Embodiments diagnose an emergency room (“ER”) patient. Embodiments receive an identifier of the patient and search and retrieve relevant information factors for the patient from publicly available sources using the identifier using a trained machine learning (“ML”) model. The trained ML model is configured to identify and fetch medically relevant information of the patient in response to the identifier of the patient. Embodiments weight each of the retrieved factors relative to contributing to a diagnoses of the patient and assign a score to each of the retrieved factors and provide the scores and corresponding diagnoses to ER personnel.
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 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
2.
DECISION SUPPORT SYSTEMS FOR DETERMINING CONFORMITY WITH MEDICAL CARE QUALITY STANDARDS
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
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
4.
Automated Artificial Intelligence Based Medical Procedure Code Determination
Embodiments determine a medical procedure code. Embodiments receive a description of a medical procedure comprising unstructured data and structured data. Embodiments provide the description to a trained machine learning (“ML”) model, the ML model being trained with training data comprising a database of medical procedure codes and historical documentation and corresponding medical procedure codes for a category of patients. Embodiments generate, by the trained ML model, one or more predicted medical procedure codes corresponding to the description.
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
Systems and methods for providing a financial/clinical data interchange are provided. The financial/clinical data interchange provides a distributed implementation to a secure hash key (i.e., a token) and value pair data derived from a medical claim (e.g., patient identification, submitter identification, payer identification, encounter identification, and the like) and enriched with submitter-based domain data. The token may be used as a data attribute in an API that unlocks a pointer to the value (e.g., a fast healthcare interoperability resources (FHIR) uniform resource identifier (URI) to the patient encounter associated with the claim) to leverage a FHIR query for all documented medical records associated with the claim the payer is authorized by the submitter to view.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
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.
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
7.
Concept Mapping Using Fine-Tuned Large Language Models
Techniques for fine-tuning a pre-trained vector embedding model for recommending standard codes for mapping with proprietary codes are disclosed. Proprietary codes, as referred to herein, include reference codes particular to organizations or vendors. Standard codes, as referred to herein, are industry or standardized codes. The system access a candidate set of standard codes that have been mapped to one or more proprietary codes. The system determines a number of times a standard code is mapped to a proprietary code. Standard codes that have been mapped to proprietary codes a number of times that meet a threshold are selected to be included in a training set for fine-tuning the pre-trained vector embedding model. Standard codes with a number of mappings that does not meet the threshold are not included in the training set. The system may also use vector embedding models for generating aggregated datasets for datasets of the training set.
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
8.
Advanced Simulation Management Tool For A Medical Records System
Systems for testing computing devices of a medical records system using a simulated environment are provided. The simulated environment is generated by identifying transaction traffic over the network, where the transaction traffic comprises messages of different transaction types. A computing device is configured to output a simulated status by extrapolating a performance of the computing devices upon receiving a series of messages indicating the transaction types. The output of the computing device may indicate an error in the simulated environment where the performance of a computing device exceeds a performance threshold. Based on the error indication, transaction traffic can be diverted from the computing device to another computing device having performance capacity.
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
Techniques for early prediction of medical billing classification codes and associated medical billing costs using routine clinical text are disclosed. The system predicts the medical billing codes within defined hours of admission by generating vector embeddings from a set of medical notation data, bypassing the need for post-discharge medical codes. Using a novel segmentation technique, the system processes lengthy medical notation data by dividing them into smaller subsequences. These subsequences are input to a large language model (LLM) to generate a plurality of sets of probability values for a set of medical billing classifications. The system selects a particular predicted medical billing classification for the patient based on the sets of probability values. Additionally, the system estimates medical billing costs early in the admission process. The system ensures comprehensive context utilization from clinical notes, enabling hospitals to manage treatment expenses proactively and improve operational efficiency.
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
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
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 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
12.
