{"id":1196,"date":"2019-06-25T00:00:00","date_gmt":"2019-06-25T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/using-natural-language-processing-to-manage-healthcare-records"},"modified":"2025-06-24T23:05:09","modified_gmt":"2025-06-25T06:05:09","slug":"using-natural-language-processing-to-manage-healthcare-records","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/using-natural-language-processing-to-manage-healthcare-records\/","title":{"rendered":"Using natural language processing to manage healthcare records"},"content":{"rendered":"\n

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\u2014and 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.<\/p>\n\n\n\n

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.<\/em><\/p>\n\n\n\n

Billions of records<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

It’s not for lack of trying. In the last decade, an effort was made to accumulate and upload data into electronic health records (EHR) systems. Meaningful Use<\/a> is a government-led incentive program that aims to accelerate the movement from hard-copy filing systems to electronic health records. Still, the problem is related to the volume and the lack of time and resources to assimilate masses of data.<\/p>\n\n\n\n

Note:<\/strong> the Meaningful Use program has a number of goals. An important one is, \u201cEnsure adequate privacy and security protection for personal health information.\u201d Data security is a prime value for Azure services. Data services such as Azure SQL Database encrypt data at rest and in-transit.<\/p>\n\n\n\n

Moving the needle on healthcare<\/h2>\n\n\n\n

As costly and extensive as this effort was, many believe that we have yet to see evidence of any significant impact from the digitization of healthcare data to the quality or cost of care. One way to radically improve this is using AI for natural language processing (NLP)\u2014specifically to automate reading of the documents. That enables subsequent analytics, yielding the most relevant actionable information in near real-time from mountains of documents to the medical professional. It empowers them to deliver better quality care, more efficiently, at lower cost.<\/p>\n\n\n\n

In action<\/h2>\n\n\n\n

A Microsoft partner, SyTrue<\/a> is leading the way. In the words of their Founder and CEO, Kyle Silvestro, \u201cAt SyTrue, the next big challenge is accessing this vast pool of accumulated patient data in a serviceable way. We\u2019ve created a platform that transforms healthcare documentation into actionable information. The focus is on three main features: speed, context, and adaptability. Our technology consumes thousand-paged medical records in sub-seconds. The innovation is built on informational models that can ingest data from multiple types of clinical and financial health care organizations. This allows diverse healthcare stakeholders to use the system. The main objective for the technology is to present key clinical and financial insights to healthcare stakeholders in order to reduce waste and improve clinical outcomes.\u201d<\/p>\n\n\n\n

Informed by natural language processing and machine learning<\/h2>\n\n\n\n

SyTrue relies on NLP and machine learning (ML) as the underlying technology. Using their own proprietary methods, they perform \u201ccontext-driven information extraction.\u201d In other words, they connect the dots. The graphic below shows their processes.<\/p>\n\n\n\n

\"Diagram<\/figure>\n\n\n\n

Improving healthcare<\/h2>\n\n\n\n

SyTrue\u2019s offers the NLP OS (Operating System)<\/a> for healthcare. It aids in several ways.<\/p>\n\n\n\n