Key Data Considerations When Modernizing Healthcare Applications

Looking to modernize your healthcare application? Looking to modernize your entire enterprise architecture? Then you must go through the effort to evaluate and define your data strategy. What’s a data strategy? Consider the following questions:

• What domains of data does the application deal with?
• Is it a system of record or a system of reference?
• Is it a data consumer or data provider or both?
• If it consumes data, where does the data it consumes need to be sourced from?
• What applications need to consume data from this new application?
• How is the data secured in place and how is the data secured in transit?

These questions are the first steps in understanding how you need to approach the effort of modernizing your applications and evolving your enterprise architecture and will help to frame your data strategy. Once you’ve defined your data strategy, plan for how you bring everything else up to par – it will be a phased approach. Keep in mind, your strategy is worthless if you don’t have a mechanism to monitor and pulse-check your applications’ data as they progress through their modernization.

Application Modernization is a Data Migration

Whenever you are considering a modernization initiative, you are almost always talking about performing a data migration. Your new application will need to source its seed data from somewhere, and often the old application is the second best place to pull the data from. First prize? Your data management platform, which should have the complete view of your data and should have the absolute best and reliable version of the data. In some cases, you will use the existing system to seed the new one, but you should be prepared to utilize your data management platform to validate your migration. The risks of an incomplete or failed data migration can be catastrophic for a healthcare organization (can you think of the consequences of messing up an Epic migration?) and a robust data management platform can significantly reduce that risk.

Your healthcare data management platform should be a facilitator in this process, not an inhibitor. It should allow for continued refinement as you progressively re-define your enterprise architecture and data strategy. It should support you in transitioning from the post-migration go-live straight into production operations; remember, you aren’t done with enacting your data strategy just because you migrated the data, you still must continually govern the data and adjust over the lifetime of the application as you expand and modernize your other applications.

Phased Modernization is Enabled Through Information Agility

As the healthcare data technology landscape shifts and evolves (which it will) you will need a platform that helps you move along with technology at a pace that is appropriate to your organization – you can’t modernize everything at once. Not everyone is ready to turn off their COBOL mainframes in favor of K8s and Databricks, but being able to plan for it and off-load the responsibility of the data strategy to a platform allows you to set more focused goals so you can eventually turn off the mainframe.

Having a healthcare data management platform that is functionally positioned like a systems integrator with a central data hub is crucial in being able to progressively migrate off your legacy systems and begin to expand and align other business units across your enterprise to your data strategy. In healthcare, the ability to take a phased approach to just about everything is immensely valuable in order to minimize impacts to mission critical systems that impact the day-to-day operations of people providing and receiving healthcare services.

Data Strategies are Built on a Flexible Data Model

One of the most important elements of your data strategy: your data model. Not every business unit looks at a healthcare provider, organization, patient, or member the exact same. There are different owners of different areas of the data domains and while having a tool to govern the complexity of trust and ownership is fantastic, without a common enterprise data model you can’t govern anything. Utilizing an unbiased data model that is driven from industry standards like HL7 and FHIR and builds upon them to handle “the extra” that we all know always comes with healthcare is critical to success in your data strategy – this ensures that everyone’s opinion of the data can be governed in a common format.

Building an enterprise healthcare data model that expands all data domains of healthcare is not for the faint of heart and I would recommend against starting from scratch. Industry standards like FHIR and HL7 aide in a starting place towards interoperability but fall short when it comes to all those “extras” your business needs. So, inevitably, you may need FHIR plus some in order to run your business and if your healthcare data management platform isn’t flexible enough to allow for extending the data model as part of your data strategy, you introduce risk in your ability to meet the needs of your business.

Your healthcare data management platform should come out of the box with a robust healthcare specific data model that was built to accommodate the intricacies of healthcare data. I cannot emphasize enough the importance of a healthcare specific model – healthcare is complicated and oversimplifications are pitfalls to technical debt and massive re-works. It should allow you to extend the model in ways that do not cost an arm and a leg and six months of development work…. Six months is 179 days late.

Leverage Coperor™ Healthcare Data Management Platform

At Gaine, we’ve walked into some of the largest healthcare organizations on the planet and taken the time to tackle a very specific problem space and master it. We’ve poured an immense amount of technical expertise in building a data management platform that is purpose built to support healthcare organizations in modernizing their information systems away from legacy file-based exchanges to more real-time web API or data streaming. Coperor has native identity resolution and an out of the box extensible healthcare data model. This allows us to very quickly support concurrent data modernization efforts that are multi-application, multi-phased, and multi-domain – at scale.

Conclusion

Application modernization requires a clearly defined data strategy and a data management platform that can facilitate progressive modernization across your enterprise. The data management platform needs to be purpose built with healthcare in mind and needs the flexibility to support your specific business needs as it relates to your data.