Deduplicating And Grouping Medication Events Using Concept Mapping Of Free Text With Large Language Models
Techniques for generating recommendations of standard medication codes for storing in association with medication free text to facilitate deduplication of patient medication events are disclosed. Standard medication codes are alphanumeric identifiers that represent medication events. Medication free text is medication event information in natural language. The system generates vector embeddings for the standard medication codes by applying a vector embedding function to a set of attributes associated with the standard medication codes. The system generates a vector embedding for a target unmapped medication code by applying the vector embedding function to medication free text of the target unmapped medication code. The system compares the target vector embedding for the target unmapped medication code to the vector embeddings computed for each of the standard medication codes. The system presents recommended standard medication codes and groupings of similar standard medication codes to a user for mapping to the medication free text.
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
13.
Deduplicating And Grouping Allergy Events Using Concept Mapping Of Free Text With Large Language Models
Techniques for generating recommendations of standard codes for storing in association with allergy free text to facilitate deduplication of patient allergy events are disclosed. Standard codes are alphanumeric identifiers that represent allergy events. Allergy free text is allergy event information in natural language. The system generates vector embeddings for the standard codes by applying a vector embedding function to a set of attributes associated with the standard codes. The system generates a vector embedding for a target unmapped allergy code by applying the vector embedding function to allergy free text of the target unmapped allergy code. The system compares the target vector embedding for the target unmapped allergy code to the vector embeddings computed for each of the standard codes. The system presents recommended standard codes and groupings of similar standard codes to a user for mapping to the allergy free text.
G06F 16/174 - Redundancy elimination performed by the file system
G06F 16/14 - Details of searching files based on file metadata
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
14.
Context-aware post traumatic stress disorder monitoring and intervention
A method, computer-readable media, and system associated with psychiatric and physiologic monitoring of a patient having PTSD or anxiety-related mental health conditions are provided. For example, data associated with a survey or questionnaire that was provided to a patient is received. A score may be determined from the data associated with the survey or questionnaire. A time-series is formed using heart-rate measurements detected by a sensor. Fractal properties, such as a Hurst exponent, may be calculated for the time-series. The score may be contextually matched to a set of reference scores. Further, the contextual matching may be used to determine a range of normative HRV values for determining whether a value associated with the patient is within the range. If the value associated with the patient is not within the range, a notification may be provided.
A computer-implemented trading platform, system, and method are provided for converting a health quality offset credit that includes retiring an offset credit verified under a first verification standard, verifying a new offset credit under a second verification standard, the new offset credit being essentially equivalent to the offset credit at least in terms of representing a desired reduction in emissions, and registering the new offset credit in a registry to an owner thereof.
Systems and methods for providing a financial/clinical data interchange are provided. The financial/clinical data interchange provides a distributed implementation to a secure hash key (i.e., a token) and value pair data derived from a medical claim (e.g., patient identification, submitter identification, payer identification, encounter identification, and the like) and enriched with submitter-based domain data. The token may be used as a data attribute in an API that unlocks a pointer to the value (e.g., a fast healthcare interoperability resources (FHIR) uniform resource identifier (URI) to the patient encounter associated with the claim) to leverage a FHIR query for all documented medical records associated with the claim the payer is authorized by the submitter to view.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
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
18.
Discovering context-specific serial health trajectories
Systems and methods are provided for injury characterization including detecting matches of an individual subject's record (such as an athlete's record) with collections of other subjects records, based on serial, longitudinal patterns, for facilitating athlete health and training, preventive and rehabilitation medicine, and risk management in athletics. In an embodiment, time series are formed from information pertaining to successive longitudinal episodes of injury and the circumstances in which the injuries were incurred; calculating time-series K-nearest-neighbor clusters and distances for each such combination; determining the cluster to which a given candidate player injury record is nearest or belongs, and prescribing an injury-risk reduction intervention specific to the plurality of hazards that are characteristic of trajectories that are members of that cluster and that are deemed to be relevant to reducing or mitigating those hazards and thereby are efficacious in preventing subsequent injuries that are prevalent in that trajectory cluster.
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 50/30 - 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
19.
