person entity database. The system and method can merge the new or updated record with the one or more suspect records or splitting the new or updated record from the one or more suspect records based on the person entity profile that is obtained.

Systems and methods for selecting a treatment regimen for a particular patient are provided. The treatment regimen is selected using a first data store including a first plurality of patient records for a first plurality of patients, and a second data store including efficacy rates of a plurality of treatment regimens against a plurality of infections and infection-causing pathogens. The system, or a user of the system, identifies a first infection of the particular patient and a first treatment regimen to be prescribed to treat the first infection. A second plurality of patient records is generated by identifying, in the first plurality of patient records of the first data store, patient records associated with diagnosis or treatment of the first infection. The second plurality of patient records are appended with efficacy rates for the first infection from the second data store. Systems and methods of the present disclosure can use these appended records to generate a dynamic model configured to determine a likelihood estimation that the first treatment regimen is an appropriate treatment regimen to treat the first infection.

A method for processing a sparsely populated data source, method for generating a training set for training a model to predict mitral regurgitation from echocardiograph data, and method of predicting heart failure from echocardiograph data including the steps of: retrieving echocardiograph measurement data from a plurality of patient records comprising echocardiography reports; analysing the echocardiograph data to determine unpopulated data fields; populating the unpopulated data fields with imputed echocardiograph data determined by a machine learning model; calculating a probability output from a trained model; analysing echocardiograph measurement data of individual patient records from the echocardiograph data to determine a prediction of the presence of a disease state in the patient on the basis of the calculated probability output; and associating the presence of the disease state to a prediction of heart failure in the patient.

To provide a diagnosis and a prescription support system: having a large-scale language model additionally learn patient records of a great amount of diagnosed cases; having the additionally learned large scale language model propose an effective inspection plan for determining diagnosis, from symptom remarks obtained from a visiting diagnosis undetermined patient; support obtaining an established diagnosis by integrating the obtained testing results; and furthermore propose a suitable treatment plan on the basis of the established analysis.SOLUTION: An electronic medical chart system also combined with a large scale language model includes: "possible diagnosis term, additional symptoms, body remarks, and test answer means" for responding to at least one of a possible diagnosis name and a list of the probability, recommended additional symptoms, body remarks, and a test; and sequential diagnosis improvement means repeating "symptoms, physical remarks, and test result data provision means" and "diagnosis name with a possibility, additional symptoms, body remarks, test answer means" until the list of the diagnosis name and its probability has sufficient accuracy as a definite diagnosis.SELECTED DRAWING: Figure 5

A virtual health system is provided that creates a digital platform to connect patients to healthcare providers. Healthcare providers create profiles that describe their healthcare specialty and provide scheduling information that indicates their availability along with preferences such as appointment or meeting length, minimum break between appointment, and price. Patients create profiles that includes demographic information as well as medical records. The patients may use the system to search for available healthcare providers using a variety of criteria, and to schedule virtual health services such as video chats. The system facilitates payment for the healthcare services, and records any notes, assignments, or prescriptions generated by the healthcare provider during the services. In the event that a patient would like to engage a different healthcare provider associated with the system, the system only allows access to the patient records after receiving permission from the patient.

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.

In a local coil arrangement (5) for use on a patient bed (1) with a magnetic field sensor (6) for measuring the main magnetic field of a magnetic resonance device (16) in order to determine position information of the local coil arrangement (5) by comparison with a main magnetic field map, the following sequence of steps is proposed to check the correct function of the magnetic resonance sensor in a procedure: - Recording of the first measured value (12) of the magnetic field sensor (6) outside the patient recording (2) at the first measuring position of the local coil arrangement (5) at a first lying position (4) of the patient bed (1) and determination of the first position information, - Recording of a second measurement (15) of the magnetic field sensor (6) during or after completion of a movement of the patient bed (1) into the patient receptacle (2) at a second measurement position (13) of the local coil arrangement (5) at a second position (14) of the patient bed (1), which is different from the first measurement position (9) and is located in particular within the patient receptacle. - Determination of at least one reference value that describes the expected main magnetic field at the second measurement position (13), which is determined from the first position information and the displacement distance of the patient bed (1) between the first and the second bed position (4, 14) according to the position determination device (18), and - Determination of error information indicating a potential measurement error by comparing the second measured value (15) with the reference value.

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.