To leverage the latest automation and technology, healthcare organizations need a data analytics infrastructure they can build on. Here, Arcadia Chief Product and Technology Officer Nick Stepro and UCLA Health Chief Data Officer Albert Duntugan share steps that organizations can take to set up a solid foundation.
Big data requires big planning. For organizations that want to leverage data for a competitive advantage — or just improve outcomes and strengthen performance — a robust data analytics roadmap is critical. To successfully navigate the complexities of data management and analytics, organizations need to lean on three pillars: leadership buy-in, ironclad security, and regulatory compliance.
Below, we’ll walk through each of these important pieces of a reliable data analytics infrastructure, and share questions organizations can ask as they refine their strategies.
To build a robust, efficient data analytics program, there are three key considerations a healthcare organization should consider. Start here and establish a solid foundation of healthy, reliable data that users trust.
Building an effective data analytics framework begins at the top. Before they trust a system, users will look to leadership to gauge whether or not it’s worth investing the time and energy to learn it.
Often, executive leadership aren’t obsessed over details or particular inputs the way individual users are — they’re thinking of a data platform’s capabilities holistically. But thinking about this from a user’s perspective can help them speak to data analytics’ value for specific roles and purposes.
Explaining new tech adoption within an organization goes a long way, because its utility and potential can be hard to understand.
“How do you not scare everyone away, but still somehow convey the value of it?” asks Duntugan. “That it’s worth it to make this investment?”
In data analytics, security and privacy are key prerequisites for a trustworthy tech tool. Data shouldn’t lose its integrity as it’s passed between systems or users, and it must remain both confidential and accessible for those who need it. This means attempting a balancing act between security measures and readily available data, protecting against breaches and considering interoperability.
Leaders use regulatory compliance as a compass to guide them through the legal challenges associated with data usage. From industry-specific regulations to international data protection laws, a compliant data analytics system helps organizations adhere to legal standards, mitigating the risk of penalties.
Having established these pillars, organizations must address three critical questions:
1. The diversity of data: What are we compiling?
Data comes in various forms and from multiple sources — Electronic Health Records (EHR), Customer Relationship Management (CRM) systems, payroll, research outputs in academic health systems, and beyond. Understanding the type of data that’s coming into a system is essential to design the analytics architecture that can handle complexity and deliver meaningful insights.
2. The interpreters of data: Who validates our findings?
Analytics is not a solitary pursuit. It requires collaboration across disciplines. Technologists need the validation that only subject matter experts can provide. Clinicians, for instance, must verify healthcare data, while business line experts are essential to contextualize workflow insights. This interdisciplinary validation ensures that data insights are accurate and actionable.
“We can’t be siloed off as technologists, thinking that all we need are total sums or check values and all will be good,” Duntugan reiterates. “We need some kind of clinician to validate numbers. We need someone in the business line to tell us, ‘No, this is how things happen in workflow.’”
3. The accessibility of data: How do users engage with it?
The engagement with data varies based on technical expertise. While advanced programmers and data scientists may delve into complex analyses, those on the front lines of business operations need simpler, accessible insights. This requires solutions ranging from low-code/no-code platforms to intuitive visualizations. By democratizing data access, organizations empower all employees to make informed decisions and take effective action.
At its best, a data visualization serves as a bridge between users of varying technical backgrounds and valuable insights hidden within data. Visual representations can take complex ideas and make them comprehensible to different audiences.
Different users need data with different degrees of detail. Data visualization can take intricate insights and package them for someone with a lower degree of context with that data, allowing them to understand those insights.
“It’s fun to geek out and see what the latest, greatest thing is, and turn that on,” Duntugan says. “But in the end, we have to remember that we’re in healthcare. We’re about taking care of patients. We’re about making sure that the clinicians have their job set up in a way that it’s easy to do. How do you take all this data and bring it to bear on their jobs?”
The road to building a resilient and efficient data analytics structure is paved with thoughtful planning and a commitment to these foundational pillars. By thinking about end users’ needs and data best practices, leadership can foster an environment where data is both accessible and secure. With a great foundation in place, businesses can unlock the true potential of their data assets, turning information into insights and insights into action.