Using natural language processing to manage healthcare records

Using natural language processing to manage healthcare records

  • June 28, 2019
Table of Contents

Using natural language processing to manage healthcare records

The next time you see your physician, consider the times you fill in a paper form. It may seem trivial, but the information could be crucial to making a better diagnosis. Now consider the other forms of healthcare data that permeate your life—and that of your doctor, nurses, and the clinicians working to keep patients thriving.

Forms and diagnostic reports are just two examples. The volume of such information is staggering, yet fully utilizing this data is key to reducing healthcare costs, improving patient outcomes, and other healthcare priorities. Now, imagine if artificial intelligence (AI) can be used to help the situation.

The Azure platform offers a wealth of services for partners to enhance, extend, and build industry solutions. Here we describe how SyTrue, a Microsoft partner focusing on healthcare uses Azure to empower healthcare organizations to improve efficiency, reduce costs, and improve patient outcomes. Valuable insights remain locked in unstructured medical records such as scanned documents in PDF format that, while human-readable, present a major obstacle to the automation and analytics required.

Over four billion medical notes are created every year. The clinical and financial insights embodied within these records are needed by an average of 20+ roles and processes downstream of the record generation. Currently, healthcare providers and payors require an army of professionals to read, understand, and extract healthcare data from the flood of clinical documents generated every day.

But success has been elusive.

Source: microsoft.com

Tags :
Share :
comments powered by Disqus

Related Posts

How to run evolution strategies on Google Kubernetes Engine

How to run evolution strategies on Google Kubernetes Engine

Reinforcement learning (RL) has become popular in the machine learning community as more and more people have seen its amazing performance in games, chess and robotics. In previous blog posts we’ve shown you how to run RL algorithms on AI Platform utilizing both Google’s powerful computing infrastructure and intelligently managed training service such as Bayesian hyperparameter optimization. In this blog, we introduce Evolution Strategies (ES) and show how to run ES algorithms on Google Kubernetes Engine (GKE).

Read More
DeepMind and Google: the battle to control artificial intelligence

DeepMind and Google: the battle to control artificial intelligence

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: “So today I’m going to be talking about different approaches to building…” He stalled, as though just realising that he was stating his momentous ambition out loud.

Read More