According to a cross-industry study by MIT and Databricks, only 13% of organizations deliver on their data strategy. Now, as we all know, healthcare organizations, especially providers, have been through a lot over the last few years, and this doesn’t make delivering on their data strategy any easier.
This is why we ask ourselves daily: ‘how can we help healthcare organizations modernize their data & analytics capabilities?’ We firmly believe that success is not due just to the systems or applications you buy and implement, or even which cloud vendor you use… it’s about growing the right capabilities across your organization, including IT, within Business, Operations, and Clinical functions and staff.
Most healthcare organizations we work with have taken some steps towards modernizing their data and analytics capabilities. However, they’ve not been able to truly take advantage of the full set of benefits and innovation that this modernization vision offers. We’ve seen some movement to the cloud, mainly lifting and shifting data. However, much of this, really, just replicates many of the same legacy challenges that existed in the on-prem world and don’t take full advantage of the momentum that could be leveraged from these initiatives and investment.
Let’s take a minute to dig into what we really mean by data and analytics modernization and what some of the benefits of embracing this modernization really are:
Data Modernization in Healthcare
For starters, there’s a large opportunity for most organizations to reduce the cost and complexity of their data management environment and programs. The low-hanging fruit to get real ROI is to:
- Retire redundant on-prem systems,
- Leverage the OpEx budget instead of the CapEx budget, where this makes sense
- Push some of your data & analytics administration and support activities to your cloud provider.
- But in addition to direct ROI, this evolution allows you to focus resources and staff on things that truly add value to your core mission instead of using these resources to manage your IT infrastructure.
Next, as we all know, healthcare data is under constant attack. The rate at which ransom attacks and data breaches are occurring is simply out of control, and the number of attacks seems to increase every month. No wonder we all get 4 or 5 emails a week advertising security offerings or services.
Healthcare organizations need to take steps to increase data security. Historically, healthcare leaders have been understandably hesitant to give up control of patient and member data. Still, the reality is the investment and skill of, say, AWS’, the cloud-data security team is multiple orders of magnitude greater than that of any single healthcare organization, so really, we should view cloud migration as one step in helping us grow our data security capabilities.
Many organizations have done a lift and shift of data. Still, they have not fully embraced modernization, leveraging development techniques such as DevOps infrastructure as code, continuous integration and deployment, and containerization.
Organizations need to develop the capabilities to provision and instantiate new environments quickly and reliably – this is a huge benefit for data & analytics project teams, and reduces the effort, timeline, and cost to deploy environments compared to traditional manual deployments. And this eases the burden of spinning-up sandboxes for discovery-based analysis
We should also employ modern DataOps techniques that automate the execution, orchestration, and monitoring of operational workloads. This helps reduce user downtime and provides more predictable data processing operations with less direct human intervention.
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Over the past few years, the perspective on which and how much of our data we should curate and publish has shifted, limiting the data we curate and transform to only that data needed to satisfy known, priority use cases, as opposed to the traditional Data Warehousing practice of curating and integrating everything you touch.
There’s a pressing need to ingest and leverage an ever-increasing variety and volume of data. This data comes from many places, including:
- Streamed data from devices and patient monitoring… think Internet-of-Medical-Things data, data captured within the digital engagement experience, and alternative digital health modalities like telehealth, not to mention unstructured data from clinical notes, imaging, audio, as well as data generated through social media. This is all in addition to the pile of data that comes from the traditional EMR, labs, and other HC systems.
- Capturing and managing this data involves massive amounts of data ingest, storage, and processing capacity.
- Although much of this could be handled, in theory, with current on-premises technologies, cloud platforms like AWS have services already built into the platform, such as AWS Glue, Athena, and Redshift to help solve many of these challenges, and they can scale when and as needed.
Switching gears, I talked a lot about data here, but what about analytics modernization?
Many teams get caught up in, or perhaps overly consumed by, the need to deal with all this data. But let’s not lose sight of the real value here: to drive analytics value and insight to business users and stakeholders.
To accomplish this, we need to enable seamless access to the right information. This includes BI scorecards and visualizations of course, but also self-directed analysis that is or should be, table-stakes for any modern analytics program. This means enabling teams to quickly and easily create accessible discovery sandboxes for ad-hoc exploration by power users and data scientists without sacrificing the necessary governance and security.
Success with self-service analytics requires using Data Catalogs and Business Glossaries to allow users to locate, access, and trust the data they need and provide mechanisms to interpret the semantic meaning of similarly named data, which is so important to use data appropriately and responsibly.
If we look at analytics and insight from a forward-looking perspective, we need to prepare for the expanded use of AI and Robotic Process Automation. Doing so requires providing an increased level of trust in your data and the ability to manage the bias inherent within that data.
We need to grow the capabilities to create and use predictive models and ML algorithms, allowing us to move beyond the typical ‘reactive response’ and enable ‘predictive responses’. Sustained success with AI and ML requires capabilities to manage these rapidly changing and growing predictive models and algorithms through employing AI-Ops tools and techniques.
When we get this right, we can advance beyond simply providing insight to better understand healthcare and achieve the capabilities to automate functions that reduce the load on our completely overburdened HC workers, which, as we know, is a formidable threat to continued success in delivering healthcare to our population.
As we embrace modernization, we should also consider the solution and product development process, meaning Agile, use-case-centered project execution, and treating analytics solutions with a product mindset, which helps focus on the value delivered by that product instead of the process of creating that value.
Modernizing Data and Analytics in Healthcare
A global health service company needs 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 to CMS.
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.
We provide data lake roadmaps, rapid deployment pilots, and full life-cycle service engagements. Our main objective is to provide a better “time-to-value” delivery model using a data lake in conjunction with or as a replacement to an EDW and to provide best practices to ensure the data lake doesn’t turn into a “dumping pond.”