Google unveils AI-powered Care Studio Conditions to make sense of patient records

Google Health’s new Care Studio feature, Conditions [Screenshot courtesy of Google]Google Health previewed a new Care Studio feature called Conditions to make electronic health records more accessible and useful for clinicians treating patients.

Powered by artificial intelligence, Conditions can interpret and organize clinical notes stored across different systems for different purposes by different health care professionals.

‘When it comes to writing notes, clinicians use different abbreviations or acronyms depending on their personal preference, what health system they’re a part of, their region and other factors.” Paul Muret VP and GM of Google Health’s Care Studio, wrote yesterday in a blog post. “All of this has made it difficult to synthesize clinical data — until now.”

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Google unveils AI-powered Care Studio Conditions to make sense of patient records

Google Health’s new Care Studio feature, Conditions [Screenshot courtesy of Google]

Google Health previewed a new Care Studio feature called Conditions to make electronic health records more accessible and useful for clinicians treating patients.

Powered by artificial intelligence, Conditions can interpret and organize clinical notes stored across different systems for different purposes by different health care professionals.

‘When it comes to writing notes, clinicians use different abbreviations or acronyms depending on their personal preference, what health system they’re a part of, their region and other factors.” Paul Muret VP and GM of Google Health’s Care Studio, wrote yesterday in a blog post. “All of this has made it difficult to synthesize clinical data — until now.”

Conditions uses natural language processing to understand the notes, rank conditions b…

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The role of natural language processing in advancing disease research 

Image courtesy of Markus Spiske/Pexels

In any area of disease research, a deep understanding of recent and future trends surrounding a particular condition is crucial to the drug discovery process. But with the volume of scientific literature increasing all the time, it is difficult to manually sift through all the existing information and correlate data in such a way to produce meaningful direction. This predicament can lead to the misallocation of resources on research in areas that are less likely to yield promising treatments.

By analyzing all literature related to a specific condition or disease, researchers can better identify which areas will likely lead to a breakthrough. Natural language processing (NLP) uses a combination of linguistics, artificial intelligence, and computer science to understand text in the same way as people. Researchers can use NLP in trend analysis to determine the rate at whic…

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What’s driving the natural language processing revolution in pharma and life sciences

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Pharmaceutical and life sciences companies are faced with a constant stream of new data flowing into often siloed information systems. About 80% of that information exists in unstructured text that is difficult to extract and use, despite its paramount importance in driving clinical and commercial outcomes.

As a result, these organizations find themselves increasingly overwhelmed with volumes of inaccessible data. At the same time, researchers and data scientists lack effective search tools to find the right information in this “big data” tsunami, causing them to miss opportunities to enhance patient safety, improve clinical trial design, identify previously undetected biomarkers and better understand the voice of the customer.

To overcome the limitations of time-consuming, manual searches through mountains of data, pharma and life sciences companies are looking to artificial…

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