DATA EXTRACTION AND DATA-STRUCTURE TRANSFORMATION FOR IMPROVING DATA ACCESSIBILITY AND USAGE CONSISTENCY
A system and computer-implemented method includes querying a data source for treatment plan templates based on a medical condition of a patient. Raw data related to treatment plan templates is received from the data source. The raw data includes text information. Raw data is parsed, and tags are identified. elements are extracted from the tags using complex regular expression and keywords. Formatted data is constructed from the elements in a machine-readable format. The elements are arranged in a hierarchical structure within the formatted data. Structured data having data frames is generated based on the formatted data. The structured data is provided to a medical provider for review. An update in the structured data is received from the medical provider based on a deviation in schedule provided in the treatment plan templates. The treatment plans are generated based on incorporation of the update into the structured data.
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
Techniques for generating recommendations of model domain entities from a model domain for mapping to comparison domain entities from a comparison domain are provided. A model domain includes a code set of standard references codes. A comparison domain includes a code set of reference codes that include non-standard reference codes. The reference codes represent clinical and non-clinical health concepts and are represented by one or more attributes. The system generates vector embeddings for entities of the comparison and model domains by applying a vector embedding function to the attributes fields of the comparison and model domain entities. The system compares the vector embeddings of the comparison domain entity to the vector embeddings of the model domain entity to compute similarity metrics for the entity pairs. The entity pairs are presented to a user based on the similarity metrics. A selected model domain entity is mapped to the comparison domain entity.
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
21.
Computer-based measurement of production events using automated timers across multiple client-server systems
The activities performed by an organization are conventionally identified using activity analysis. This involves determining what activities are done within the department, how many people and of which skills perform the activities, how much time they spend performing the activities, what resources are required to perform the activities, what operational data best reflect the performance of the activities, and the value of the activities to the organization. Historically, these determinations involved extensive interviewing and time-and-motion data gathering. However, some embodiments provided herein facilitate using an automated response time measurement system for measuring and characterizing activities for presentation on a user interface using online data that accrue as a byproduct of the performance of the activities.
A method is performed by one or more processors. The method includes generating a non-executable code template that includes reusable code that defines a set of operations; determining project variables that pertain to a plurality of projects of the set of operations; determining project logic that pertains to the plurality of projects; generating an executable code by integrating the project variables and the project logic into the non-executable code template; and generating a plurality of results by executing the executable code. The plurality of results of the plurality of projects are attained by using the reusable code populated with the project variables and the project logic respectively corresponding to each of the plurality of projects.
A catastrophe-theoretic approach is provided for predicting an occurrence of an acute inflammatory condition or event (e.g., SIRS or sepsis) for a human patient based on a time series of monitored vital signs values measured from a patient, and in some instances, for providing advanced notice to clinicians or caregivers when such an acute inflammatory event is forecasted or modifying treatment for the patient, according to the predicted likelihood. In particular, an acute inflammatory condition management system is provided for determining a likelihood of near-term future significant acute inflammation in human patients. Embodiments of the disclosure described herein may provide a forecasted risk for future significant acute inflammation within a time horizon comprising a future time interval. In one embodiment, the future time interval is from 30 min to approximately 8 hours into the future, and may be dependent on the frequency of vital signs measurements.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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 5/021 - Measuring pressure in heart or blood vessels
Embodiments optimize a user interface (“UI”) of an application for a user. Embodiments train a machine learning (“ML”) model on one or more optimized routes for navigating the UI to arrive at a desired result. Embodiments monitor at least a portion of a first navigation route during a user interaction with the UI to achieve the desired result. Embodiments determine by the ML model that the first navigation route is not the one or more optimized routes and redirect the user to one of the optimized routes during the user interaction.
Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.
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 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
26.
CONFIGURABLE VALIDATION SYSTEM FRAMEWORK AND TOOL FOR DATA MIGRATION
Systems, methods, and other embodiments are described that are associated with a configurable validation system and framework that may be used for data exchange or migration. The system provides a specification neutral design for validating a source dataset. The configurable validation system is configured to receive a specification configuration associated with a selected source dataset, wherein the specification configuration provides at least scanning parameters and formatting rules that are input to configure the configurable validation system to validate the selected source dataset. The configurable validation system is reconfigurable to scan and validate a different source dataset in response to receiving a different specification configuration without reprogramming the configurable validation system.
