At Cloudbakers, we like to say we’re 50% tech and 50% people. We pride ourselves on our ability to develop partnerships with our clients by understanding their goals and aligning ourselves with their motivations. When it comes to our healthcare and life sciences (HCLS) clients, this is something we’re especially driven to do. Time and time again, our HCLS clients have emphasized to us that their most important objectives are protecting patient data and improving patient outcomes; their priority is to better and often save the lives of the people in their care and treat them with respect when they are at their most vulnerable. Who wouldn’t want to get behind these efforts?
It’s why we’ve been so excited to see Google’s continuous focus on the HCLS space. Not only are the fundamental cloud computing services on the Google Cloud Platform fully HIPAA compliant, but Google has also incorporated specialized tools that make it easier to develop customized solutions for research facilities as well as hospitals and other care providers.
Bridging the gap between care systems and cloud applications
One such tool is the Google Healthcare API – a combined storage and data interface that offers native support and interoperability for the HL7 v2, FHIR, and DICOM standards. Cloudbakers had the privilege of using the alpha release of the Healthcare API as part of an effort to create a clinical data warehouse, and I can personally attest that it makes working with these specialized (and complicated) data formats simple and easy. This means you as a HCLS provider can focus on analyzing EHR and imaging data and developing powerful, healthcare-focused AI models, rather than worrying about ingesting and storing data in a compliant and secure manner.
Tools built for speed, scale, and security
Another component in the Google healthcare toolset is the Genomics API, which provides petabyte scale genomic data processing and analysis for bioinformatics. The human genome contains over 3 billion base pairs - the combinations of A, C, T, and G that act as the building blocks of DNA. If each pair was encoded as a 2-bit character, that would mean that a DNA sequence for a single human would be approximately 715 megabytes, or about ⅔ of a gigabyte. Realistically, the amount of disk space taken by such data, which might involve multiple passes over specific genome segments as well as metadata about the reads, can be hundreds of times this value. Processing that data on a large scale, with hundreds or even thousands of patients, means that you need your computing power to scale accordingly. Enter the Genomics API: by incorporating a GA4GH-compatible interface with the scalability of GCP infrastructure and analytical tools, you can handle and process entire genetic sequences in seconds.
By utilizing these specialized tools along with the data analytics options on Google Cloud, we can facilitate automated integrations of different EHR systems, set up clinical data warehouses that can be used to generate insights into hospital operations, forecast the onset of sepsis for surgical patients, and prevent loss of protected patient data. We’re seeing more and more clinical and research applications of data analytics emerge as the data to support analytics is increasingly accessible – some great things have already come out of it and at Cloudbakers, we can’t wait to see what’s next.
In healthcare, and thinking about going Google? Contact firstname.lastname@example.org to set up a conversation with our experts and learn more about your options.Originally published on August 22, 2019