Reciprocal Labs Corporation

Meredith Ann Barrett

John David Van Sickle

Christopher Hogg

Michael J Tuffli

Gregory F Tracy

Mike Lohmeier

Dmitry Stupakov

Shannon M Hamilton

Robert Austin Lee

Mark William Sehmer

Olivier Humblet

Chris Hogg

Kelly Anne Henderson

Guangquan Su

Michael Lohmeier

Amber Michelle Markey

Robert Louis Baddeley

Ki Hong Han

Nicholas John Hirons

Rahul Bhailal Gondalia

Leanne Kaye

David Stempel

Melissa Williams

Lucas Karl Dailey

Ian Daniel Alderman

Systems and methods to determine and ensure proper usage technique of a respiratory medicament device for a respiratory ailment. Medication data relating to a medication plan for a patient is collected and used to determine the respiratory medicament device type in use. For respiratory medicament device types requiring multiple consecutive uses, use data is collected and used to determine proper or improper use technique based on the timing between consecutive uses of the respiratory medicament device. Notifications of proper or improper usage technique are provided based on the determined usage technique. Real-time monitoring of the use data is used to provide directions on the required timing of consecutive uses of the respiratory medicament device and thus ensure proper usage technique.

Systems and methods for improving respiratory medicament device usage

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

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.

Growing community of inventors

Redwood City, CA, United States of America

Meredith Ann Barrett