Real-time data from apps and wearables helps create a holistic perspective of a patient's health. This longitudinal data provides more context to their social and behavioural patterns outside of the clinical environment. This data from devices can also be used for remotely monitoring patients allowing providers to track vitals and prevent adverse events
Health data from consumer health devices and apps can drive your engagement programs with better data insights and help create better health outcomes. You can identify members that are high risk and those with early signs of chronic conditions allowing you mitigate risk across your insured population.
You can remotely collect data from participants in clinical trials meaning fewer in-person visits and less manual tracking. Access to real-time data provides researchers the ability to make amendments to protocols and better decisions based on how participants are reacting to a drug. Early signals can also be detected to help identify drug efficacy for specific subsets of the study population.
We support integration with several popular health devices and apps such as Fitbit, Biostrap, Withings, Apple Health, Oura Ring, Google Fit, Garmin, iHealth, Omron, Polar, Strava and others. You can use our cloud based API or we can help you implement our prebuilt integration libraries into your environment / app directly.
healthR now supports Optical Character Recognition (OCR) and Medical Concept detection via our new Vision API. You can now upload images containing text (prescriptions, medical notes, readings from non connected devices) and the API will return the text from the image along with all associated medical concepts (classification of text)
As well as being able to connect to a wide range of wearable devices and apps, healthR is also a secure health data storage platform. This allows app developers and device manufacturers to use healthR as their core data platform for devices and apps. We can also help develop predictive health insight algorithms based on longitudinal data using Machine Learning.