Methods and systems for enhanced natural language processing of clinical documentation are provided. Using natural language processing, a clinical condition is extracted from unstructured data within a current electronic document. A clinical ontology identifying itemsets associated with the clinical condition is retrieved, and indicators of relevant clinical concepts, as identified from the ontology, are searched from within the patient's longitudinal record, which comprises documentation from at least a prior encounter. Based on the whether the clinical concepts are present in the patent's record, a confidence is assigned to the NLP-extracted clinical condition, and one or more actions may be performed.
Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Computerized systems and methods are provided to intelligently and dynamically manage a data center comprising at least one server and at least one central manager. The central manager is programmed to access the at least one server on a predetermined schedule to determine whether at least one application is functioning properly by determining a functionality level. Alternatively, the central manager determines whether the at least one server is actively used by determining an activity level for the server. Based on the central manager's determinations, the system dynamically adjusts the power level of the server, resulting in reduced power consumption and a reduction in wasted resources and unnecessary processing power in the management of servers in a data center.
Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Embodiments herein disclose systems, methods, and computer-readable media for generating CDA (Clinical Document Architecture) documents from Past Healthcare Interoperability Resources (FHIR) APIs utilizing a unified, flexible, cloud-based service that can be leveraged across disparate solutions and/or systems.
G06F 16/25 - Integrating or interfacing systems involving database management systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
6.
PROXIMITY-BASED MOBILE-DEVICE UPDATES OF ELECTRONIC HEALTH RECORDS
A system, method, and computer-readable media are provided for facilitating clinical decision making, and in particular, decision making based on a third party's clinical situation by determining and providing useful, up-to-date information, such as patient-related information to a decision maker. In one embodiment, a user first identifies an information item concerning a patient. Based on that item, a set of related information items is determined and prioritized, and a reference pointer, which identifies the set of related information, is generated. The reference pointer is communicated to the user's mobile device. Subsequently, the user's mobile device requests information from the set of information items associated with the reference pointer, and provides information authorization information. Following authentication of the user's credentials, updates of information from the set of information items may be communicated to the user's mobile device as they become available.
G16H 10/65 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
G06F 16/90 - Details of database functions independent of the retrieved data types
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 50/22 - Social work or social welfare, e.g. community support activities or counselling services
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Methods, systems, and computer-readable media are provided for improving patient safety using virtual observation. A falls risk assessment and a patient safety risk assessment are initially provided within an electronic health record of a patient. A clinician is prompted at a clinician device to provide input to the falls risk assessment and the patient safety risk assessment for the patient. Based on the input, a safety assessment score is determined for the patient. The safety assessment score is provided to the clinician via the clinician device and the clinician is prompted to initiate an order to place a camera in the room of the patient. Based on the order, a virtual sitter may be assigned to the patient to monitor the camera.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
8.
PROXIMITY-BASED MOBILE-DEVICE UPDATES OF ELECTRONIC HEALTH RECORDS
A system, method, and computer-readable media are provided for facilitating clinical decision making, and in particular, decision making based on a third party's clinical situation by determining and providing useful, up-to-date information, such as patient-related information to a decision maker. In one embodiment, a user first identifies an information item concerning a patient. Based on that item, a set of related information items is determined and prioritized, and a reference pointer, which identifies the set of related information, is generated. The reference pointer is communicated to the user's mobile device. Subsequently, the user's mobile device requests information from the set of information items associated with the reference pointer, and provides information authorization information. Following authentication of the user's credentials, updates of information from the set of information items may be communicated to the user's mobile device as they become available.
G16H 10/65 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
G06F 16/90 - Details of database functions independent of the retrieved data types
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 50/22 - Social work or social welfare, e.g. community support activities or counselling services
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Methods, systems, and computer-readable media are provided for improving patient safety using virtual observation. A falls risk assessment and a patient safety risk assessment are initially provided within an electronic health record of a patient. A clinician is prompted at a clinician device to provide input to the falls risk assessment and the patient safety risk assessment for the patient. Based on the input, a safety assessment score is determined for the patient. The safety assessment score is provided to the clinician via the clinician device and the clinician is prompted to initiate an order to place a camera in the room of the patient. Based on the order, a virtual sitter may be assigned to the patient to monitor the camera.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
10.
System and Method for Predicting the Impact of Source Code Modification Based on Historical Source Code Modifications
Methods, systems, and computer-readable media are disclosed herein that utilizes historical changes made to files and methods in computer programming code to predict related files and methods that may be affected by current and/or future changes made to other files and methods. In aspects, when computer code for a particular method is going to be edited, other methods are identified that were changed in previous editing sessions that also included changes to the particular method. Using scoring techniques for the other methods, a recommendation is provided that details the relative strength of whether the other methods are predicted to be affected by any changes made to the computer code for the particular method that is edited.
A system, method and article of manufacture are presented for improving therapy such as adjustment of a chronotherapeutic pharmaceutical regimen. Physiological variables are measured longitudinally and a time series of the measurements is constructed. In some cases, a time series is pre-whitened and transformed to a frequency spectrum while applying multi-taper filtering, and entropy or other statistical measures are calculated for the power spectral distribution. Improved timing, medication and dosage are individually or collectively improved and/or verified through successive testing. An improvement is illustrated for hypertension, using medication to achieve autonomic control and to reduce blood pressure variability and to reduce spectral diversion.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/00 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data
G16H 40/00 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices
12.
System And Method For Generating Multi-Category Searchable Ternary Tree Data Structure
Systems, methods, and computer-readable media are disclosed herein that generate a ternary tree data structure that includes multiple categories (e.g., terminologies) using dynamic array modifications that facilitate sharing of one or more nodes across categories. A plurality of different categories may be added and stored within a single ternary tree data structure such that each categories may be separately queried using the single ternary data structure.
Systems and methods are provided for evaluating an alarm condition for a monitored patient in a population of one or more patients. One or more physiological parameters pertaining to the patient, or physiological variables, are used to form a time series describing the patient status. A quantile threshold is determined for a patient. A monitor is initialized and begins generating a binary raw alarm signal. The binary signal is filtered with a low pass filter, and the filtered data is subjected to a quantile operation to determine if a particular sample exceeds the determined quantile threshold. If the quantile threshold is exceeded, then a check is performed to see if the raw binary alarm signal also indicates an alarm. If both the preliminary alarm indication and the raw binary signal indicate an alarm, then an alarm is emitted; otherwise monitoring continues.
Methods, systems, and computer-readable media are provided for managing health status of persons with a chronic condition including providing dynamic, adaptive monitoring, detection, and prediction of suicide risk to a person at risk for suicide related to mental health. In an embodiment, a patient-assessment application is used to obtain information periodically on a patient's mental health status. Based on this information, a logistic regression model is employed to determine a patient's probability of attempting suicide. The probability is evaluated against a default threshold to determine if the patient's status has changed significantly, and if the threshold is exceeded, an action is evoked. In one embodiment, the action includes providing notice to the patient's care provider, caregiver, or case manager.
