Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

Abstract: There is provided a system for tracking events associated with a treatment by personalised medicine, the system comprising a plurality of nodes hosting a blockchain. The plurality of nodes include a plurality of sequence manager nodes, each associated with a respective sequence manager contract on the blockchain, and a hub node associated with a hub contract on the blockchain. A first sequence manager contract of the sequence manager contracts is arranged to receive first event data indicating a first event associated with the treatment, and store the first event data on the blockchain in association with a first event sequence. The hub contract is arranged to store an association between the first event sequence and one or more further event sequences associated with the treatment, on the blockchain.

Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A force profile segmentation model can construct a force profile using the received data. The force profile includes a plurality of force patterns. The force profile segmentation model includes at least one first machine learning trained using historical surgical instrument usage data. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a force profile pattern recognition model using the constructed force profile and the extracted features. The force profile pattern recognition model includes at least one second machine learning model. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).

Abstract: Methods and systems for automatically establishing an enhanced electronic health record (EHR) for a patient include an automatic data collection facility that collects data of a medically related event in proximity to a patient upon occurrence of the event. The collected data may include medication administration data such as medication, time of administration, administration of a dosage of medication, reaction data, and the like. The collected data is communicated to a real-time data integration facility that automatically integrates the data with a patient's electronic health record to establish an enhanced electronic health record.

Abstract: A computer system visualizes medical information for a patient. Natural language processing techniques are applied to identify medical concepts within unstructured text of medical information. Values for the identified medical concepts are derived from combinations of values within the medical information. A visualization is generated for a selected derived value, wherein the visualization includes a plurality of interconnected nodes each associated with a corresponding information source utilized for determining the selected derived value, and wherein paths of the interconnected nodes in the visualization indicate the corresponding information sources utilized to produce the selected derived value. Information of a selected node of the visualization is updated based on user verification of the information of the selected node. The visualization is modified based on the updated information.

Abstract: A system to electronically coordinate and document patient care regardless of physical setting. The system includes a wearable subsystem attached to a patient at the point of injury and configured to remain attached to the patient at the point of injury and during one or more encounters with medical personnel or to a time the patient reaches a clinical health care facility. The wearable subsystem is configured to store patient identification information and critical health care information received via wireless communication from an end user computing device at the point of injury and is configured to store added health care information provided by medical personnel from or at the point of injury and during the one or more encounters with the medical personnel or to a time the patient reaches a clinical care facility.

Abstract: Computer-implemented methods, computer program products, and system facilitating evaluation of a progress of a diagnosis process are provided. The computer-implemented method comprises: obtaining, by a device operatively coupled to one or more processing units, a list of symptom(s); retrieving, by the device, a sub-graph associated with the list of symptom(s) from a knowledge graph; extracting, by the device, a list of feature(s) from the retrieved sub-graph; estimating, by the device, an average number of inquiries based on the extracted list of feature(s); and calculating, by the device, the progress of the diagnosis process based on the estimated average number of inquiries.

Abstract: The present disclosure includes a medical monitoring hub as the center of monitoring for a monitored patient. The hub includes configurable medical ports and serial ports for communicating with other medical devices in the patient's proximity. Moreover, the hub communicates with a portable patient monitor. The monitor, when docked with the hub provides display graphics different from when undocked, the display graphics including anatomical information. The hub assembles the often vast amount of electronic medical data, associates it with the monitored patient, and in some embodiments, communicates the data to the patient's medical records.

Abstract: Technology is directed to an interactive text message based coaching program (“the technology”). A user enrolled in a campaign interacts with a server to obtain guidance on performing a set of tasks of the campaign to achieve a health goal. The user interacts with the server via text messages using a mobile computing device. After the health goal of the user is identified, the server generates text messages having information related to the campaign. The text messages are generated based on the health goal for the user, and are customized for the user. The server sends the text messages to the user in a sequence, wherein a next text message of the sequence sent to the user is based on a response received from the user for a previous text message of the sequence.

Abstract: Aspects of the invention include methods and systems for a vehicle insurance claim management application which improves communication and streamline tasks between a vehicle repair shop and an insurance company system in three general task groups: assignment (comprising work requests, estimates/photos, and reinspections), financial management, and performance measurement. Aspects of the invention also provide interoperability with different types of estimating systems that do not necessarily follow an industry standard. This interoperability is provided through an adapter module with a claim processing system to communicate with the insurance company system and the vehicle insurance claim management application. The invention is preferably accessed by a user over a computer network such as the Internet, from a remote location such as a vehicle repair shop.

