Reciprocal Labs Corporation

Dmitry Stupakov

John David Van Sickle

Mike Lohmeier

Christopher Hogg

Gregory F Tracy

John Kalmi

Leah J Morrell

Meredith Ann Barrett

Ki Hong Han

Michael J Tuffli

Charles Clayton Donaldson

Shannon M Hamilton

Jason Grosz

Jeffrey J Alexander

Chase Acton

Mark William Sehmer

Robert Austin Lee

Chris Hogg

A system and method for providing chronic obstructive pulmonary disease (COPD) exacerbation risk notifications in real time or near-real time is described. Rescue and controller medication events are detected by sensors associated with the patient's medicament device/s, and provide a basis to determine to rescue and controller medication use trends for the patient. This data is analyzed to determine the patient's risk for COPD exacerbation after each event, and is used to send notifications to one or both of the patient and their health care provider.

Pre-emptive chronic obstructive pulmonary disease risk notifications based on medicament device monitoring

This description relates to insight into the asthma management habits of a patient or a plurality of patients. The provided information includes records of rescue medication usage events, records of controller medication events, and overall evaluations of a patients disease management. Users of the application may include both patients who may use the information to monitor their own habits or providers who may use the information to monitor patients or improve a patient's existing medication regimen.

Dynamic graphical user interface for interaction with patient respiratory disease data

This description provides risk notifications for a geographic region to inform patients and effect behavior changes to prevent rescue events and exacerbations from occurring. A risk analysis for a geographic region is based on data gathered from a population of patients who may experience respiratory events within the geographic region. Rescue medication events, environmental conditions relevant to the rescue medication events, and other contextually relevant information are detected by sensors associated with a population of patients' medicament device/s and are collected from other sources, respectively, to provide a basis to determine a risk score. This data is analyzed to determine the severity of the risk of a respiratory event occurring within a geographical region and accordingly sending a notification to patients within that geographical region.

Pre-emptive asthma risk notifications based on medicament device monitoring

Systems and methods for monitoring accurate, real-time medicament device events, performing analytics on that data, and providing notifications are described. In various embodiments, an application server receives controller medication events, analyzes the events, associated event times, and controller medication dosage plans to characterize event times and send notifications for future doses. The controller medication dosage plan may specify a dose time for a planned dose, a narrow time window comprising the dose time, and an expanded time window comprising the narrow time window and longer in duration than the narrow time window, and the events may be characterized based on their time relative to the dose time, the time windows, and other events.

Real time adaptive controller medication dosing

This description provides asthma risk notifications in advance of predicted rescue usage events in order to help effect behavior changes in a patient to prevent those events from occurring. Rescue medication events, changes in environmental conditions, and other contextually relevant information are detected by sensors associated with the patient's medicament device/s and are collected from other sources, respectively, to provide a basis to determine a patient's risk score. This data is analyzed to determine the severity of the patient's risk for an asthma event and is used to send notifications accordingly.

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Cupertino, CA, United States of America

Dmitry Stupakov