Healthcare, has experienced a vast amount of change in the last five years. In this age of all things mobile, virtual visits and real-time patient scheduling are more popular since patient expectations are greater. With the rise of the internet of things (IoT) — estimated to reach 161 million devices in 2020 — wearables and connected medical devices have also grown in use and are providing care outside of the traditional four walls of a clinic.

As healthcare organizations use more connected devices, electronic health records (EHRs) and virtual visits with patients, the influx of data is pouring in. Today, for many patients, that data continues to sit siloed in large data banks and does not improve their care until it’s been reviewed and filed away for an undetermined time in the future.

More advanced technology, however, is turning wearable devices — merely consumer gadgets three to five years ago — into useful tools that can provide preventive care for patients, such as necklaces or bracelets that measure vital signs and can help diagnose conditions such as hypertension or asthma.

But what if patients were to call into a triage unit with nurses who have access to real-time data about their medical histories, as well as analytics that could guide those nurses to recommendations based on the data and real-time information flowing in throughout the call (or via text message)? That’s the future of patient care in an era marked by digital transformation.

One of the major issues in the healthcare industry today is the amount of medical fraud that occurs. In 2017, for example, there were $1.3 billion in false billings, according to the Office of Inspector General of the U.S. Department of Health & Human Services. Analytics can play a significant role in detecting medical fraud as well as aiding in the early diagnosis of medical conditions. Our streaming dataops product for healthcare help deliver real time diagnostic capabilities as well as prevent fraud through robust anomaly detection. 

Healthcare DataOps

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In-depth analysis that ingests medical data and identifies parallels,including correlations from wearable devices like Apple watches

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Location analytics for important uses, such as network planning, hot spotting for re-admittance rates and pharmaceutical sales

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