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
Technologies are provided for providing a dashboard interface comprising a plurality of API tools for accessing 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
29.
OBSTRUCTIVE SLEEP APNEA PREDICTION AND ANALYTICAL REASONING USING HYPERPARAMETERS FOR ACCURATE MODELING OF RISK
The system and methods for predicting sleep apnea in subjects using machine-learning models. The method entails collecting a dataset from a variety of data sources such as electronic records, sleep audio data, or biometric sensor data from wearable devices. A set of features are extracted from the dataset including demographic features, comorbidities features, anthropometric features, or sleep history features. The method generates a set of compound features by performing transformations on the set of features. The extracted set of features and generated set of compound features are collated into a compound feature vector used for training the machine-learning models. The models accurately predict the sleep apnea intensity score which is mapped to a sleep apnea category such as controlled, mild, moderate, or severe apnea. Based on the predicted sleep apnea category treatment regimens as action strategies are recommended to the users.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
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 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
Techniques for generating recommendations of candidate standard codes for association with unmapped proprietary codes are disclosed. Initially, the system generates vector embeddings for mapped standard codes by applying a vector embedding function to datasets of proprietary codes that are mapped to the respective mapped standard codes. The system generates vector embeddings for unmapped standard codes by applying a vector embedding function to a dataset of the unmapped standard codes. The system compares a target vector embedding for a target unmapped proprietary code to the vector embeddings computed for each of the mapped and unmapped standard codes. Based on a similarity measure between the target vector embedding and the vector embeddings for the mapped and unmapped standard codes, the system selects a subset of the mapped and unmapped standard codes for recommending to the user as a set of candidate standard codes for mapping to the target unmapped proprietary code.
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 other embodiments associated with error resolution and auto-correction are described. In one embodiment, a method includes, for a selected application that was executed by the computing system, accessing a system error log and identifying error messages that occurred during execution. Skipped data records that were not processed are identified. A resolving function is executed to resolve a selected error type that is associated with a group of skipped data records by: identifying an error pattern from an error message and matching it to a database of observed error patterns. In response to a match, retrieving a corrective action that is assigned to the observed error pattern and executing the corrective action to resolve the error on the group of skipped data records. The skipped data records are re-submitted and reprocessed where previous error should be resolved.
Methods, systems, and computer-readable media are disclosed herein for a machine learning engine and rule engine that leverage historical and contextual data to intelligently identify, score, and suggest one or more documents for auto-population of a graphical user interface. The machine learning and rule engine employ vectorization and clustering technique to identify, score, and suggest the most factually accurate and contextually relevant documents as selectable candidates for electronic documentation. Further, the selection, rejection, or modification of the candidate documents are ingested by the machine learning engine and/or the rule engine to update a clustering algorithm and/or to update a relevance scoring algorithm, which are then utilized for subsequent instances.
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
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
34.
Discovering Context-Specific Complexity And Utilization Trajectories
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 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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
35.
Discovering Context-Specific Complexity And Utilization Trajectories
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 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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
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
Methods, systems and computer storage media are disclosed for providing resources to a platform issue. Embodiments describe associating educational resources and an event resource to resolve the platform issue.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
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
38.
Clinical decision support system using phenotypic features
Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.
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 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 computer-readable media are provided for identification of patients having an elevated near-term risk of chronic kidney disease progression, including quantitatively predicting whether or not an elevated risk of progression of Stage 3 or Stage 4 chronic kidney disease is likely within a time interval of up to 36 months subsequent to computing the prediction. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In some embodiments, serial measurements are obtained of urine osmolality, and a challenge with an AVP V2 antagonist and serum sodium concentration is provided. From a time series based on the serial measurements, estimates of each variable's velocity and/or doubling-time may be determined. These values then may be combined via a multivariable mathematical model for providing a leading indicator of near-term future abnormalities in kidney function.
