59 results about "Patient database" patented technology

Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.

A system having a processor and memory coupled to the processor is provided. The memory stores a central testing database having a plurality of test configuration profiles and a central patient database. A first communications interface coupled to the processor receives a request from a test device for at least one of the plurality of test configuration profiles and sends the requested test configuration profile to the test device. The first communications interface also receives a test result collection instance from the test device. The processor is configured to store the test result collection instance in the central patient database.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

A system for an automated dental patient prioritization and treatment processing based on dental patient-related data, including a processor of a dental patient processing server node configured to host a machine learning (ML) module and connected to an office manager-entity node and to at least one dentist entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a dental patient report including a list of dental procedures prescribed to a dental patient from the office manager-entity node; parse the dental patient report a to derive a plurality of key ordered features; query a local dental-patient's database to retrieve local historical dental-patients'-related data based on the plurality of the key ordered features; generate at least one feature vector based on the plurality of the key ordered features and the local historical dental patients'-related data; and provide the at least one feature vector to the ML module configured to generate a predictive model for producing at least one dental-patient processing recommendation parameter; and generate at least one procedure performance recommendation and dental patient prioritization based on the at least one dental-patient processing recommendation parameter.

The present invention is a healthcare service estimating system including a patient application, enabling users to input healthcare service requirements, search for providers, request quotes, and review estimates. A provider application enables healthcare providers to manage profiles, respond to quote inquiries, and configure automated responses for frequently requested services. A server system, communicatively coupled to the applications, incorporates a patient database, a provider database, an automated quote module for generating good faith estimates, and an application server for managing data and interactions. The system is adapted to adjust estimates based on insurance coverage. The system offers real-time cost transparency, regulatory compliance, and empowers patients to make informed decisions and promotes efficient provider interactions.

Systems and methods for assigning labels to medical images are provided. One or more prompts comprising instructions for assigning labels to medical images are received. The labels are assigned to the medical images using a large language model based on 1) the instructions and 2) patient data stored in a plurality of patient databases. The assignment of the labels to the medical images is output.

The invention provides a multi-modal fusion Bayesian medical auxiliary diagnosis method and system, belongs to the technical field of medical auxiliary diagnosis, and is used for solving the problems of inquiry redundancy, insufficient adaptation of small sample / emergency treatment / rare disease scenes and large disease probability calculation deviation in related technologies. According to the method, a knowledge graph containing disease-symptom exclusion weight and a patient database are constructed, intelligent inquiry is combined with regional correction, core symptoms are screened through real-time evidence, the disease probability is calculated through multi-modal data processing and Bayesian reasoning, and tool collaborative verification and scene adaptation optimization output are carried out. The system comprises a core calculation unit, a multi-modal acquisition unit, a database unit, a detection interaction unit and a result display unit for supporting method execution. The method can improve the inquiry efficiency and diagnosis precision, is suitable for multiple clinical scenes, and provides reliable assistance for doctors.

The invention relates to a computer-implemented method and system for automatically providing disease information relating to verification of a suspected diagnosis, comprising the following steps: providing at least one disease database, which contains a plurality of disease patterns relating to one or more diseases, and at least one patient database, the patient database comprises a plurality of electronic patient files with patient information; extracting a disease pattern for diagnosing a disease of the patient from at least one provided disease database based on the suspected diagnosis; matching the extracted sick pattern with patient information of an electronic patient profile of the patient loaded from a patient database by using a mathematical model for automatically determining at least one piece of disease information for suspected diagnosis; and outputting the determined disease information via the first user interface.

A system for predicting clinical trial participation may comprise a participation probability (PP) server connected to a machine learning (ML) module. The PP server may receive clinical trial (CT) parameters from a user device. The PP server may query a patient database using the CT parameters to identify potential participants. The PP server may collect social media data for the potential CT participants. The PP server may generate feature vectors based on the social media data. The PP server may provide the feature vectors to the ML module and receive participation probability index (PPI) scores for the potential CT participants from the ML module. The PP server may generate a ranked list of likely CT participants based on the PPI scores. The PP server may provide targeted content to potential participants with low PPI scores. The PP server may update the PPI scores based on engagement with the targeted content.