G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
15.
ACTIVE MANAGEMENT OF FILES BEING PROCESSED IN ENTERPRISE DATA WAREHOUSES UTILIZING TIME SERIES PREDICTIONS
Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method generating a feature for a machine learning model to use to forecast a time interval between receipt of first data at a staging area of a data warehouse and receipt of the first data at a target database of the data warehouse based at least in part on second data received from the staging area and third data received from the target database. The method can further include generating, using the machine learning model, a forecasted time interval based at least in part on the feature. The method can further include comparing the forecasted time interval with an expected time interval for fourth data received at the staging area. The method can further include updating a priority of the first data based at least in part on the comparison. The method can further include transmitting the first data to the target database based at least in part on the updated priority.
Methods, computer readable media, and systems for providing a Cloud-Based system that takes maintenance and access of patient-centric healthcare records to the next level by providing a healthcare-provider-specific app store in a healthcare web portal. The cloud-based system serves as a patient web portal which hosts a computer App Store. The system determines a set of relevant computer applications from a plurality of available computer applications that are relevant to the particular patient and generates a customizable dashboard on a graphical user interface.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 16/954 - Navigation, e.g. using categorised browsing
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G16H 50/00 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
18.
Systems, Methods, And Storage Media For Conducting Security Penetration Testing
Methods, systems, and computer-readable media are disclosed herein that conduct security penetration testing. In accordance with embodiments, a security extension is run in a web browser in order to record a user interface workflow and to generate a test script of the user interface workflow. The test script is executed in an application in order to perform penetration testing of the user interface workflow, in some embodiments. Based on the outcomes of the penetration testing, a security report may be generated in order to identify one or more security vulnerabilities in the user interface workflow.
Techniques for presenting recommendations of candidate unmapped proprietary codes for target standard codes are disclosed. The techniques include comparing datasets of unmapped proprietary codes with datasets of a target standard code. The datasets are represented as vector embeddings generated using word embedding techniques. Cosine similarities between the vector embeddings of candidate proprietary codes and the target standard code are used to identify and rank a list of candidate unmapped proprietary codes for the target standard code. The cosine similarity scores may be weighted.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
20.
PATIENT INFORMATION MANAGEMENT SYSTEMS AND METHODS OF PROCESSING PATIENT INFORMATION BASED ON NON-HEALTHCARE DATA
In one aspect: A non-healthcare data source associated with a patient may be identified, and a non-healthcare dataset may be extracted from the non-healthcare data source. A target subset of healthcare data may be selected from a set of healthcare data based on the non-healthcare dataset. A query may be executed on the target subset of healthcare data to identify a candidate patient information dataset. A patient information data structure may be created or updated based on the candidate patient information dataset associated with the patient. In another aspect: A match score may be computed between a non-healthcare dataset and a candidate patient information dataset, and responsive to the match score meeting a threshold, the candidate patient information dataset may be selecting as a patient information dataset. The patient information dataset may be stored in the patient information data structure in association with the patient.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
21.
Concept Agnostic Reconcilation And Prioritization Based On Deterministic And Conservative Weight Methods
Methods, systems, and computer-readable media are disclosed herein to provide rule-based reconciliation of records. Specifically, rules are utilized to reconcile one or more records and identify duplicates therein. Once duplicate records are identified, one or more ranking sets can be utilized to identify which of the duplicate records to write to the system.
A decision support tool is provided for identifying and assisting clinicians with patient ventilator asynchrony. The information used to make the identification may include data from a patient's ventilator including the volume, flow, and pressure associated with that ventilator. At least some of this information may be used to compute one or more features for a time series of the data received for the patient. These features may be used in connection with heuristic rules and machine learning algorithms to identify instances of patient ventilator asynchrony. Based on the identification, one or more intervening actions may be initiated to reduce the impact of patient ventilator asynchrony.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61M 16/00 - Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators Tracheal tubes
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/40 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Systems, methods, and user interfaces provide integrated coordination of care. Care team coordination and collaboration is promoted by bringing together each of the necessary elements of a patient's plan into a single point of access. Current workflow silos that exist in the care planning space are eliminated. This allows seamless support for the many different care planning regulations across care settings and supports and involves the entire care team including the patient and personal care team.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
24.
Systems And Methods For Ontologically Classifying Records
Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Methods, systems, and computer-readable media are provided for facilitating record matching and entity resolution and for enabling improvements in record linkage. A power-spectrum-based temporal pattern-specific weight may be incorporated into record linkage methods to enhance the record linkage accuracy and statistical performance. For example, in embodiments, a value-specific weight may be calculated from a population-based frequency of field-specific values and provides an opportunity to capture and measure the relative importance of specific values found in a field. A timeseries-derived Bayesian power spectrum weight may be calculated from the population-based frequency of temporal pattern-specific values in terms of intensities at various frequencies of the power spectrum computed from the timeseries, and further provides an opportunity to capture and measure the relative importance of specific sequences of care episodes.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Systems, methods and computer-readable media are provided for identifying patients having an elevated near-term risk of chronic kidney disease (CKD) progression, including predicting an individual's risk of progression to Stage 3 CKD within a future time interval, which may be up to 36 months. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In an embodiment, measurements of physiological variables are obtained, including serial measurements for uric acid levels from a longitudinal time series of serum or plasma samples spanning the previous two to five years. An annualized uric acid velocity of the patient is determined and used to generate a multivariable mathematical model for determining a likelihood of risk for developing Stage 3 CKD within 36 months.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06Q 10/1093 - Calendar-based scheduling for persons or groups
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
27.
CODE GENERATOR FOR ACCESSING DIFFERING HEALTH RECORD SYSTEMS
Techniques for enabling a user, having little or no code-writing experience, to build and configure a user-customized software application configured to extract, process, and display information associated with user-selected health records or portions of health records is disclosed. The techniques may include operations such as receiving a selection of a first resource defined by a first EHR system, analyzing metadata associated with the first resource to identify a first set of characteristics corresponding to the first resource, identifying configuration fields associated with the first resource based on the first set of characteristics, presenting the configuration fields associated with the first resource, receiving configuration values for the configuration fields, and generating code for executing API calls to extract data associated with the first resource in accordance with the configuration values and for presenting the data associated with the first resource.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Techniques for enabling a user, having little or no code-writing experience, to build and configure a user-customized software application configured to extract, process, and display information associated with user-selected health records or portions of health records is disclosed. The techniques may include operations such as receiving a selection of a first resource defined by a first EHR system, analyzing metadata associated with the first resource to identify a first set of characteristics corresponding to the first resource, identifying configuration fields associated with the first resource based on the first set of characteristics, presenting the configuration fields associated with the first resource, receiving configuration values for the configuration fields, and generating code for executing API calls to extract data associated with the first resource in accordance with the configuration values and for presenting the data associated with the first resource.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
29.
