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Healthcare’s digital transformation is charging forward at a breakneck pace, but roadblocks remain. One of the biggest challenges healthcare organizations are facing? Data quality and seamless integration. It’s a high-stakes challenge that’s getting more important every day, as interoperability mandates become the norm, patients demand more connected care experiences, and industry stakeholders across the ecosystem want to make decisions using real-time insights.
In a 2025 webinar, Unite Genomics CEO Taner Dagdelen joined Vivek Mukhatyar, Generative AI Medical Engagement Lead at Pfizer, Lance Hill, Chief Executive Officer at Within 3, and moderator Paulo Machado, Chief Executive Officer at Health Innovation Inc. for an an exploration of the promises and challenges in applying AI to real-world health data. This insightful conversation touched on many timely topics, including the importance of solving data quality and integration challenges to clear the path for true innovation.
In this blog, we’ll explore key takeaways from the conversation, while sharing best practices gleaned from Unite Genomics’ groundbreaking work with leading life sciences companies.
Solving data integration doesn’t mean solving one unique challenge. Rather, it’s a complex set of problems that require strategic, methodical, and disciplined unraveling. Most health systems are juggling multiple EHRs, inconsistent data formats, siloed and legacy systems, and ever-evolving data security and privacy regulations. No wonder healthcare technology leaders feel like they’re always two steps behind.
And successful data integration isn’t only about connecting systems. It’s about ensuring clean, accurate, usable data—the data quality piece we mentioned earlier.
“It takes time to build out these integrations," says Taner. “It takes time to figure out how to properly chronicalize data from different sources and merge things into a common data model in a way that’s really high quality. Some of it is really tough engineering models. Some of it isn’t rocket science, but it’s just a large volume of work that needs to be handled.”
So what’s a healthcare leader to do, when the data is complex, the systems are disparate, and the clock is ticking? Work with the right partner.
Many life sciences companies make the mistake of going it alone—assuming they can tackle their biggest, most challenging data integration problems on their own. The problem is that this approach requires bandwidth, specialty expertise, and time that most companies simply can’t afford.
At the end of the day, building integrations at scale is time intensive, labor intensive, and costly. So it’s better to find a partner who’s been there, done that.
“The crucial thing is moving forward with platforms that have already done this at scale,” explains Taner. “If you leverage a platform that’s already done the hard work, you can piggyback, move quickly, and unlock value right away.”
“On the other hand,” he cautions, “if you partner with someone who has done proof of concept work but has to build out that capability while they’re working with you, it’s just going to take a lot of time.”
Healthcare organizations can accelerate data maturity by collaborating with partners who have already solved the problems they’re facing. At Unite Genomics, we’ve spent years laying the groundwork for life sciences companies to be able to tap into our roadmap.
“We built out integrations with 19,000 different health systems and hospitals across the US,” says Taner. “It took years. They're using different EHRs, each with their own quirks that need to be addressed to make sure the data is high quality. We didn’t have the option of not solving these problems early on—but it was painful. A lot of sleepless nights, a lot of work, and no shortcuts.”
But, the proof is in the data quality.
“We’re delivering data back to patients on our platform and helping them make meaningful use of their data in real time,” says Taner.
At the end of the day, health systems that trust companies with theoretical capabilities risk falling behind those using platforms tested with real data.
As Taner recommends, “To the extent possible, leverage platforms that have been in production with real health systems, real patients, real data for years.”
To learn more about the Unite Genomics approach and get an evaluation of your current data strategy and how we can help you accelerate your path to connected, actionable data, reach out to our team.