According to one embodiment, a method of designing, selecting, dispensing custom finished abutments or performing custom finished abutment minor surgery by a plurality of custom finished abutment sets designed or manufactured based on a patient database is disclosed, the method comprising the steps of: provisioning a plurality of custom finished abutment sets based on a patient database; picking up patient oral data; selecting a suitable abutment among the plurality of custom finished abutment sets using software based on the picked up patient oral data; and dispensing the selected custom finished abutment or performing custom finished abutment minor surgery.

The invention relates to a particularly flexible, resource-sparing system (110) that is attuned to patient interests and serves to process patient-specific data and treatment-specific data in a medical treatment center (111) that has a plurality of medical treatment devices (113), which may be used by a plurality of patients (107) and may be occupied at assigned treatment times, said system comprising: • a presence detection system (114) for acquiring presence data containing current presences or absences of patients (107) and medical staff (106) in the treatment center (111), • a status detection system (115) for acquiring status data containing a current device status for the respective treatment device (113), the status data containing a value that is preferably updated continually, characteristic for the duration or remaining duration of a planned or already started treatment and provided by the respective treatment device (113), • a report system (116) for acquiring procedure data containing delays and / or requirements relevant to the treatment, reported by the patients (107) and / or by the medical staff (106), • a patient database (117) having patient data that contain a treatment regime for the respective patient (107) and used to control the respective assigned treatment device (113), and • an electronic data processing unit (109) with a scheduler (118) which, taking account of presence data, status data, optionally procedure data, and / or patient data, independently and in automated fashion creates and / or updates and / or optimizes a schedule containing an assignment of treatment devices (113), patients (107) and treatment times and optionally the medical staff (106) to one another, or makes an appropriate suggestion.

The embodiments disclose a data processing cost and price determination platform with electronic negotiation, including a mobile app configured to register a medical patient, a medical database configured to retrieve patient demographics, patient medical necessity codes and patient billed account data related to the medical patient of a medical provider, a server configured to electronically collect updated patient medical pricing data from governmental and health insurance carrier sources and medical necessity standards based on demographics of the medical provider, a processor configured to compare the patient billed account with the updated medical pricing data to determine a suggested fair market of the patient billed account, an electronic negotiation module to submit the suggested fair market of the patient billed account data to the medical provider for soliciting a negotiated settlement rate and a communication module to transmit the accepted negotiated settlement rate from the medical provider to the medical patient.

A system and method for automated imaging protocol formulation are provided. The system receives patient data from one or more patient databases. Using a first machine learning model, imaging protocol formulation information is extracted from the patient data. Using a second machine learning model, an imaging protocol template is selected from multiple candidate templates based on the extracted information. Using a third machine learning model, the selected template is fine-tuned based on the patient data. The fine-tuned template is output.

Systems and methods for automatic imaging protocoling are provided. Patient data from one or more patient databases is received. Imaging protocoling information is extracted from the patient data using a first machine learning model. An imaging protocol template is selected from a plurality of candidate imaging protocol templates based on the extracted imaging protocoling information using a second machine learning model. The selected imaging protocol template is fine-tuned based on the patient data using a third machine learning model. The fine-tuned imaging protocol template is output.

The invention discloses a patient disease data management system, and relates to the technical field of medical information, the patient disease data management system comprises a data management module, a patient symptom module and a communication sharing module, the data management module is in communication connection with the patient symptom module, and the patient symptom module is in communication connection with the communication sharing module; the data management module is used for managing a database and completing normal database operation, the patient symptom module is used for storing and updating symptom data in a patient database, and the communication sharing module is used for managing a mode of communicating a treatment scheme and a conditioning means for a patient. The data management module comprises a database transmission module and a patient self-recording module, the patient self-recording module is in communication connection with the database transmission module, and the patient file module is in communication connection with the symptom filing module; and the database transmission module is used for registering a newly registered patient in the database in the medical system. The system and the method have the characteristic of accurate matching.

A patient centered social platform according to systems, methods, and apparatuses of the present disclosure may include a patient database module configured to interpret at least one of a plurality of patient profile values corresponding to a user of the patient centered social platform, a stakeholder database module configured to interpret a plurality of stakeholder service values, and a patient interface module configured to implement a user social interface and to provide a patient engagement communication to the user on the user social interface in response to the corresponding at least one of the patient profile values and the plurality of stakeholder service values.