SYSTEM AND TECHNIQUES FOR GENERATING A REUSABLE PROCESS
In some aspects, a method can be performed by a processor of a computing device. The method includes generating a non-executable code template that includes code that defines a set of operations. Additionally, the method includes accessing data about a particular project. Further, the method includes determining a set of project variables that pertain to the particular project and determining a set of project logic that pertains to the particular project. Furthermore, the method includes generating an executable code by integrating the set of project variables and the set of project logic into the non-executable code template. Moreover, the method includes compiling the executable code and generating a result by executing the compiled executable code.
Systems, methods and computer-readable media are provided for monitoring patients and quantitatively predicting whether an event, such as a significant change in health status meriting intervention, is likely to occur within a future time interval subsequent to computing the prediction. Medical data for a patient is collected from one or more different inputs and used to determine time series data. From this, a forecasted numerical value is computed for one or more physiologic parameters associated with the patient, which may be used to further monitor the patient and facilitate decision making about a need for intensified monitoring or intervention to prevent or manage deterioration of hemostasis. An evolutionary algorithm, such as particle swarm optimization and/or differential evolution, may be used to determine the most probable value of the one or more physiologic parameters at one or more future times.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/02 - Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
31.
SYSTEM AND TECHNIQUES FOR GENERATING A REUSABLE PROCESS
In some aspects, a method can be performed by a processor of a computing device. The method includes generating a non-executable code template that includes code that defines a set of operations. Additionally, the method includes accessing data about a particular project. Further, the method includes determining a set of project variables that pertain to the particular project and determining a set of project logic that pertains to the particular project. Furthermore, the method includes generating an executable code by integrating the set of project variables and the set of project logic into the non-executable code template. Moreover, the method includes compiling the executable code and generating a result by executing the compiled executable code.
System, methods and computer-readable media are provided for facilitating decision support by assessing the severity of injuries and the acute risk of physiologic deterioration and short-term mortality in trauma patients, both on the scene where emergency medical personnel initially evaluate and treat trauma victims and while in-transit to receive care at a health care facility. Embodiments of the invention provide a quantitative numerical score and calculated probabilities of deterioration and/or mortality, based on multi-variable statistical regressions. In some embodiments, fuzzy-set mathematics are employed in the regressions to account for physiologic and epistemologic uncertainty that are inherent in one or more emergency personnel members' observations and recordings of a plurality of measurements periodically during the time on-scene and during transit. Embodiments also enable the prediction of the other outcomes of trauma patients, such as the intensity of care that will likely be required prior to hospital arrival.
In some examples, data associated one or more medical presentations of a person is received. A mapping that associates the data to one or more candidate diagnoses is determined. The one or more candidate diagnoses correspond to one or more possible medical conditions associated with the person. A validation score for a first candidate diagnosis, of the one or more candidate diagnoses, is generated based on a set of features associated with the person. The validation score indicates an accuracy of the candidate diagnosis. At least partially in response to the generating of the validation score, an indicator is caused to be provided at a user device. The indicator indicates the validation score.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
A computer-implemented trading platform, system, and method are provided for facilitating the trading and accounting for health quality offsets. So-called “health quality credits” related to emissions of ‘potentially avoidable complications’ (PACs) or ‘potentially avoidable mortality’ (PAMs). Emissions trading or “cap-and-trade” is an administrative approach used to control emissions that degrade public-goods assets by providing economic incentives for achieving and sustaining economically significant reductions in the emissions of pollutants that impair public goods such as clean air or health. In one embodiment, a method of accounting for health quality offsets established in one or more offset markets includes registering a health quality offset to an owner thereof; assigning a unique identifier to the health quality offset; crediting a client account with the health quality offset; receiving notification of a trade between a buyer and a seller, if the seller has an amount of health quality offsets sufficient for the trade, adjusting buyer and seller accounts with corresponding credits and debits; otherwise, providing a notification of insufficient health quality offsets to carry out the trade.
Methods and systems are provided for detecting objects or patient safety events in a patient room. Artificial intelligence is utilized to enhance safety issue recognition capabilities by the methods and systems. Sensors collect a series of images and depth data in a room of a patient. Data (corresponding to images and depth data of an object or patent safety event) is received from the sensors and compared to stored data to identify the object or patient safety event. The images are communicated to a central video monitoring system and a user may be prompted to confirm if the object requires learning or a patient safety event occurred (or identify the object or patient safety event) or to provide additional parameters or actions. A patient safety learning system analyzes the data and incorporates the user response to enhance safety issue recognition capabilities of the system and reduce false alerts.
Methods, systems, and computer-readable media for rapid event voice documentation are provided herein. The rapid event voice documentation system captures verbalized orders and actions and translates that unstructured voice data to structured, usable data for documentation. The voice data captured is tagged with metadata including the name and role of the speaker, a time stamp indicating a time the data was spoken, and a clinical concept identified in the data captured. The system automatically identifies orders (e.g., medications, labs and procedures, etc.), treatments, and assessments/findings that were verbalized during the rapid event to create structured data that is usable by a health information system and ready for documentation directly into an EHR. The system provides all of the captured data including orders, assessment documentation, vital signs and measurements, performed procedures, and treatments, and who performed each, available for viewing and interaction in real time.
G10L 17/00 - Speaker identification or verification techniques
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Techniques for predicting, by a machine learning model, reimbursement characteristics associated with healthcare services are disclosed. A system trains a machine learning model to estimate characteristics of a predicted reimbursement associated with a healthcare service. The predicted reimbursement may be generated by applying the trained machine learning model to healthcare services data prior to the generation of medical claims, or subsequent to the generation of medical claims. The system generates recommendations for modifying one or more of healthcare services, recorded descriptions of healthcare services, and medical claims based on the healthcare services in response to the machine learning model predictions.
Systems and methods determine consumability scores of data to be transformed from a source clinical data schema to a target clinical data schema. Determining the consumability score can include calculating values for characteristics of the source data set transformed from the clinical data schema, weighting the individual scores, and aggregating the weighted scores. The consumability score may indicate a predicted suitability of the transformed data for a target use. Further, the system can generate recommendations for using the target data set based on the consumability score. The determination can include predicting whether the transformation produces elements of a target data set are sufficient for the intended purposes of users of the target data set, whether the source data set includes sufficient information that can be mapped to the target clinical data schema, and whether the transformation captures the source data set in sufficient quantity and quality.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
39.
