Thoughts on Healthcare data and technology integrations

The following case study pertains to a massive Healthcare mission to modernize data and technology operations. A previous article discussed public cloud adoption for the same cause and the journey therein. This article discusses future improvements in terms of overall ambition and growth.

First, it must be said that the mission is just over a decade old and has made significant strides in all areas of computing including migrations and modernizations towards the public cloud. For instance, the detection and redressal of incorrect or fraudulent payment activities alone are in the range of hundreds of millions of dollars each year. Similarly, the use of technology stacks is as exhaustive as it could possibly be, with mention about the use of all forms of latest languages, libraries, packages, and hosting solutions including Golang and Python with significant investments in GPU based Machine learning models, old and new data science and analysis software including statistical analysis and use of most recent versions of each for reducing technical debt and overhead. There are internal portals to request all kinds of resources for computing, storage and network as well as cloud infrastructure and Platform-as-a-service stacks demonstrating the best practices in the industry.

Second, there are multiple levels of development, architecture, and cross cutting initiatives that the venture has already tried and tested, which although began on-premises has also nimbly moved to the cloud. Leaders and pioneers have forged significant trends and patterns with little or no concerns about organizational mindsets about remaining on-premises or adhering to old and outdated practice.

With this background, some of the improvements that can be called out are in the areas of data and machine learning pipelines because the infrastructure is a significant contributor to the spirit and practice of exploring new opportunities for business improvements. The next sections focus on these independently:

Data pipelines:

Some of the lessons learned from centralizing data and moving on-premises data to the cloud are that access to the data and the manner of use are just as significant as making the data available.  Pass-through authentication allows a variety of clients to access data so that the callers are responsible individually for their actions.

Another significant area of challenge is that network utilization becomes increasingly a bottleneck as data transfers must be spread across time and calendar to justify the bandwidth that they consume.

The third area of challenge involves setting up a variety of central data stores for structured, unstructured and event or streaming data architectures.