Reciprocal Labs Corporation

Other

John David Van Sickle

Gregory F Tracy

Christopher Hogg

Leah J Morrell

Mike Lohmeier

Dmitry Stupakov

Meredith Ann Barrett

Eric Hoffman

Justin Conrad Krosschell

David Hubanks

John Kalmi

Samuel A Koblenski

Benjamin J Kopp

Greg Tracy

Ki Hong Han

Michael J Tuffli

Shannon M Hamilton

Charles Clayton Donaldson

Amber Michelle Markey

Mark Gehring

Sam Koblenski

Robert Austin Lee

Ben Kopp

Kevin Houlihan

Chris Hallberg

Chris Hogg

Mark William Sehmer

Olivier Humblet

Kelly Anne Henderson

Jason Grosz

Jeffrey J Alexander

Chase Acton

Guangquan Su

Michael Lohmeier

Gary J Reed

Nicholas John Hirons

Lucas Karl Dailey

Ian Daniel Alderman

Jim M Franklin

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

Systems and methods are described for monitoring usage of a medicament dispenser—particularly an inhaled medicament dispenser. The usage monitoring device is configured to emit a detection signal responsive to receiving a request to locate the medicament dispenser. The detection signal alerts presence of the medicament dispenser to the user of the medicament dispenser. The usage monitoring device can be embodied as an attachment configured to be selectively coupled to one of a variety of different inhalers including, for example, a Diskus-type inhaler and a Respimat-type inhaler.

Usage monitoring attachment for medicament dispenser

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 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 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

This description provides trigger identification notifications to patients suffering from respiratory diseases based on large amounts of patient data in order to help effect behavior changes in a patient to prevent inhaler rescue usage events from occurring. Rescue medication events, environmental conditions, and other contextually relevant patient information are detected by sensors associated with the patient's medicament devices and are collected from other sources, respectively to provide a basis to determine identify various triggers of rescue inhaler usage events for a patient. Each trigger is analyzed to determine the severity of the patient's reaction to the trigger and is used to send notifications accordingly.

Identification of asthma triggering conditions based on medicament device monitoring for a patient

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

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.

Systems and methods are described for monitoring usage of a medicament dispenser&#x2014;particularly an inhaled medicament dispenser. While operating in a low-power mode, a first sensor is monitored for a signal indicative of handling of the medicament dispenser by a user. In response to detecting such handling, the device exits the low-power mode and begins to monitor a second sensor for a signal indicative of dispensing of the medicament. The usage monitoring device can be embodied as an attachment configured to be selectively coupled to one of a variety of different inhalers including, for example, a Diskus-type inhaler and a Respimat-type inhaler.

Growing community of inventors

Oregon, WI, United States of America

John David Van Sickle