System And Method For Autonomous Testing, Machine-Learning Model-Supervised Prioritization, And Randomized Workflow Generation
Methods, systems, and computer-readable media are disclosed herein combine randomization functionalities with the machine-learning prioritization of workflows for performance testing. In aspects, a primary workflow having a sequence of user interface steps is input. Testing workflows are generated that represent each variable position of unlocked steps in the sequence of the primary workflow while maintaining the sequential position of any locked steps. These testing workflows are then ingested to a machine learning model that identifies as subset of the testing workflows to prioritize over other. Specifically, testing workflows are prioritized that at least partially match sequence patterns in historical workflow data that is associated with vulnerable computer code. The subset is output and tested by testing engine to generate a report of any vulnerable computer code.
Systems, methods and computer-readable media are provided for identifying patients having an elevated near-term risk of chronic kidney disease (CKD) progression, including predicting an individual's risk of progression to Stage 3 CKD within a future time interval, which may be up to 36 months. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In an embodiment, measurements of physiological variables are obtained, including serial measurements for uric acid levels from a longitudinal time series of serum or plasma samples spanning the previous two to five years. An annualized uric acid velocity of the patient is determined and used to generate a multivariable mathematical model for determining a likelihood of risk for developing Stage 3 CKD within 36 months.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06Q 10/1093 - Calendar-based scheduling for persons or groups
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Systems, methods and computer-readable media are provided for monitoring patients and quantitatively predicting whether an event, such as a significant change in health status meriting intervention, is likely to occur within a future time interval subsequent to computing the prediction. Medical data for a patient is collected from one or more different inputs and used to determine time series data. From this, a forecasted numerical value is computed for one or more physiologic parameters associated with the patient, which may be used to further monitor the patient and facilitate decision making about a need for intensified monitoring or intervention to prevent or manage deterioration of hemostasis. An evolutionary algorithm, such as particle swarm optimization and/or differential evolution, may be used to determine the most probable value of the one or more physiologic parameters at one or more future times.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/02 - Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/021 - Measuring pressure in heart or blood vessels
Methods, computer systems, and computer-storage medium are provided for providing clinical information for a person, such as a test result, in real time where the information is automatically pushed to a selected notification device of a physician. A graphical representation is generated for a clinician to select clinical notification options. Received selections create a notification to be automatically sent to one or more clinician devices. Automatic notifications reduce delay in clinician action when new clinician information is provided.
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Methods, systems, and computer-storage media are provided for determining an individual's second event risk score where the second event risk score represents a likelihood that the individual will experience the second event within a predetermined time period after the occurrence of a first event. Upon occurrence of the first event, a sampling protocol is initiated where an electronic medical record store is accessed on a predetermined schedule to sample a pre-selected set of medical data elements for the individual. Logistic regression analysis is executed on the pre-selected set of medical data elements to generate a second event risk score for the individual. The second event risk score is communicated to a medical professional managing the medical care of the individual, and the individual's electronic medical record is modified to reflect the second event risk score.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
44.
Active management of files being processed in enterprise data warehouses utilizing time series predictions
Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method includes a device receiving historical data from a staging area and a target database of a data warehouse. The device can generate a feature for a machine learning model based at least in part on the historical data. The device can generate a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database. The device can compare the forecasted time interval with an expected time interval. The device can generate a work ticket based at least in part on the comparison.
Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing sub-models of a data model pipeline in a deployed or pre-deployment environment. The tool may compute a plurality of performance measures that quantitatively assess the performance of each sub-model in the data model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the sub-model. Additionally, multiple versions of a sub-model deployed in similar data model pipelines, or in a pre-deployment environment, may be comparatively evaluated. A leading version of the sub-model may be identified and deployed.
Methods are provided for the surveillance and monitoring of a patient's medical care when the patient is treated at two or more medical organizations having different medical record systems. Patient information is received from a first medical organization and populated into an active risk assessment array that monitors the patient's risk for developing a particular disease or condition. Patient information is then received from a second medical organization and populated into the array. It is determined that actionable criteria have been met, and in response, a notification or alert is sent to the medical organizations indicating that the patient is at risk.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
47.
PREDICTING ADDICTION RELAPSE AND DECISION SUPPORT TOOL
Technologies are provided for determining an individual's likelihood of relapsing into prior behavior subsequent to a treatment for a mental health or addiction disorder, and in some instances predicting a likelihood time frame for such a relapse. Target subjects having a risk of addiction relapse, non-adherence to a treatment program, or absconding, may be automatically identified based on a multiplicative-regression model for relative survival (MRS) that is developed for predicting risk or likelihood of relapse or non-adherence. Further, in some embodiments, a leading indicator of near-term future abnormalities may be provided thereby proactively notifying supervisory personnel responsible for the person and providing such personnel with timely notice to enable effective corrective, preventive, or trend-modifying maneuvers to be undertaken.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06F 17/18 - Complex mathematical operations for evaluating statistical data
48.
Predicting sphingolipidoses (fabry's disease) and decision support
A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder. In particular, a mechanism is provided to detect and classify a lysosomal storage disorder in a human patient, which utilizes a logistic regression classifier determined based on a multi-variable-composite-biomarker comprising a specific set of physiological variables of the patient. This multi-variable statistical predictive biomarker approach may be employed for identifying persons whose attributes are consistent with features of sphingolipidoses, such as Fabry's Disease.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Embodiments of the invention provide decision support services, including an enhanced user interface. A reason for a clinical visit is determined and an initial workflow may be provided via the user interface during a clinical visit. The workflow may include a set of initial user interface elements that are determined based on a context associated with the clinical visit. The set of initial user interface elements may include a first initial interface element associated with a pre-identified clinical concept. During the clinical visit, a first suggested interface element can be provided. The first suggested interface element is generated during the workflow based on detecting a selection of the first initial interface element. The first suggested interface element can be associated with a first suggested clinical concept that is related to the pre-identified clinical concept.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
50.
RESPONDING TO REQUESTS FOR INFORMATION AND OTHER VERBAL UTTERANCES IN A HEALTHCARE FACILITY
Systems, methods, and devices are provided for responding to requests received from users in a healthcare facility, and for generating alerts based upon criticality detected in received verbal utterances. A personal assistant device (PAD) may receive a verbal request for information. The identity of the requestor may be verified and a response to the request may be received by the PAD. In some cases, prior to audibly outputting the response, it may be determined whether the response contains protected health information (PHI). Upon determining that the response contains PHI, the presence of persons other than the patient in proximity to the PAD and/or security of the location of the PAD may be evaluated prior to audibly outputting the response. Additionally, alerts having a heightened criticality may be generated when a received verbal utterance is determined to have a heightened criticality.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
51.
