Abstract: A system and method to determine whether a patient should receive enhanced treatment of a respiratory ailment such as the prescription of biologics. Use data of a respiration medicament device to deliver controller or rescue respiration medicament to the patient is collected. The use data is transmitted to a storage device. The use data is stored in the storage device. Based on the use data, the system determines whether the patient is over a first threshold level of adherence in use of the respiration medicament device. Based on the use data, the system determines whether the patient has a rescue respiration medicament use over a second threshold level. A notification of recommendation of the enhanced treatment is provided if the patient is over the first and the second threshold.

Abstract: An asthma analytics system 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.

Abstract: 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.

Abstract: 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. Notification 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.