Google Launches Interoperability Cloud Healthcare API

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Google has recently announced the availability of the Cloud Healthcare API. Google noted that there is a need for a software tool that addresses the issue of interoperability of healthcare data, especially as more healthcare organizations move their data to the cloud and utilize machine learning in the analysis of patient data. The Cloud Healthcare API aims to resolve the interoperability issue by providing the necessary infrastructure to ingest and process different types of healthcare data, including Digital Imaging and Communications in Medicine (DICOM), Health Level 7 (HL7), and Fast Healthcare Interoperability Resources (FHIR) standards.  The search giant also stated in its blog post that the Cloud Healthcare API is currently in early access release, although the tech firm plans to deploy the software tool to more customers starting next year.

Within the last few years, the Mountain View-based search giant has been involved in the development of cloud-based software tools that researchers and healthcare professionals could use. For example, the search giant developed the Cloud Genomics API, which permits researchers to upload, process, and search for genomic data in the cloud. Google is also offering an API management platform dubbed as the Apigee, which permits organizations to deploy the FHIR APIs on top of the healthcare platforms that the firms already use. Healthcare organizations have also utilized the Apigee platform in order to ensure that the patient data can be accessed quickly and securely, as well as to improve the functionality of their existing electronic health record systems. Moreover, Google also announced that two more Google Cloud Platform services, the Google App Engine and Cloud Machine Learning Engine, are now compliant with the HIPAA. The tech firm further mentioned that it is working to expand HIPAA compliance to more GCP services in the near future.

In the past few years, Google has been collaborating with researchers from the University of California San Francisco, University of Chicago Medicine, and Stanford Medicine in order to develop medical tools and techniques through the use of machine learning and de-identified medical information. Last year, the search giant also mentioned that machine learning has proved useful in tracing the spread of cancer cells from the breast to the lymph nodes and in screening diabetic retinopathy, which is the loss of eyesight due to diabetes.