TOOL FOR PREDICTING PROGNOSIS AND IMPROVING SURVIVAL OF PATIENTS
A diagnostic and decision support technology is provided for determining the likely prognosis and potential treatment for patients experiencing a condition, such as COVID-19, for example. In particular, a mechanism is provided for receiving a historical patient dataset comprising one or more historical health parameters associated with a plurality of historical patients. Additionally, a patient dataset is received comprising one or more patient health parameters associated with a patient. A cluster is identified based on the similarity of the patient dataset and a plurality of historical patient datasets. From the cluster, a set of treatable features are identified and evaluated for their potential impact on the patient's successful recovery from the condition. A recommendation is generated based on the evaluation as to what feature should be treated first to decrease the mortality of the patient.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
52.
TOOL FOR PREDICTING PROGNOSIS AND IMPROVING SURVIVAL IN COVID-19 PATIENTS
A diagnostic and decision support technology is provided for determining the likely prognosis and potential treatment for patients experiencing a condition, such as COVID-19, for example. In particular, a mechanism is provided for receiving a historical patient dataset comprising one or more historical health parameters associated with a plurality of historical patients. Additionally, a patient dataset is received comprising one or more patient health parameters associated with a patient. A cluster is identified based on the similarity of the patient dataset and a plurality of historical patient datasets. From the cluster, a set of treatable features are identified and evaluated for their potential impact on the patient's successful recovery from the condition. A recommendation is generated based on the evaluation as to what feature should be treated first to decrease the mortality of the patient.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and computer-readable media for a system and method are provided for assessing the value of predictive models monitoring medical conditions. Trends in an individual's medical condition are determined based on monitoring values, and the trends are associated with the actions performed in response to the monitoring values and in accordance with the predictive models. The trends may indicate that an individual's condition is improving, worsening, or remaining stable in response to the action taken. Models used for multiple conditions for an individual or for a population of individuals may be assessed in this manner to generate knowledge regarding the performance of the models based on the actions taken. This knowledge may be used to assess the value of the models in terms of the models' performance and may provide insight on way to improve the models.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
54.
SYSTEMS AND METHODS FOR INFUSION PUMP FORMULARY VALIDATION
Systems, executable process, and methods for validation of infusion pumps is described. A set of configuration files is loaded into a validation operational instruction generator. The configuration files may define the configuration of infusion pump operational instructions, the potential units of measure, the potential routes of administration, the potential dosages, and the formulary of potentially infusible materials. A set of operational instructions are generated for each combination of the potentially infusible materials for each potential route of administration for each potential dosage. The operational performance of an infusion pump is monitored during execution of the generated operational instructions.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Systems and methods provide for an intelligent laboratory lockbox. The intelligent laboratory lockbox includes a vault unit and one or more presence sensors configured to detect a presence of a specimen within the one or more compartments of the vault unit. The presence sensor(s) may generate a load status based on sensing a specimen in the vault unit, wherein the load status includes one of a loaded status or an unloaded status. A control unit is operably connected to at least the vault unit and the one or more presence sensors. The control unit is configured to modify a specimen pickup workflow based on the load status.
Decision support technology is provided for use with patients who may be prone to a cardiovascular condition such as acute coronary syndromes. A mechanism is provided to determine a patient's risk for experiencing a cardiovascular ischemic event at a future time interval based on temporal patterns determined using physiological parameters of the patient such as serum or blood uric acid and/or C-reactive protein (CRP). A forecast or score may be determined indicating whether or not temporal patterns merit intervention to prevent occurrence or reoccurrence of ischemic events, or for determining adherence to or efficacy of treatment or preventive interventions. Based on the forecast or score, appropriate response action such as automatically issuing an alert or notification to a caregiver associated with the patient, may be determined, recommended, or implemented.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Methods, computer systems, and computer storage media are provided for utilizing system diagnostics focused in areas of disruption to improve inventory and workforce management in a revenue cycle management system. Inventory input is received from a revenue cycle management system. The inventory input includes inventory in the revenue cycle management system, types of claims in the inventory, pair agreements of the claims, and a length of time the claims have been in the inventory. Workforce input is also received from the revenue cycle management system. The workforce input includes experience, type of education or technical training, specialty, and outcomes corresponding to a workforce of the client. A portion of the inventory is automatically assigned to an associate of the workforce. The portion of the inventory tailored to the associate based on the inventory input and the workforce input.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
Systems and methods provide for implementation of a messaging protocol that provides security necessary for clinical messaging while also providing scalability needed to properly function within a clinical setting. The messaging protocol provides for federation of messages across messaging domains with a direct target address or via a role or group endpoint address that resolves to one or more target addresses. The messaging protocol also provides the ability to include content other than text in messages.
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
H04L 51/48 - Message addressing, e.g. address format or anonymous messages, aliases
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
59.
Ontological Standards Based Approach To Charting Utilizing A Generic Concept Content Based Framework Across Multiple Localized Proprietary Domains
Embodiments of the present disclosure provide a cloud-based integrated charting system that utilizes a universal mapping service across services and clients to provide insights for the provider into customizations made by clients. Additionally, the system provides content building tools that enable clients to customize content to update models provided by the provider. A mapping service maps model codes corresponding to model concepts to a plurality of standard based concept codes corresponding to client domain specific customized concepts for an ontology of the one or more ontologies. A workflow recommendation is provided that enables a user to chart data and includes the model concepts comprising charting elements. Accordingly, the data is stored in a client database in accordance with the client domain specific customized concepts. Since the model concepts are mapped to the client domain specific customized concepts, the workflow recommendation can be prepopulated with data from the client database.
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
60.
System And Method For Improved State Identification And Prediction In Computerized Queries
Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.
Systems, methods and computer-readable media are provided for monitoring patients and quantitatively predicting whether an event such as a significant change in health status meriting intervention, is likely to occur within a future time interval subsequent to computing the prediction. Medical data for a patient is collected from one or more different inputs and used to determine time series data. From this, a forecasted numerical value is computed for one or more physiologic parameters associated with the patient, which may be used to further monitor the patient and facilitate decision making about a need for intensified monitoring or intervention to prevent or manage physiologic or hemodynamic deterioration. An evolutionary algorithm, such as particle swarm optimization and/or differential evolution, may be used to determine the most probable value of the physiologic parameter(s) at one or more future times.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/021 - Measuring pressure in heart or blood vessels
Systems, methods and computer-readable media are provided for applying performance data (e.g., Electronic Clinical Quality Measures (eCQM), Pay-for-Performance (P4P) measures, Meaningful Use (MU) measures) to a fantasy league health exchange. In aspects, a set of patient data for patients is determined. The set of patient data corresponds to a time interval of treatment of the patients by a population of clinicians. In addition, performance data is determined. The performance data corresponds to the population of clinicians and is based on the set of patient data and the time interval. Further, a provider ranking is determined using a classification model determined from the performance data and from explanatory attributes. The provider ranking can be used to forecast or project future performance results for selected individual or group providers. Further, the provider rankings can be used for draft picks, roster submission, or bets placed in a fantasy league exchange system.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 10/067 - Enterprise or organisation modelling
G07F 17/32 - Coin-freed apparatus for hiring articlesCoin-freed facilities or services for games, toys, sports, or amusements
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
A system, methods, and computer-readable media are provided for the automatic identification of patients having an elevated near-term risk of pulmonary function deterioration or respiratory distress. Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent respiratory events in humans, and in one embodiment take the form of a platform for wearable, mobile, untethered monitoring devices with embedded decision support. Respiratory information is obtained over one or a plurality of previous time intervals, to classify a likelihood of events leading to an acute respiratory decompensation event within a future time interval. In an embodiment, the risk prediction is based a plurality of nonlinearity measures of capnometry information over the previous time interval(s), and the risk for an acute respiratory decompensation event determined using an ensemble model predictor on the nonlinearity measures.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
64.
