Google Cloud Healthcare API – What Google-Enabled Machine Learning Could Mean for Patient Care

Google Cloud Healthcare API

At the recent HIMSS18 conference, Google unveiled its new cloud healthcare API. This tool will help healthcare providers and healthtech companies collect and manage various types of medical data – including EHR data, administrative reporting data, and imaging data – via the cloud and is purportedly designed to address the significant interoperability challenges in healthcare data. 

 
  HIMSS Global Conference & Exhibition is the industry's largest health information and technology educational program and exhibition center.

HIMSS Global Conference & Exhibition is the industry's largest health information and technology educational program and exhibition center.

 

Google Cloud, not unlike other public cloud providers, is finding ways to differentiate in the competitive cloud market. Google is betting that, if healthcare data can be aggregated and easily accessible, companies will be better able to leverage this data to launch new analytics and machine learning projects. The upside is that this capability will open the door to identifying new trends, patterns, and insights that could significantly improve patient care and patient outcomes.

Interoperability is expected to pose a continued challenge throughout 2018 (and beyond) - Google’s healthcare API may help enable a more seamless healthcare data exchange.

“Our goal with the Cloud Healthcare API is to help transform the healthcare industry through the use of cloud technologies and machine learning. Healthcare is increasingly moving to the cloud, and the adoption of machine learning will allow the industry to unlock insights that can lead to significant clinical improvements for patients.”
– Google

What kinds of improvements to patient care is Google referring to? Here are just a few examples of machine learning projects impacting patient care:

  1. Google already has developed a machine learning algorithm to identify cancerous tumors on mammograms.

  2. Stanford researchers have trained a deep learning algorithm to identify skin cancer.

  3. A deep machine-learning algorithm has been proven to help diagnose diabetic retinopathy in retinal images.

Machine Learning is helping diagnose patients.

DVmobile has deep expertise in the healthcare market – developing healthcare solutions for the cloud and partnering with industry leaders to keep pace with healthcare industry standards (including those around data). For more information about how we can help your organization contact us through the button below, or read more on our Healthcare Industry page.

  Healthcare Industry page.

Healthcare Industry page.

 

 
Kris Soden blog author

Kris Soden

SAFe SPC4 certified consultant, proud dad, and former CU Buffs lineman.

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