This is Part 3 of our series on creating an FHIR API using Google Cloudโs offering. In the last installment, we began implementing an FHIR using GCP. We covered creating both the BigQuery resources and your FHIR repository resources. if you missed Part 1 and Part 2, be sure you go back to read those – theyโre critical to understanding!
This time, weโre continuing the implementation. Iโll explain the authentication methods, and weโll also tackle populating data in our FHIR repository.
Welcome back to our series, Creating an FHIR API. This is Part 2 in our 4-part series on standing up an FHIR using GCP. In Part 1, we talked through two of the offerings out there, Google and Azure, and based on the pros and cons, I decided to use GCP FHIR Cloud Healthcare API.
In this part, we’ll start in on our implementation. A forewarning: we won’t be able to finish it during this installment, so stay tuned for parts 3 and 4! Let’s dive in.
Data interoperability is one of the hardest problems in Healthcare IT. The most popular approach is to exchange HL7v2 messages between systems. These pipe-delimited messages are difficult to read by a human and often need additional customizations between implementations.
The next major paradigm shift is towards FHIR (Fast Healthcare Interoperability Resources), a JSON-based standard that is evolving ahead of the needs of the industry. Cloud vendors like Microsoft, Amazon, and Google are trying to lay their claim to be the one-stop shop for healthcare on the cloud.
This blog is part of a 4 part series diving into an actual use case I recently encountered while working with a client. I had to stand up an FHIR repository/API for 2+ million patients that could be used by hundreds of users every day, as well as countless background processes.
Developing custom applications is one of the hardest professional endeavors, and making them secure is even harder. Malicious actors are constantly changing tactics and strategies, which, unfortunately, makes it impossible to completely eliminate any security threat.
There needs to be a balance between delivering features quickly to meet business objectives and mitigating security risks. Thankfully, these two goals are not mutually exclusive. This blog post dives into the top mistakes that can be made while developing custom applications.
These recommendations are different from what would commonly be seen in an OWASP list, and they should be used in addition to whatever security practices and procedures are already in place by an organizationโs infosec department. These recommendations are also written from an application architectโs (rather than an enterprise infrastructure) perspective, so most of them arenโt covered by existing security checklists.
When it comes to the science of genomics, Amazonโs AWS is by far the platform of choice for most organizations. But, just like in the musical Hamilton, Microsoft Azure is โyoung, scrappy, and hungry.โ
It is driven to provide the tools and managed services that are needed to run genomics at scale, with HPC (high-performance computing) and storage being among the hardest facets of the field to get right.
This blog post will first briefly explain genomics. Then, weโll dive into what Azure has to offer in this field.