Predicting addiction relapse and decision support tool
Technologies are provided for determining an individual's likelihood of relapsing into prior behavior subsequent to a treatment for a mental health or addiction disorder, and in some instances predicting a likelihood time frame for such a relapse. Target subjects having a risk of addiction relapse, non-adherence to a treatment program, or absconding, may be automatically identified based on a multiplicative-regression model for relative survival (MRS) that is developed for predicting risk or likelihood of relapse or non-adherence. Further, in some embodiments, a leading indicator of near-term future abnormalities may be provided thereby proactively notifying supervisory personnel responsible for the person and providing such personnel with timely notice to enable effective corrective, preventive, or trend-modifying maneuvers to be undertaken.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06F 17/18 - Complex mathematical operations for evaluating statistical data
65.
DE-DUPLICATION AND CONTEXTUALLY-INTELLIGENT RECOMMENDATIONS BASED ON NATURAL LANGUAGE UNDERSTANDING OF CONVERSATIONAL SOURCES
Methods, systems, and computer-readable media are disclosed herein that provide a comprehensive view that reveals all or nearly all possible method dependencies that are present in client workflows. In aspects, when computer code for a particular method is going to be edited, other methods are identified that have upstream or downstream dependencies relative to the particular method. The methods that will be affected based on the computer code editing can be presented in a user-interactive graphical user interface that facilitates exploration of upstream and downstream dependencies.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 16/215 - Improving data qualityData cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
Methods and systems for processing of voice input to identify intents and mapped standard terminologies are provided. Using natural language processing, an intent of a voice input is identified. The intent is utilized to identify a standard terminology that maps to the intent. The standard terminology is utilized to identify information relevant to the standard terminology in a patient's electronic health record.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 15/18 - Speech classification or search using natural language modelling
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
67.
DE-DUPLICATION AND CONTEXTUALLY-INTELLIGENT RECOMMENDATIONS BASED ON NATURAL LANGUAGE UNDERSTANDING OF CONVERSATIONAL SOURCES
Methods, systems, and computer-readable media are disclosed herein that provide a comprehensive view that reveals all or nearly all possible method dependencies that are present in client workflows. In aspects, when computer code for a particular method is going to be edited, other methods are identified that have upstream or downstream dependencies relative to the particular method. The methods that will be affected based on the computer code editing can be presented in a user-interactive graphical user interface that facilitates exploration of upstream and downstream dependencies.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 16/215 - Improving data qualityData cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
68.
Determining a cardiovascular ischemic event and decision support tool
Decision support technology is provided for use with patients who may be prone to a cardiovascular condition such as acute coronary syndromes. A mechanism is provided to determine a patient's risk for experiencing a cardiovascular ischemic event at a future time interval based on temporal patterns determined using physiological parameters of the patient such as serum or blood uric acid and/or C-reactive protein (CRP). A forecast or score may be determined indicating whether or not temporal patterns merit intervention to prevent occurrence or reoccurrence of ischemic events, or for determining adherence to or efficacy of treatment or preventive interventions. Based on the forecast or score, appropriate response action such as automatically issuing an alert or notification to a caregiver associated with the patient, may be determined, recommended, or implemented.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
69.
INSURANCE RISK SCORING BASED ON CREDIT UTILIZATION RATIO
Systems and methods are provided for the problem of automatic, algorithm-guided estimation of insurance loss ratio, claims frequency, the probability of excess claims, and other insurance policy performance characteristics for an individual insured or for groups of insured individuals. A time-series-derived Bayesian power spectrum weight is calculated from the frequency of temporal pattern-specific values in terms of intensities at various frequencies of the power spectrum computed from credit utilization ratio (CUR; outstanding balance of debt, as a percentage of credit line available) time-series obtained by the insurer by ‘soft pull’ inquiries submitted periodically to credit-rating agencies, and provides an opportunity to capture and measure the relative magnitude of frequent or unexpected changes in consumer liquidity. The present technology provides a system and method for classifying insurance risk, for insurance risk scoring, or for incorporating a power-spectrum-based temporal pattern-specific weight into an actuarial method to enhance the loss ratio estimation accuracy and statistical financial performance of insurance products and health plans.
A system, method, and computer-readable media are provided for facilitating clinical decision making, and in particular, decision making based on a third party's clinical situation by determining and providing useful, up-to-date information, such as patient-related information to a decision maker. In one embodiment, a user first identifies an information item concerning a patient. Based on that item, a set of related information items is determined and prioritized, and a reference pointer, which identifies the set of related information, is generated. The reference pointer is communicated to the user's mobile device. Subsequently, the user's mobile device requests information from the set of information items associated with the reference pointer, and provides information authorization information. Following authentication of the user's credentials, updates of information from the set of information items may be communicated to the user's mobile device as they become available.
G16H 10/65 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
G06F 16/90 - Details of database functions independent of the retrieved data types
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06Q 50/22 - Social work or social welfare, e.g. community support activities or counselling services
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Embodiments relate to systems and methods that retrieve dialogue data associated with a plurality of utterances. The plurality of utterances include a first utterance. The systems and methods further determine that a target concept, of the dialogue data, is in a first dialogue segment associated with the first utterance. Additionally, the target concept is determined based on the first utterance in the first dialogue segment having a highest weight for relevancy to a knowledge domain. Further, the methods and systems determine a dialogue goal comprising the target concept. Due to the dialogue goal comprising the target concept, a structured link associating the target concept to the dialogue goal is generated.
