Reciprocal Labs Corporation

Chris Hogg

John David Van Sickle

Gregory F Tracy

Meredith Ann Barrett

Olivier Humblet

Guangquan Su

Kelly Anne Henderson

Mike Lohmeier

Dmitry Stupakov

John Kalmi

Jason Grosz

Jeffrey J Alexander

Chase Acton

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

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

Growing community of inventors

San Francisco, CA, United States of America

Chris Hogg