In part 1 of our blog series, we talked about driving ROI in healthcare with data analytics modernization.
In this blog, we will discuss accelerating time and money to value with the Perficient Healthy Lakehouse.
Through past implementations, Perficient has learned a lot about what adds time, effort, and cost to healthcare data & analytics projects. We’ve also learned a bit about what can be done to reduce the lift involved.
Over the past few years, we’ve pulled together a set of architectural design patterns, and frameworks, which can help reduce the time and cost it takes to get to value when building a healthcare data lake, data warehouse, and analytics solution. We call this framework the Healthy Lakehouse. The Healthy Lakehouse is based on field-proven AWS services and solution design patterns which Perficient has developed while implementing solutions for clients in the Provider, Payer, PBM, Pharma, and Clinical Research domains.
The Healthy Lakehouse also borrows data and analytics modernization patterns from other industries, such as Financial Services, Automotive, and Manufacturing.
Please note that we expect the frameworks used within the Healthy Lakehouse to be adapted to your needs, since healthcare source systems, standards, and at times semantic meaning, can vary broadly from client to client.
Creating this type of framework is something any organization can potentially do on its own, but starting from scratch takes time and money, and it’s increasingly hard for most of our clients to keep up with the pace of innovation by themselves.
Perficient brings the Healthy Lakehouse framework to new-client engagements, to accelerate and drive momentum in the early phases of the journey. As most that have been through this process would attest, getting started is often the hardest and slowest part of the journey. And getting it wrong can be disastrous.
Let’s take a moment to walk through the Perficient Healthy Lakehouse at a high level:
It starts with collecting and acquiring data from a variety of sources. There are many different types and flavors of data sources that have different interfaces, formats, standards, and/or semantic meanings. The Healthy Lakehouse Data Acquisition & Ingest Framework provides proven design patterns for the types of acquisition and ingests shown in the chart. Having these patterns at the start of the initiative saves time and money, and also helps drive standardization as data ingest pipelines are designed and built.
Looking to the center of the chart, under the Lakehouse roof if you will, the Lakehouse includes a Raw layer where data is landed, a curated layer that is in fact a processing layer that standardizes and conforms the data, and a publish layer to hold and present data for access… think data marts and views in the publish layer.
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- The data lake itself uses Amazon S3 Buckets to store data,
- Curation is accomplished via Amazon Glue-based pipelines which include metadata capture within the Glue Data Catalog, and selective data movement into the Publish layer,
- The Publish layer is an Amazon Redshift data store that holds and presents data for consistent use in analytics.
- There’s also an Operational Framework that acts as a guide to ensure operational best practices such as audit-balance-and control are included from the start.
- The Lakehouse also includes design patterns to enable data tenancy tags to be applied and managed within the Lake and Publish layers.
The data within the lake can be exposed for use in different ways and for different purposes
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- Data is typically used to drive BI style reporting, visualizations, and analytical trending, using Amazon Quicksight as the analytics presentation tool.
- Data can also be fed to ML models and AI generally, using Amazon Sagemaker, as well as serving data to other applications via APIs
- We refer to this area collectively as the Data Intelligence and Data Services Layer
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The business insight which can be derived from the Lakehouse is really a function of the data which is provisioned within, and the analytics developed based on that data. The Business Insights listed on the right side of the chart provide an overview of just some of the key domains of insight PRFT has developed with past clients, although this should not be seen as a finite or limited list of what is possible.
As mentioned throughout, the Healthy Lakehouse framework leverages the AWS Cloud Platform and Amazon Data and Analytics Services, as shown on the chart, including:
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- AWS Glue, Kinesis, S3 Buckets, DynamoDB, Redshift, Athena, Elastic Map Reduce (EMR), Amazon Quicksight, and Sagemaker
Success Story: Modernizing Data and Analytics in Healthcare
A global health services company needed a more comprehensive view of finances (e.g., distribution revenue, costs, and rebates) associated with the products it sells and delivers. It also needed to ensure the accuracy of financial transactions and enable rebate reporting.
As this client’s trusted partner for AWS-based data and analytics modernization, we developed a solution to meet these needs with a platform through DevSecOps work with AWS account structures. This solution provides better data insights for pharmaceutical distribution, invoicing, and rebates to the client’s internal and external partners.
Perficient + AWS
Perficient understands the complexities of the healthcare industry and the unique challenges healthcare organizations face. Our healthcare practice delivers strategic business and technology consulting insights that help our clients transform with today’s digital consumer experience demands. This strategic guidance is then transformed into pragmatic technology solutions that improve clinical, financial, and operational efficiency.
As an AWS partner, we have extensive experience with Big Data, data warehousing, business intelligence, and analytics, and work with our clients to transform data into timely and actionable insights using AWS services such as AWS Glue, Amazon Redshift, Amazon Quicksight, and Athena.
Perficient’s healthcare practice is dedicated to helping healthcare organizations leverage data and analytics to improve care quality, access, and delivery, and to better manage the costs associated with providing that care.