Decision support technology is provided for use with patients prone to recurrent urolithiasis. A mechanism is provided to determine a forecast of urolithiasis for a patient over a future time interval. The forecast may be based on temporal patterns in urinalysis parameters of the patient. In one embodiment, the mechanism utilizes a time series Hölder exponent and recurrence quantification analysis (RQA) recurrence rate to generate a forecast of recurrent symptomatic urolithiasis for a future time, such as a multi-year time horizon. Based on the generated forecast, one or more intervening actions may be carried out automatically or may be recommended, including modifying a care program for the patient, automatically scheduling interventions or consultations with specialist caregivers, or generating notifications such as electronic messages or alerts.
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
G01N 33/493 - Physical analysis of biological material of liquid biological material urine
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G01N 33/84 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving inorganic compounds or pH
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
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
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
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
43.
Discovering Context-Specific Complexity And Utilization Trajectories
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
Various tools are disclosed for providing assistive or augmentative means to enhance the fluency and accuracy of persons having speech disabilities. These technologies may automatically ascertain and dynamically improve the accuracy with which automatic speech recognition (ASR) systems recognize utterances of persons having impaired speech conditions. In an embodiment, digitized audio information about a speaker's utterance is processed to determine a set of candidate words matching the utterance. From these candidate words, a set of concepts is determined using a finite state machine model. A pictogram representing each concept is identified and presented to the speaker so that the speaker may select the pictogram corresponding to the best match of his or her intended meaning associated with the utterance. An action corresponding to speaker's selection then may be performed. For example, displaying or synthesizing speech from textual information describing the selected concept.
G10L 25/54 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for retrieval
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
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 50/30 - 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
46.
Dynamic Management Of Treatments For One Or More Conditions
Computerized systems and methods intelligently and dynamically manage treatment for one or more conditions for an individual. A manager is programmed to provide one or more active treatments associated with one or more conditions for an individual and then retrieve the one or more active treatments associated with the one or more conditions. The system communicates at least one of the one or more active treatments to a treatment repository. A first criteria is identified, which is applied to one or more potential treatments identified, resulting in the identification of a first subset. Additionally, a second criteria is identified and applied to the first subset, resulting in a second subset comprising potential treatments that satisfy the first and second criteria. The second subset is presented to a user so that the user may make treatment decisions related to the one or more conditions for the individual.
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 16/90 - Details of database functions independent of the retrieved data types
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 50/50 - 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 5/02 - 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
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.
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 50/50 - 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 5/02 - 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
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
Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based on phenotype and genomics information.
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 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
C07K 16/36 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against blood coagulation factors
C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
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
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
A decision support method and system is provided for monitoring and treating pediatric obesity. Embodiments include generating obesity risk curves corresponding to obesity risk levels, for example, severe and morbid obesity risk levels. Generating obesity risk curves depends on predicting at least one health proxy such as, for example, spend data and chronic conditions. Generating severe obesity curves depends on an age-dependent multiplier. An obesity risk level is assigned to a target pediatric patient using the obesity risk curves dependent on the age-dependent multiplier. In some aspects, an intervening response is initiated based on the assigned obesity risk level.
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 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 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 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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 40/63 - 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
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
52.
STRUCTURED DATA SHARING USING DIGITALLY ENCODED IMAGES
Techniques relate to efficiently encoding data. A data set in a first specific format is received. The data set is transformed into data-string values in a second specific format, where the data-string values includes a set of data-string values, wherein each of the set of data-string values is represented by an intensity for each of one or more channels. A digitally encoded image is generated by transforming the data-string data into an image comprising a pixel value for each of a set of pixels, wherein the pixel values for the set of pixels have at least three different intensities and/or at least three different colors relative to each other. The transformation may include generating the data set or a representation thereof in a hexadecimal format. The digitally encoded image is output.
G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/60 - Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
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
54.
Discovering Context-Specific Complexity And Utilization Trajectories
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
55.
