Smart Inhaler Internet of Things (IoT)
Lack of focus on prevention of an attack in the current model for asthma care.
Reversal of apathy among patients (30%-70%), who did not adhere to their prescribed medication.
Simplification of the clunky, difficult-to-analyse paper-based reports.
Absence of a system to set timelines and track Capex creation.
An advanced IoT-based smart inhaler that not only monitors the condition of the patient but also educates them on the measures and actions to prevent their next attack.
Connected App: Enables asthma patients and doctors to monitor and fight respiratory illnesses through usage tracking via the app.
Tracking: Accurate monitoring further helps in identifying the triggers for asthma attacks and prevention of future attacks.
Records and Reminders: Accurate records of when each dose is taken are used to communicate status to doctors, and help track illnesses. Reminders are also sent to patients to use their inhaler whenever necessary.
Patient Education: Patient is educated about triggers, like pollen or temperature, aggravating factors etc. and is better equipped to avoid asthma attacks and preventable hospitalizations.
Neebal assembled a team of industry experts, business analysts and content writers to survey and understand the pain areas to be addressed. Neebal team researched for 90 days on the existing hardware and built a prototype to capture the desired parameters in a single test cycle.
Designing the prototype
The team decided to implement the latest technology of Internet of things (IOT). The technology team wired an inhaler with a Bluetooth transmitter to communicate with an asthma patient’s smartphone. The idea was to get the sensor to activate when the inhaler is pressed.
Enhancing the prototype
The team aggregated and embedded multiple sources of public and private information into the App to supplement expert comments.
Launching the application
Post the client’s sign off, the application was launched and tested for effectiveness and efficiency with a pilot run in a supervised group of 1000 patients.