A system for generation of medical recommendation based on patient-related data, including a processor of a medical recommendations server (MRS) node configured to host a machine learning (ML) module and connected to a patient entity node and to at least one medical emergency entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sensory data from a plurality of biosensors encapsulated into a patient wearable device; acquire patient data from a mobile device of the patient, wherein the patient data may include video data and audio data; parse the sensory data to derive a plurality of key classifying features; extract a plurality of indicators from the patient data based on the plurality of key classifying features; query a local patients'database to retrieve local historical patients'-related data related to previous patients'engagements associated with previous medical recommendations based on the plurality of key classifying features and the plurality of indicators; generate at least one feature vector based on the plurality of key classifying features and the plurality of indicators and the local historical patients'-related data; and provide the feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of medical recommendation parameters from a medical recommendation predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate medical recommendations based on the plurality of the medical recommendation parameters.

A CDS system including a controller extracting EHRs of an IHG and selecting SMs to form a target patient database, capturing a time interval between each subsequent record for each of the measurements defined by the SMs, computing hospital level features including statistics on the distribution of the captured time intervals for each of the measurements, clustering the plurality of hospitals of the IHG in accordance with the computed hospital level features, determining whether an other hospital is within a threshold distance to a closest one of the plurality of clusters; placing the other hospital in the closest cluster when it is determined that the other hospital is within the threshold distance, and placing the other hospital in a new cluster when it is determined that the other hospital is not within the threshold distance.

A healthcare management system and method provide efficient and secure access to private information. A portable physical device, referred to herein as a Personal Digital Key or “PDK”, stores one or more profiles in memory. The biometric profile is acquired in a secure trusted process and is uniquely associated with an individual that is authorized to use and is associated with the PDK. The PDK can wirelessly transmit the identification information including a unique PDK identification number and the biometric profile over a secure wireless channel for use in an authentication process. The PDK is configured to wirelessly communicate with a reader. A provider interface coupled to the reader, and the reader is further configured to receive profile information from the PDK. The healthcare management system also includes an auto login server configured to communicate with the provider interface to allow access to information in a patient database.

This invention discloses a method for predicting the risk of complications after esophageal surgery. The method includes: acquiring multi-parameter monitoring data collected at fixed time intervals during postoperative monitoring of patients after esophageal surgery, generating a multi-parameter time series dataset; based on a database of historical patients with no complications, grouping and matching historical patients according to age group, surgical type, and surgical duration, calculating the mean and standard deviation of each parameter at each time point after surgery for each group, and generating a standard recovery trajectory reference template containing the expected value and normal fluctuation range of each parameter at each time point after surgery; calculating the difference between the measured values ​​of each parameter of the current patient at each time point and the expected values ​​at the corresponding time points in the standard recovery trajectory reference template, and standardizing the difference by dividing it by the corresponding standard deviation; this invention achieves dynamic postoperative assessment of the risk of complications after esophageal surgery and can capture early abnormal patterns of slight deviations in multiple parameters.

The present application provides a defibrillator. The defibrillator is used to analyze electrocardiogram (ECG) data to identify whether it is beneficial for a caregiver to apply an electric shock to the heart in an effort to restore the heart to a normal pattern and resume a sustained strong beat. By continuously checking conditions that have been pre-verified using a comprehensive patient database as having a high predictive value (e.g., having a low false positive rate), a shockable rhythm can be identified quickly (e.g., in less than 6 seconds, in less than 3 seconds, possibly in less than 1 second) and without having to analyze the ECG data in a longer time period than would otherwise be required using conventional methods.

The invention discloses a chronic disease intelligent management system based on multi-modal data fusion, and relates to the technical field of intelligent management, and the system comprises a data collection and preprocessing module which is used for collecting electronic medical record data, historical medication data and living habit data, and constructing a patient database; the cross-domain knowledge fusion module is used for fusing multi-domain related medical data and a knowledge representation technology and constructing a cross-domain medical knowledge graph; the medication plan generation module is used for analyzing the physiological monitoring data, collected in real time, of the patient and generating a personalized medication plan based on the medical knowledge graph; and the dynamic optimization module is used for optimizing the personalized medication scheme by combining the medical knowledge graph according to the medication feedback data and the real-time physiological monitoring data of the personalized medication scheme. According to the method, by adopting ontology mapping and semantic similarity calculation, deep alignment of traditional Chinese medicine knowledge and modern medical knowledge is realized, and the risk of treatment scheme conflict caused by knowledge splitting is reduced.