Systems, methods and computer-readable media are provided for determining conformity to performance of meaningful use measures in human health care delivery. A Bayesian Markov Chain Monte Carlo statistical process is utilized to achieve reliable estimates for such measures despite the small subgroup sample sizes accruing during each measurement period. One embodiment utilizes zero- and one-inflated beta regression that is robust against moderate prevalence of zero or one counts in the numerators for such measurements and determinations of statistical associations with such factors as clinician, care venue, and patient attributes. Based on the determined conformity, a notification is provided to provider clinicians or organization management indicating the conformity and, in some instances, a degree of conformity.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 10/067 - Enterprise or organisation modelling
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
73.
Pressure injury prevention sensor and decision support tool
Technologies are provided for determining a likelihood or risk of a human patient for developing pressure injury based on patterns of movement by the patient determined using a specialized measurement support surface. Some embodiments automatically ascertain whether patterns of movements of a human subject, whose weight is supported on the support surface, exhibit sufficient frequency and variability of activity such as will confer either certain health benefits or expose the individual to certain health risks, such as development of pressure ulcers in the load-bearing skin and soft tissues. The measurement support surface can include a contact layer, a pressure-measuring layer having an air bladder subjacent to the measurement support surface, and a substrate layer with a density and stiffness to mechanically isolate the pressure-measuring layer from ambient vibrations transmitted through a floor, furniture, or other intervening articles upon which the measurement support surface is mounted.
Disclosed are various methods of presenting result visualizations for test results. The visualization may include a timeline, a test label area, a test parameter indicator, and a test value representation. The test parameter indicator is associated with the test provided at a location corresponding with the test label of the test label area and with a location corresponding to a test time of the timeline, including a first test parameter indicator end associated with the upper test parameter threshold and a second test parameter indicator end associated with a lower test parameter threshold. The test value representation of a test value extends away from the test parameter indicator and proportionally represents the test value relative to an upper test limit and the test value to a lower test limit.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
Technologies are provided for providing a dashboard interface corresponding to one or more datacenter components for information originating from one or more proprietary applications. A selection of information corresponding to the one or more datacenter components is initially received. Upon receiving the selection, an application programming interface (API) call request is made for the information corresponding to the one or more datacenter components from the one or more proprietary applications. As the information corresponding to one or more datacenter components from the one or more proprietary applications is received, it is aggregated and provided within the dashboard interface.
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
H04L 67/131 - Protocols for games, networked simulations or virtual reality
H04L 67/133 - Protocols for remote procedure calls [RPC]
H04L 67/567 - Integrating service provisioning from a plurality of service providers
76.
Determining new knowledge for clinical decision support
Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
77.
DETERMINING ADEQUACY OF DOCUMENTATION USING PERPLEXITY AND PROBABILISTIC COHERENCE
Technologies are provided for determining deficiencies in narrative textual data that may impact decision-making in a decisional context. A candidate text document and a reference corpus of text may be utilized to generate one or more topic models and document-term matrices, and then to determine a corresponding statistical perplexity and probabilistic coherence. Statistical determinations of a degree to which the candidate deviates from the reference normative corpus are determined, in terms of the statistical perplexity and probabilistic coherence of the candidate as compared to the reference. If the difference is statistically significant, a message may be reported to user, such as the author or an auditor of the candidate text document, so that the user has the opportunity to amend the candidate document so as to improve its adequacy for the decisional purposes in the context at hand.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
78.
Determining mucopolysaccharidoses and decision support tool
A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder. In particular, a mechanism is provided to detect and classify a lysosomal storage disorder in a human patient, which utilizes a logistic regression classifier determined based on a multi-variable-composite-biomarker comprising a specific set of physiological variables of the patient. This multi-variable statistical predictive biomarker approach may be employed for identifying persons whose attributes are consistent with features or lysosomal storage diseases, such as attenuated mucopolysaccharidosis Type 1 (Hurler-Scheie or Scheie syndromes).
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 25/00 - ICT specially adapted for hybridisationICT specially adapted for gene or protein expression
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
79.
Discovering context-specific complexity and utilization sequences
Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to the plurality of hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing voice-activated ambulance booking. Initially, a non-medical user initiates a workflow, via a voice command, that enables the user to provide information about a patient that can be utilized to prioritize the booking of ambulances for patients. Upon providing the initial voice command, the workflow is provided, via a user interface of the user device. The workflow comprises a series of questions corresponding to a status of the patient that can easily be answered merely by observing the patient (e.g., is the patient conscious, is the patient breathing, is the patient bleeding). Based on responses provided by the user, an ambulance is requested for the patient. User device vitals including battery charge levels and signal strength can be used to resolve conflicts if multiple requests are made.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Methods and systems for enhanced natural language processing of clinical documentation are provided. Using natural language processing, a clinical condition is extracted from unstructured data within a current electronic document. A clinical ontology identifying itemsets associated with the clinical condition is retrieved, and indicators of relevant clinical concepts, as identified from the ontology, are searched from within the patient's longitudinal record, which comprises documentation from at least a prior encounter. Based on the whether the clinical concepts are present in the patent's record, a confidence is assigned to the NLP-extracted clinical condition, and one or more actions may be performed.
Systems, methods, and other embodiments associated with indicating physical progress of a clinician through patient locations are described. In one embodiment, a method includes accessing a workflow of locations of patients within a clinical care environment that a clinician is to visit on a schedule. The method includes sensing that the clinician is at a current location within the clinical care environment using a location sensor carried by the clinician. The method includes detecting that the current location corresponds to one of the locations in the workflow. The method includes, in response to the detection, generating in a display an indication of physical progress of the clinician through the workflow as of the current location.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
Systems, methods and computer-readable media are provided for determining conformity to performance of meaningful use measures in human health care delivery. A Bayesian Markov Chain Monte Carlo statistical process is utilized to achieve reliable estimates for such measures despite the small subgroup sample sizes accruing during each measurement period. One embodiment utilizes zero- and one-inflated beta regression that is robust against moderate prevalence of zero or one counts in the numerators for such measurements and determinations of statistical associations with such factors as clinician, care venue, and patient attributes. Based on the determined conformity, a notification is provided to provider clinicians or organization management indicating the conformity and, in some instances, a degree of conformity.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G06Q 10/067 - Enterprise or organisation modelling
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
85.
Discovering context-specific complexity and utilization sequences
Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care. In an embodiment, time series are formed by electronically representing information pertaining to successive longitudinal episodes of health services utilization and the circumstances in which the episodes were incurred; calculating time-series K-nearest-neighbor clusters and distances for each combination; determining the cluster to which a given candidate patient complexity record is nearest, and prescribing one or more interventions specific to hazards that are characteristic of trajectories that are members of that cluster, or that are deemed to be relevant to mitigating those hazards, thereby preventing the adverse outcomes and subsequent excess utilization that are prevalent in that cluster.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Methods, systems, and computer-readable media are provided for facilitating record matching and entity resolution and for enabling improvements in record linkage. A power-spectrum-based temporal pattern-specific weight may be incorporated into record linkage methods to enhance the record linkage accuracy and statistical performance. For example, in embodiments, a value-specific weight may be calculated from a population-based frequency of field-specific values or dichotomized values of selected phenotypic variables, and provides an opportunity to capture and measure the relative importance of specific values found in a field. A phenotypic bit-vector “fingerprint” pattern-specific weight or Bayesian power spectrum weight may be determined and incorporated into record linkage methods.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
87.