Discovering Context-Specific Complexity And Utilization Trajectories
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
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
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
Various tools are disclosed for providing assistive or augmentative means to enhance the fluency and accuracy of persons having speech disabilities. These technologies may automatically ascertain and dynamically improve the accuracy with which automatic speech recognition (ASR) systems recognize utterances of persons having impaired speech conditions. In an embodiment, digitized audio information about a speaker's utterance is processed to determine a set of candidate words matching the utterance. From these candidate words, a set of concepts is determined using a finite state machine model. A pictogram representing each concept is identified and presented to the speaker so that the speaker may select the pictogram corresponding to the best match of his or her intended meaning associated with the utterance. An action corresponding to speaker's selection then may be performed. For example, displaying or synthesizing speech from textual information describing the selected concept.
G10L 25/54 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for retrieval
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
59.
Systems And Methods For Refactoring A Knowledge Model To Increase Domain Knowledge And Reconcile Electronic Records
Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.
G06N 5/046 - Forward inferencingProduction 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 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 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 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based on phenotype and genomics information.
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 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
C07K 16/36 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against blood coagulation factors
C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
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
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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
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
63.
Predicting Glycogen Storage Diseases (Pompe 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 or glycogen storage diseases, such as late-onset Pompe disease.
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 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
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
A decision support method and system is provided for monitoring and treating pediatric obesity. Embodiments include generating obesity risk curves corresponding to obesity risk levels, for example, severe and morbid obesity risk levels. Generating obesity risk curves depends on predicting at least one health proxy such as, for example, spend data and chronic conditions. Generating severe obesity curves depends on an age-dependent multiplier. An obesity risk level is assigned to a target pediatric patient using the obesity risk curves dependent on the age-dependent multiplier. In some aspects, an intervening response is initiated based on the assigned obesity risk level.
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 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 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 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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 40/63 - 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
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
A decision support method and system is provided for monitoring and treating pediatric obesity. Embodiments include generating obesity risk curves corresponding to obesity risk levels, for example, severe and morbid obesity risk levels. Generating obesity risk curves depends on predicting at least one health proxy such as, for example, spend data and chronic conditions. Generating severe obesity curves depends on an age-dependent multiplier. An obesity risk level is assigned to a target pediatric patient using the obesity risk curves dependent on the age-dependent multiplier. In some aspects, an intervening response is initiated based on the assigned obesity risk level.
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 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 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 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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 40/63 - 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
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
67.
Systems And Methods For Refactoring A Knowledge Model To Increase Domain Knowledge And Reconcile Electronic Records
Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.
G06N 5/046 - Forward inferencingProduction 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 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 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 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based on phenotype and genomics information.
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 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
C07K 16/36 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against blood coagulation factors
C07K 16/44 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material not provided for elsewhere
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
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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 50/50 - 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 5/02 - 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
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
70.
Time-Based Healthcare Data Management with Partitioned Storage and Compaction
Methods, systems, and computer-readable media are provided for delivering healthcare records with low latency. Healthcare data is collected from various disparate healthcare data sources. The data is filtered in accordance with routing rules to identify healthcare data to deliver to a processing node. The routing rules specify that healthcare data from a particular originating source of a particular data type is to be delivered to a particular processing node. The healthcare data is converted to a local format for use by a computing solution. This system of delivering healthcare data with low latency ensures that the data is delivered to the correct location in the correct format, even if the routing rules change.
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 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
71.
Predicting Recurrent Urolithiasis And Decision Support Tool
Decision support technology is provided for use with patients prone to recurrent urolithiasis. A mechanism is provided to determine a forecast of urolithiasis for a patient over a future time interval. The forecast may be based on temporal patterns in urinalysis parameters of the patient. In one embodiment, the mechanism utilizes a time series Hölder exponent and recurrence quantification analysis (RQA) recurrence rate to generate a forecast of recurrent symptomatic urolithiasis for a future time, such as a multi-year time horizon. Based on the generated forecast, one or more intervening actions may be carried out automatically or may be recommended, including modifying a care program for the patient, automatically scheduling interventions or consultations with specialist caregivers, or generating notifications such as electronic messages or alerts.
