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Google Taking on Healthcare and Clinical Artificial Intelligence

by Madhu Reddiboina

Google recently published a paper in NPJ Digital Medicine, wherein they worked with the electronic medical records (EMR) data from two large hospital systems to predict in-hospital mortality, length of stay, readmissions and diagnosis at discharge using deep learning and fast healthcare interoperability resources (FHIR).

Traditional machine learning and statistical models are built using a limited number of known variables from patient records ignoring most of the data that exists on every admitted patient. Technically speaking, this work is very different wherein they utilized all the patient data including encounters, labs, medications, notes, procedures, observations, etc. to build deep learning models that are superior than any other work published thus far.


This paper entitled, Scalable and Accurate Deep Learning with Electronic Health Records published in NPJ Digital Medicine, is a masterpiece with amazing results in predicting patient outcomes using personalized predictive models.

In this study, the authors assumed the position that by representing a patient’s raw EHR data according to the FHIR format, deep learning can accurately predict outcomes and medical events regardless of the location of the patient from one setting to the next. According to the authors:

“We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes.”

Events Predicted and Accuracy Rates Include the Following:

  • In-hospital mortality - 93 to 94 percent accuracy
  • 30-day unplanned readmission - 75 to 76 percent accuracy
  • Prolonged length of stay - 85 to 86 percent accuracy
  • All of a patient’s discharge diagnoses - 90 percent accuracy

Impact of Deep Learning on Healthcare

Deep learning techniques used to accurately predict medical events hold great potential for the care and monitoring of critical care patients while also optimizing hospital resources. If put to work in a clinical setting, we can expect the machine and deep learning algorithms to improve the predictability of critical events and the care delivery workflow.


Possibilities for deep learning to how doctors are able to treat patients and the quality of care are endless. Stay posted to our blog at RediMinds as we keep you updated on the latest news about the use of artificial intelligence in healthcare.

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