Previously we outlined what tomorrow’s healthcare analytics platforms will need to resolve the challenges that healthcare organisations are facing.
The second is to consider the need for scale to accommodate traditional and new and emerging data types.
As organisations look beyond core clinical and claims data integration to data sourced from devices, large genome data sets, and information from supporting systems that provide insights into behavioural, social, and environmental factors, healthcare analytics platforms will be presented with ever-increasing scalability challenges.
Other industries have already been forced to tackle these challenges, and technologies frequently referred to as “distributed databases” and “computing engines” have emerged to meet the demand. Databases built for speed such as Cassandra and Elastic Search, and computing engines such as Apache Spark, allow storage capacity and processing power to be spread across multiple servers, providing the ability to incrementally increase capacity by augmenting the size of the server clusters that support the deployment. There are numerous examples of these technologies being deployed across thousands of clusters, storing petabytes of data, and processing millions of transactions per second. But equally, deployments can begin on small, commodity hardware clusters, which eliminates the need to anticipate future processing needs and make a large, up-front hardware investment.
Complementing these technologies are Infrastructure as a Service (IaaS) providers such as Amazon Web Services (AWS), who simplify the process of provisioning and deploying new hardware, literally to the point of the click of a button.
For software vendors, leveraging these technologies requires a significant R&D investment and specialised engineering teams for integration and deployment. Waiting until existing technology choices reach a breaking point will leave many without sufficient time to make the transition. Recognising this, Orion Health has been investing in developing its next generation platform for the past two years. It has large-scale installations of its data engine, streaming & computation engine and patient registries (backed by Cassandra, Elastic Search, and Apache Spark, respectively) in production today in large payer and provider organisations. And we are using AWS to simplify our ability to deploy and scale our customer solutions on demand.
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