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
G01N 33/493 - Physical analysis of biological material of liquid biological material urine
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G01N 33/84 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving inorganic compounds or pH
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
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
72.
Predicting Recurrent Urolithiasis And Decision Support Tool
Decision support technology is provided for use with patients prone to recurrent urolithiasis. A mechanism is provided to determine a forecast of urolithiasis for a patient over a future time interval. The forecast may be based on temporal patterns in urinalysis parameters of the patient. In one embodiment, the mechanism utilizes a time series Hölder exponent and recurrence quantification analysis (RQA) recurrence rate to generate a forecast of recurrent symptomatic urolithiasis for a future time, such as a multi-year time horizon. Based on the generated forecast, one or more intervening actions may be carried out automatically or may be recommended, including modifying a care program for the patient, automatically scheduling interventions or consultations with specialist caregivers, or generating notifications such as electronic messages or alerts.
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
G01N 33/493 - Physical analysis of biological material of liquid biological material urine
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
G01N 33/84 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving inorganic compounds or pH
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
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
73.
Predicting Glycogen Storage Diseases (Pompe 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 or glycogen storage diseases, such as late-onset Pompe disease.
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 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
74.
Predicting Glycogen Storage Diseases (Pompe 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 or glycogen storage diseases, such as late-onset Pompe disease.
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 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
75.
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
76.
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
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
78.
Systems And Methods For Refactoring A Knowledge Model To Increase Domain Knowledge And Reconcile Electronic Records
Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.
G06N 5/046 - Forward inferencingProduction 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 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 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 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
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 50/30 - 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
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 50/50 - 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 5/02 - 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
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
81.
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 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
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
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.
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
System and methods for controlling healthcare devices and systems using voice commands are presented. In some aspects a listening device may receive voice command from a person. The voice command may be translated into human readable or machine readable text via a speech-to-text service. A control component may receive the text and send device-specific instructions to a medical device associated with a patient based on the translated voice command. In response to the instructions, the medical device may take an action on a patient. Some examples of actions taken may include setting an alarm limit on a monitor actively monitoring a patient and adjusting the amount of medication delivered by an infusion pump. Because these devices may be controlled using a voice command, in some cases, no physical or manual interaction is needed with the device. As such, multiple devices may be hands-free controlled from any location.
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
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
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
85.
SUBJECT-CENTRIC SMART HOSPITAL WITH CUSTOMIZABLE SMART SERVICES
Techniques disclosed herein provide a layered healthcare medical stack for embodying the architecture of a subject-centric smart hospital (SH) with customizable smart services. A subject centric matrix hosted on a centralized cloud that is configured to provide interoperability, integration, and customization of services for various healthcare organizations. Data is obtained for each subject to create a subject-specific persona for performing prescriptive and predictive analysis thereby generating alerts and trends based on the current and historical healthcare data of a subject. The subject-centric matrix can enable healthcare facilities to choose any service in any layer without a prerequisite service to be in place first. The healthcare medical stack enables dynamically changing services to the subjects as their health profile and status changes such that they get a personalized and customized experience that is created based on the current health status, which is different from the one observed in the previous visit.
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
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
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
86.
OUTREACH COMMUNICATION CONTROLS USING MACHINE LEARNING
A system and method for predicting resource usage using machine-learning models. The method entails collecting a dataset from a variety of data sources such as electronic medical/health records or medical registries. The method identifies if an outreach communication occurred or is scheduled to occur for a subject. A set of features are extracted from the dataset and the method generates derived features from one or more extracted features. The extracted features and generated set of derived features are collated into a candidate feature vector used for training the machine-learning models. The models generate a predicted likelihood of the subject seeking care at the medical facility within a defined time period. Based on the predicted likelihoods of the subjects seeking care at the medical facility, the method predicts an upcoming resource demand at the medical facility. The method generates a recommended action in case predicted resource demand exceeds a threshold.
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
87.
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.
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
92.
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
94.
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
96.
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
98.
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
