Structure Your Data Approach

“Cloud enabled” sounds impressive, yet overcoming the challenge of aggregating and structuring data is the first major technical obstacle for many organizations seeking to implement a centralized patient data estate.

Has your business built a targeted approach to evaluating existing data sets, future data needs and outlining a methodology to integrate it all?

Disparate Data, Common Challenges

Several cloud technologies are emerging to push boundaries of traditional medicine via enablement of customized treatments such as targeted genetic medicine, bionics & AI-based treatment plans. A patient's "digital profile" is beginning to emerge and a wave of innovation is bringing the consumer-driven digital health that we have heard about to reality.

Patient empowerment is a major requirement moving forward, where patients must take ownership of their own health records and analytics to shape their personal healthcare strategy. By owning their data and having visibility to their own records, new personalized (and accurate) treatment allows more targeted treatment. From this, integrated delivery models emerge in non-standard methods - such as at-home & digital care, telemedicine and potentially even remotely operated procedures. Of course, this enables access to care in remote & rural locations where specific facilities may not exist.

When we think cloud-enabled we may first think of cloud-connected health devices such as smart watches and other wearables. The cloud also enables secure online access to medical records, PACS servers and hospital information systems. Patient data, being contained in a multitude of disparate sources - various lab systems, imaging systems, electronic records, etc. - is also unstructured in nature. These data points are often trapped in clinical narratives behind EHRs, PDFs or image files that can be difficult to put together in a single, cohesive system.

Success in healthcare requires immersion in the space - not only technical expertise, but healthcare domain expertise and the willingness to tackle obstacles such as disparate data sources, privacy and compliance requirements and integration/interoperability challenges.

Structured Approach, Structured Risk

Building a conceptual data model at a high-level allows an organization to gain consensus and begin development, perhaps even enabling application use-cases, but often efforts are slowed down the line when more tailored use-cases or unique integrations are defined. Consider the following common data challenges:

• Paralysis-by-analysis: You want to ensure everything is accounted for, but is that realistic? Can your organization understand all the future use cases and requirements, or spend the time necessary to fully document all the requirements of the existing healthcare data integrations?

• Workflow documentation: When business rules exist in both Information Technology, Operations and day-to-day service/patient care - is it realistic that 100% accuracy or capture of all workflow logic can be completed in a timely manner? Is workflow logic likely to update, change, or be deprecated in the timeframe it would take you to document it all?

• Predefined: “Out of the box” data models are often a good starting place, but they weren’t built for you. Consider the time spent understanding why a common data model was built, whom it was built for, and what customizations would be required to get it functioning for your business. Could you build your own data model in that timeframe instead?

A structured, methodical approach to how your data estate is designed is required to get off the ground quickly, without spinning wheels on design specifics for edge-cases. Data must be designed to support organization perception of the business world, applicable to end-user clinical & operational processes and ultimately extensible for the future.

Thinking Outside the Box

Whether you are leveraging a typical application development pattern or a low-code/no-code platform, the need to structure entities and store/manage data is paramount. Microsoft’s common data model, as depicted above, paints broad strokes to allow the flexibility to begin development while allowing for integration down the line. This data model doesn’t consider all factors associated with your structured data requirements - the operating model (think Who, What, Where, When, Why), the storage constructs, automation of integrations and ETL, approval workflows and analytics visualizations. Working with a trusted partner is key. Eastwall has worked with dozens of leading organizations to prioritize data initiatives, gather requirements and build data estates that fit cloud-native use cases. We help design, build, and operate innovative cloud solutions on the Azure platform. Please contact us for a free consultation on how various Azure cloud services can help transform your business. Eastwall has a proven Data Modernization methodology that can help your business navigate the challenges of modern data.