Reciprocal Labs Corporation

Resmed Limited

Gregory F Tracy

John David Van Sickle

Samuel A Koblenski

Leah J Morrell

Amber Michelle Markey

Mike Lohmeier

Christopher Hogg

Justin Conrad Krosschell

Dmitry Stupakov

Benjamin J Kopp

David Hubanks

John Kalmi

Meredith Ann Barrett

Ki Hong Han

Robert Louis Baddeley

Charles Clayton Donaldson

Chris Hogg

Michael J Tuffli

Olivier Humblet

Robert Austin Lee

Jason Grosz

Jeffrey J Alexander

Kelly Anne Henderson

Chase Acton

Guangquan Su

Mark William Sehmer

Eric Hoffman

Shannon M Hamilton

Sam Koblenski

Ben Kopp

Michael Lohmeier

Nicholas John Hirons

A medicament device sensor (Sensor) comprises a body, a front wall, a back wall, an audio sensor, and a plurality of pressure sensors. The body includes a bottom surface on which the various sensors are coupled. The front wall and the back wall provide mirror symmetry in at most one plane, thereby defining a single orientation in which the Sensor attaches to the medicament device. The front wall includes a securement clip that attaches to a ledge on the medicament device. To detect secure attachment of the Sensor to the medicament device, one pressure sensor detects pressure when the Sensor is secured to the medicament device. Another one or more pressure sensors detect depression of one or more dosing buttons confirming priming of the medicament device. The audio sensor detects an inhalation confirming dispensing of medicament. In effect, the Sensor determines a dispensing event has occurred.

Single dose inhaler monitoring attachment

Modular adherence monitor for attachment to an inhaler

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

A respiratory disease analytics system provides respiratory disease risk reports to a patient, provider, or third-party entity describing a patient's risk of experiencing a medication usage event given data in a geographic region. Regional data, including air pollutant conditions, weather conditions, demographic information, built environment factors, and regional health conditions for a geographic region are accessed from other sources and assigned based on event data recorded during a medicament usage event, as collected by sensors associated with the patient's medicament device/s. The regional data is assigned to medicament usage events occurring within a period of time. The assigned regional data is analyzed to determine an expected number of medication usage events for the geographic region occurring over the period of time.

Evaluation of respiratory disease risk in a geographic region based on medicament device monitoring

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

Semi-integrated sensor monitor for attachment to an inhaler

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 present disclosure describes a computer-implemented method for updating a plurality of parameters on a medical inhaler sensor via a wireless connection by a client device. The client device stores the plurality of parameters for the medical inhaler sensor on the client device. The client device updates one or more parameters of the plurality of parameters on the client device. The client device receives a plurality of periodic chirps from the medical inhaler sensor, and can establish the wireless connection with the medical inhaler sensor in in response to having received a chirp. Through the wireless connection, the client device transmits the updated one or more parameters of the plurality of parameters to the medical inhaler sensor.

Decreased latency wireless communication for use with medicament devices

An application server predicts respiratory disease risk, rescue medication usage, exacerbation, and healthcare utilization using trained predictive models. The application server includes model modules and submodel modules, which communicate with a database server, data sources, and client devices. The submodel modules train submodels by determining submodel coefficients based on training data from the database server. The submodel modules further determine statistical analysis data and estimates for medication usage events, healthcare utilization, and other related events. The model modules combine submodels to predict respiratory disease risk, exacerbation, rescue medication usage, healthcare utilization, and other related information. Model outputs are provided to users, including patients, providers, healthcare companies, electronic health record systems, real estate companies and other interested parties.

Predictive modeling of respiratory disease risk and events

Modular inhaler adherence monitor

Growing community of inventors

Madison, WI, United States of America

Gregory F Tracy