In an example embodiment, a system, a method, and an apparatus are provided that include an application, configured for: creating a subjective, objective, assessment, and plan (SOAP) report, the creation includes: using a voice-to-text converter to turn a free-form verbal conversation with a patient into a text stream, the text stream is enhanced with metadata from a database to create an enhanced text stream that forms a basis for the SOAP report. Other examples can provide that the metadata includes data from a previous SOAP report, and data associated with a prevision visit of the patient. Still other scenarios can provide that the enhanced text stream is sent to a large language model (LLM) to automatically create a practitioner-specific SOAP report. The SOAP report is structured according to a practitioner's specific requirements, and the SOAP report is automatically entered into a patient database for subsequent access.

The invention discloses a cardiac surgery patient postoperative cardiopulmonary rehabilitation training guidance and supervision system based on an applet, and belongs to the technical field of postoperative cardiopulmonary rehabilitation training. The platform end comprises a patient database, an online service module and a data analysis module; the patient database is used for counting patient rehabilitation data of the target patient in real time; the online service module is used for analyzing medical care resources of a hospital and establishing a medical care guidance information base; the data analysis module is used for analyzing the patient rehabilitation data in the patient database, obtaining a rehabilitation analysis report of the target patient and sending the rehabilitation analysis report to the corresponding user side; the user side comprises a data acquisition module and a guidance module; the data acquisition module is used for acquiring patient detection data of a target patient in real time and sending the patient detection data to the user side; and the guidance module is used for performing medical guidance on the target patient, and the target guidance personnel guide the target patient according to a preset communication mode.

A particularly flexible, resource-saving and patient-interest-oriented system (110) for processing patient- and treatment-specific data in a medical treatment center (111) with a plurality of medical treatment devices (113) that can be visited by a plurality of patients (107) and used for assigned treatment periods, comprising: • an attendance recording system (114) for recording attendance data containing the current presence or absence of patients (107) and medical staff (106) in the treatment center (111), • a status recording system (115) for recording status data containing a current device status for the respective treatment device (113), • optionally, a reporting system (116) for recording expiration data containing delays and / or treatment-related requirements reported by patients (107) and / or medical staff (106), • a patient database (117) with patient data containing a treatment regime for the respective patient (107), which is used to control the respectively assigned treatment device (113), and • an electronic data processing unit (109) with an appointment planner (118) which, taking into account attendance data, status data, if applicable expiry data, and / or patient data, automatically creates and / or updates and / or optimizes an appointment plan containing an allocation of treatment devices (113), patients (107) and treatment periods as well as, if applicable, the medical personnel (106) to one another, or makes a corresponding suggestion

The present application discloses a method and device for establishing a classification prediction model for the prognosis outcome of cesarean scar pregnancy. The method for establishing the classification prediction model for the prognosis outcome of cesarean scar pregnancy includes: obtaining a patient database, where the patient database includes at least one patient data; classifying the patient data in the patient database according to the treatment method and prognosis, so as to obtain the first type of patient data and the second type of patient data; establishing a model for the first type of patient data based on the first type of patient data; establishing a model for the second type of patient data based on the first type of patient data, the second type of patient data, and the third type of patient data. The present application utilizes ultrasound and clinical features, combined with a machine learning model, to establish a more accurate classification model.

A system and method are described for analyzing electronic data records received from a patient database. Analyzing the electronic data records may include identifying one or more common care gaps shared between a group of patients having a common value in a data field of the patient's corresponding electronic data record. The identification of the common care gap can be used to identify treatments that are not being provided to the group of patients, but should be provided to the group of patients.

The following relates generally to solutions built utilizing LLMs, generative Al, and ML- based methods to facilitate recommendations for the appropriate genetic test for a patient. In some embodiments, one or more processors: (1) receive a patient identifier (ID) of the patient and test code identifier of the genetic test ordered for the patient; (2) extract from multiple databases, previous testing histories, relevant concurrent tests, family history, internal notes, reason for testing, as well as other data elements associated with the ordered test to be evaluated; (3) extract from a patient database, patient features based on the patient ID; (4) generate a condensed phenotypic description of the patient using contextualized targeted clinical documents; (5) generate a list of relevant phenotype ontology terms based on the condensed phenotypic description of the patient; and (6) enable genetic test validation of the existing test order(s) or inform new genetic test recommendations.