Abstract: Systems and methods for estimating quantitative histological features of a subject's tissue based on medical images of the subject are provided. For instance, quantitative histological features of a tissue are estimated by comparing medical images of the subject to a trained model that relates histological features to multiple different medical image contrast types, whether from one medical imaging modality or multiple different medical imaging modalities. In general, the trained model is generated based on medical images of ex vivo samples, in vitro samples, in vivo samples or combinations thereof, and is based on histological features extracted from those samples. A machine learning algorithm, or other suitable learning algorithm, is used to generate the trained model. The trained model is not patient-specific and thus, once generated, can be applied to any number of different individual subjects.

Abstract: A computer system generates a clinical summary for a patient based on machine learning. One or more templates are generated, each indicating medical information for a corresponding clinical summary with respect to a medical condition of a patient. Preferences for medical information for each corresponding clinical summary are learned based on a history of desired medical information for clinical summaries for the medical condition. The learned preferences are applied to the one or more templates. A clinical summary is generated with respect to the medical condition of the patient based on the one or more templates with the learned preferences. Embodiments of the present invention further include a method and program product for generating a clinical summary for a patient based on machine learning in substantially the same manner described above.

Abstract: Using a centralized system, it is possible to allow multiple disparate health care providers to gain a complete view of data regarding a patient's health and health care. Data accessible through such a central system can also be made available for researchers after being de-identified. Data in such a central system can not only include data culled from traditional physical and electronic medical records, but can also include data from distributed diagnostic devices, such as fitness trackers and consumer diagnostic equipment. Such a central system could potentially be accessed through applications made available to patients and health care providers and, in implementations where they are present, such applications could also be used for other purposes, such as performing interactive health evaluations and making recommendations of actions to take to maintain or restore a user's health.

Abstract: Technologies for managing a treatment program include a treatment management server, smart pills, and patient computing devices. The treatment management server is configured to generate treatment data usable by the smart pills to control a release of one or more drugs in patients. The treatment management server is also configured to transmit the treatment data to the smart pills, obtain physiological data associated with the patients, identify a preferred physiological response among the patients based on the physiological data, and identify the treatment data associated with the preferred physiological response. The smart pills are configured to obtain the treatment data, release one or more drugs into the patients based on the treatment data, sense physiological conditions in the patients, and transmit the physiological conditions to the treatment management server. The patient computing devices facilitate communication between the treatment management server and the smart pills.

Abstract: A method and apparatus for receiving nutritional information are disclosed. For example, the method receives a profile of a user, receives over the communications network, grocery information on one or more grocery items at one or more locations of the user, receives over the communications network, ingestion information on ingestion by the user of at least one grocery item from the one or more grocery items, generates nutritional information, wherein the nutritional information is determined by performing an analysis based on the profile of the user, the grocery information, and the ingestion information, and provides over the communications network, the nutritional information, to an endpoint device of the user.

Abstract: Embodiments may include an automated method for evaluating an infection status associated with a blood sample obtained from an individual. Methods may include determining, using a first module, a white blood cell concentration associated with the blood sample. In addition, methods may include determining, using a second module, a monocyte volume measure associated with the blood sample. Methods may include evaluating, using a data processing module, the infection status associated with the blood sample. The data processing module may include a processor and a computer readable medium. The computer readable medium may be programmed with a computer application. This computer application, when executed by the processor, may cause the processor to calculate a parameter using a function comprising the white blood cell concentration and the monocyte volume measure. The computer application may also cause the processor to evaluate the infection status associated with the blood sample based on the parameter.

Abstract: An application file defines a first data object that identifies a fast healthcare interoperability resources (FHIR) server and a second data object that identifies a data type associated with the FHIR server. Based on the first data object and the second data object, a FHIR model requests data of the data type from the FHIR server. The FHIR server provides the requested data, which is received by the FHIR model. Based on the received data, the FHIR model generates data in a user interface (UI) control format and provides the generated data to a UI module that causes a UI to be presented. The FHIR model supports binding of complete entities such as patients or organizations to a UI. This binding enables the requesting of whole entities from a FHIR endpoint, provision of the entity data to the UI, and updating the FHIR data.