Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 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.

Abstract: 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.

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: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: A system and method for accurately and reliably determining and recording the time, date and location where a medication is used, and a system and method for transmitting, collecting, and using that data to improve clinical care, disease management, and public health surveillance. The device allows information concerning drug usage, including the time, date and location of use, to be transmitted to a remote network computer system so that the data can be evaluated to determine current impairment and future risk, and to identify changes in the frequency, timing, or location of medication usage indicative of change in disease control or management, and to examine spatial, temporal or demographic patterns of medication use or absence of use among individuals and groups. In addition, the device may further be configured to transmit signals indicative of its status, condition or other results to the remote network computer system.