West Affum Holdings Dac

Physio-control, Inc.

West Affum Holdings Corp.

Medtronic Physio-control Corp.

Steven E Sjoquist

Jonathan Paul Niegowski

Zoie R Engman

David Peter Finch

Pamela F Breske

Laura Marie Gustavson

Joseph Leo Sullivan

Erick Michael Roane

Douglas Keith Medema

Angela M Stewart

Richard C Nova

Robert Reuben Buchanan

Ronald Eugene Stickney

David R Hampton

Cynthia P Jayne

D Craig Edwards

Stephen W Radons

Dusan Beblavy

Martin Pribula

Peter Curila

Martin Kolesár

Kenneth Frederick Cowan

Garrett McCann Kotlarchik

Zoie R Brent

John C Daynes

Phillip Dewey Foshee, Jr

James W Taylor

Erik L Schneider

Brian D Webster

Mark P Moore

Korine W Haynes

Robert C Birkner

Megan E Briscoe

Heather Mareth

Hema A Ingle

Leo J Gilbert

A medical data management system is provided that facilitates expedited review of health data for patient care. The medical data management system includes devices for collecting the health data, associating the health data with health events, optimizing storage of health data, and displaying comprehensive patient data for efficient and rapid review. The system includes a medical monitoring device that uses sensors to acquire health data and logic to specify data blocks of the data that characterize health episodes. Data management device(s) stitch the data blocks according to timelines and store the stitched data blocks as a full episode. The stitched data blocks are rendered for display, as a data presentation with timelines and graphic representations.

Health data management and display

A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence ('AI') model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Selection of a wearable article for a medical device

In one example, a method to aggregate information from one or more medical devices comprises collecting information from one or more medical devices used by a patient, aggregating the information from the one or more medical devices into a patient record, storing the patient record to be accessible by a care station server, and providing access to the patient record to via the care station server. Other examples and related methods and apparatuses are also disclosed herein.

Analysis and presentation of aggregated patient and device data within a system that includes a medical device

Technologies and implementations for a wearable healthcare system including an information management module (IMM). The IMM may be configured to detect proximity of a healthcare device. The IMM may be configured to determine a type of the detected healthcare device. Once the type of healthcare device is determined, the IMM may be configured to communicate information regarding a medical device, where the communicated information may be adapted to correspond to the determined type of healthcare device.

Providing wearable device information to rescuers

Disclosed are embodiments directed to security methods applied to connections between components in a distributed (networked) system including medical and non-medical devices, providing secure authentication, authorization, patient and device data transfer, and patient data association and privacy for components of the system.

Permission-based control of interfacing components with a medical device

A WCD system is configured to monitor various characteristics of the WCD system including about the patient. The information collected by the WCD is generally referred to as 'patient information.' The WCD system is further configured to transmit certain of the patient information to different recipients based on predetermined profiles with which one or more of the recipients is associated. In various embodiments, different sets or subsets of the patient information may be sent to different recipients.

Wearable defibrillator system forwarding patient information based on recipient profile and/or event type

A WCD system is configured to detect when a therapy administered to a patient by the WCD system is unsuccessful, and in response determine whether to send notifications to remote non-witness responders. The WCD system may be configured to decide to send such notifications after the WCD system determines it has administered a predetermined number of unsuccessful shocks to the patient. The predetermined number of unsuccessful shocks may be the maximum number of unsuccessful shocks the WCD system will administer to a patient, or every Xth shock (e.g., 3shock). The WCD system can be configured to periodically resend the notification. The notifications may be in the form of SMS, voice messages, emails, app notifications, etc. sent to cell phones, smartphones, computers, laptops, tablets, etc. of the responders either directly, via a server, or via a CAD-coupled server.

Wearable cardiac defibrillator systems and methods and software for contacting non-witnessing responders

Disclosed are a timeline-based navigation feature and a channel selection feature. The timeline-based navigation feature allows the user to visually identify significant events during an episode and quickly navigate the display of data to those significant events. Embodiments of such feature allow a user to quickly see that there is a shock delivered, for example, and click on that part of a timeline navigator to advance the detailed display of data to that point in the episode. The data channel selection feature enables selection of display channel(s) from among a set of available data channels. Embodiments of such feature enable a selection and/or deselection of various channels for display to minimize display of extraneous data and/or to select preferred channels of data for review.

Data channel selection and timeline navigation in a cardiac monitoring system

A WCD system is configured to monitor various characteristics of the WCD system including about the patient. The WCD system is further configured to transmit a notification of a notifiable event to responders that are outside a local area of the patient, and to silence the notification for any responder(s) that are inside the local area of the patient.

Wearable cardioverter defibrillator system with remote alerts based on proximity

In embodiments, a wearable medical system (WMS) for an ambulatory patient, which can be a wearable cardioverter defibrillator (WCD) system, analyzes the patient's ECG signal to generate a detection outcome. The WMS also has an ambulatory user interface that outputs a human-visible indication. A programming device, such as a PC, a tablet, etc., establishes a communication link with the WMS during an in-person session with the patient. The programming device may include a programming screen that reproduces the human-visible indication in real time. An advantage can be that the person programming the WMS need not strain to look also at the ambulatory user interface at the time they are looking at the programming device. Another advantage can be that the patient will recognize that he or she is better protected, and have their confidence in the WMS increased, and therefore better comply with wearing the WMS as required.

Wearable cardioverter defibrillation (WCD) system with proximate programming device which stores ECG data that the WCD system normally discards

A wearable cardioverter defibrillator (WCD) system comprises a plurality of patient parameter electrodes and a plurality of defibrillator electrodes to contact a patient's skin when the WCD is delivering therapy to the patient, a processor to receive one or more patient parameters from the one or more patient parameter electrodes, an energy storage device to store a charge to provide electrical therapy to the patient via the plurality of defibrillator electrodes, and a non-invasive blood pressure (NIBP) monitor to obtain a blood pressure measurement of the patient and to provide the blood pressure measurement to the processor. The processor is to determine whether to provide electrical therapy to the patient based on the one or more patient parameters during an episode, and to obtain the blood pressure measurement from the NIBP monitor during the episode.

Wearable cardioverter defibrillator with a non-invasive blood pressure monitor

In one embodiment, a method of alerting a user of a WCD is described. The method includes receiving an alert from the WCD on a remote device associated with the user and in communication with the WCD and transcribing the alert into a message for the user. The method also includes personalizing the message for the user and delivering the message to the user via the remote device.

Detailed alarm messages and support

'Artificial Intelligence' or 'AI' technology can be applied to Wearable Cardioverter Defibrillators (&#x201c;WCDs&#x201d;) and other wearable medical equipment in various ways, including: garment fitting and adjustment; analyzing electrocardiogam (&#x201c;ECG&#x201d;), other sensor data and/or other patient data (e.g., age, gender, previous medical conditions, etc.) in real time to detect/assess the patient's present condition and/or need for treatment for cardiac and other conditions (e.g., stroke, coughing, apnea, etc.); detecting imminent failure of the wearable medical device components; capturing and reporting data collected from the patient for presenting to clinicians; adjusting thresholds for alarms and notifications based on patient's responses; improving patient compliance based on the patient's past non-compliant behavior and actions that resulted in the patient becoming compliant; providing tests to the patient (e.g., grip test, dexterity tests, balance tests, etc.) and learning the patient's responses to detect/assess the patient's present condition and/or need for treatment; learning the patient's voice, activity, posture, time of day, etc. for implementing intelligent voice recognition/activation of the medical device.

Wearable cardioverter defibrillator with AI-based features

Growing community of inventors

Lynnwood, WA, United States of America

Steven E Sjoquist