De-duplication and contextually-intelligent recommendations based on natural language understanding of conversational sources
Methods and systems for natural language processing/understanding of voice conversations are provided. Using natural language processing, a clinical condition is extracted from a voice conversation. A clinical ontology identifies clinical concepts associated with the clinical conditions. The clinical concepts are classified for documentation. The clinical concepts are searched and validated from within an individual's longitudinal record.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 15/18 - Speech classification or search using natural language modelling
G06F 16/215 - Improving data qualityData cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
Methods and systems for natural language processing/understanding of voice conversations are provided. Using natural language processing, a clinical condition is extracted from a voice conversation. A clinical ontology identifies clinical concepts associated with the clinical conditions. The clinical concepts are classified for documentation. The clinical concepts are searched and validated from within an individual's longitudinal record.
G10L 17/00 - Speaker identification or verification techniques
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
89.
Context-sensitive health outcome surveillance and signal detection
Systems, methods and computer-readable media are provided for facilitating patient health care through automatic discovery, establishment, and statistical validation of safety signals in repositories of EMR information, identifying safety signals regarding therapeutics or therapeutics combinations administered to patients. In an embodiment, a method includes identifying, from a dataset of health care records, a first group of patients that received a drug; extracting one or more medical events that the first group experienced; identifying a second group of patients similar to the first group who have received one or more concomitant comparator drugs that address the same set of conditions that prevail in both the first group and second group; extracting one or more medical events that the second group of patients have experienced; and comparing the medical events of the first group to the medical events of the second group to determine statistically significant differences in occurrences between the groups.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
90.
Pressure injury prevention sensor and decision support tool
Technologies are provided for determining a likelihood or risk of a human patient for developing pressure injury based on patterns of movement by the patient determined using a specialized measurement support surface. Some embodiments automatically ascertain whether patterns of movements of a human subject, whose weight is supported on the support surface, exhibit sufficient frequency and variability of activity such as will confer either certain health benefits or expose the individual to certain health risks, such as development of pressure ulcers in the load-bearing skin and soft tissues. If the determined patterns manifest such features associated with benefits or risks, then a notification may be provided for the patient to move; for instance, to the patient or a caregiver. In some embodiments, a notification may be provided at an irregular time intervals within-day intervals.
Technologies are provided for determining deficiencies in narrative textual data that may impact decision-making in a decisional context. A candidate text document and a reference corpus of text may be utilized to generate one or more topic models and document-term matrices, and then to determine a corresponding statistical perplexity and probabilistic coherence. Statistical determinations of a degree to which the candidate deviates from the reference normative corpus are determined, in terms of the statistical perplexity and probabilistic coherence of the candidate as compared to the reference. If the difference is statistically significant, a message may be reported to user, such as the author or an auditor of the candidate text document, so that the user has the opportunity to amend the candidate document so as to improve its adequacy for the decisional purposes in the context at hand.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A system, methods, and computer-readable media are provided for the automatic identification of patients having an elevated near-term risk of pulmonary function deterioration or respiratory distress. Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent respiratory events in humans, and in one embodiment take the form of a platform for wearable, mobile, untethered monitoring devices with embedded decision support. Respiratory information is obtained over one or a plurality of previous time intervals, to classify a likelihood of events leading to an acute respiratory decompensation event within a future time interval. In an embodiment, the risk prediction is based a plurality of nonlinearity measures of capnometry information over the previous time interval(s), and the risk for an acute respiratory decompensation event determined using an ensemble model predictor on the nonlinearity measures.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of non-downloadable computer
software for use in predicting, tracking, analyzing, and
reporting pharmaceutical market fluctuations, product
demand, and sales expectations.
94.
Indicator For Probable Inheritance Of Genetic Disease
Systems, methods and computer-readable media are provided for identification of patients or family member having genetic disease or probable genetic disease. During or after registration of a patient, parents, grandparents, or siblings of the patient are identified. If it is determined that one of the patient or the parents, grandparents, or siblings of the patient has been assigned with a diagnosis indicating a genetic disease, an alert for genetic disease or probable genetic disease for the patient or family member of the patient is provided. A clinician is then prompted to confirm or rule out the patient or family member inheriting the disease.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
96.
Systems and methods for enhancing natural language processing
Methods and systems for enhanced natural language processing of clinical documentation are provided. Using natural language processing, a clinical condition is extracted from unstructured data within a current electronic document. A clinical ontology identifying itemsets associated with the clinical condition is retrieved, and indicators of relevant clinical concepts, as identified from the ontology, are searched from within the patient's longitudinal record, which comprises documentation from at least a prior encounter. Based on the whether the clinical concepts are present in the patent's record, a confidence is assigned to the NLP-extracted clinical condition, and one or more actions may be performed.
An improved decision support tool is provided for detecting (or for diagnosing or treating human patient at risk for developing) a functional gastrointestinal condition, such as irritable bowel syndrome (IBS). The decision support tool, which may comprise a smart sensor, determines microbiota diversity, relative abundances of microbial taxa, trends in the relative abundances, and concentrations of immunoglobulin G subclasses, from specimens from the subject, and combines these determined values using a classifier to automatically ascertain whether changes or trends in the values are statistically significant and clinically actionable with respect to diagnosing and managing the subject's condition. The decision support tool may further initiate an intervening action based on this determined joint significance, such as generating an electronic notification, modifying a treatment program, providing a recommendation, automatically allocating health care resources to the patient, or automatically scheduling a consultation with a caregiver.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
Systems, methods and computer readable media are provided for determining patient risk of participating in a physical therapy digital home exercise program. The patient risk is generated by one or more artificial intelligence/machine learning (AI/ML) models. Based on the patient risks compared to the benefits, one or more actions may be initiated to create or modify a digital home exercise program for a patient.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Systems, methods and computer readable media are provided for determining patient risk of participating in a physical therapy digital home exercise program. The patient risk is generated by one or more artificial intelligence/machine learning (AI/ML) models. Based on the patient risks compared to the benefits, one or more actions may be initiated to create or modify a digital home exercise program for a patient.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Systems, methods and computer readable media are provided for determining patient risk of participating in a physical therapy digital home exercise program. The patient risk is generated by one or more artificial intelligence/machine learning (AI/ML) models. Based on the patient risks compared to the benefits, one or more actions may be initiated to create or modify a digital home exercise program for a patient.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
