We know the value of each data point and aspire for as many quality data points – therefore we invest in a simple to implement Application Protocol Interface (API), that will encourage integration and win-win with our partners  – the open architecture enabling integrating two types of connected devices:

– Connected Medical Intake Devices: Vaporizers, sprayers, inhalers and more.
– Physio-measuring wearables: Pulse watches, sleep monitors, and more.

To better understand how Asaya’s big data analytics and our partners IoT devices work together, we can outline the overall flow:

  1. Our partner’s devices (i.e sleep monitoring device) are installed and use sensors for collecting and transmitting data (i.e sleep levels, patterns and timings)
  2. Large amount of data is then collected, often in real time. Both structured data from prepared data sources (patient profiles, plant chemovars, etc.) and unstructured data from third party sources (heart-monitoring devices, skin-conductivity devices, precise-dosage vaporizers and more)
  3. The data is graded for fidelity and accuracy – and is then added to subjective patient feedback towards building a multi-source treatment feedback legend
  4. Asaya’s engine processes the data, finds its correlated categories, comprables and interconnections, and creates analysis, insights, reports, charts, and other outputs. Such outputs are mostly displayed in ASAYA’s HCP dashboard and in some cases, they make their way to the patients mobile device. The analyzed data feeds back to the Asaya engine to increase its exactitude.

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