Abstract: Apparatus and methods for generating pulse pacing in a wearable cardioverter defibrillator (“WCD”). In one aspect the WCD circuitry includes a power source such as a battery coupled to a charger that provides charge energy to an energy storage module. Control circuitry is operatively coupled to the charger and the output circuitry, and configured to cause the WCD circuitry to generate pacing pulses delivered to therapy electrodes (attached to an ambulatory patient) without a current source. The WCD circuitry includes one or more processing elements that are used to execute instructions provided by one or more software modules that are configured to support various functionality, including controlling generation of pacing pulses.

Abstract: A medical device troubleshooting system is provided to interact with and assist a user in resolving operational problems with a medical device. The troubleshooting system may detect an operational problem with the medical device and initiate a troubleshooting session to output a series of different tailored prompts for a user to rectify the glitch. The user prompts are selected and presented in an adaptive manner based on patient specific factors. Selection of prompts and prompt presentation may be responsive to user actions, changes in the patient, and/or the condition of the operational problem. The troubleshooting system checks the condition of the operational problem after presentation of each user prompt and determines a next user prompt accordingly, or ends the session. When the operational problem persists after ending of the session, an external support resource may be contacted.

Abstract: 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.