Beyond the buzzwords: Daniel on what healthcare systems really need

The global conversation on healthcare innovation continues to centre on technology, but a growing number of experts are questioning whether this focus is misplaced. While artificial intelligence, machine learning, and automation are often presented as signs of progress, some argue that their value lies not in their presence but in their impact on care delivery.

Daniel Abaneme, a Research Program Manager at CentraCare Health in Minnesota, is among those calling for a shift in thinking. Speaking from his experience within a health system serving rural and micropolitan communities, he said the sector risks losing sight of what matters most.

“AI doesn’t improve healthcare by existing,” he said. “It improves healthcare when it’s designed to fit people, workflows, and accountability structures.”

According to him, many systems invest in building tools but fail to integrate them into daily clinical practice. He explained that the main challenge is not the development of models or platforms, but what happens after deployment.

“The hard part is what comes after: aligning new tools with real workflows, regulatory demands, data integrity, clinician trust, and patient needs,” he said. “Without that alignment, even the most sophisticated solutions stall as pilots, slide decks, or abandoned ambitions.”

Abaneme’s approach is shaped by his earlier work at the National Institute on Aging at the National Institutes of Health, where he helped build a high-throughput proteomics workflow within a regulated environment. The project required coordination across procurement, protocols, and data systems, laying the foundation for his current focus on system-level infrastructure.

“Healthcare research isn’t just about answering questions,” he said. “It’s about building systems that allow good questions to be asked repeatedly, ethically, and at scale.”

In his current role, he oversees research initiatives that cut across departments, including clinical studies, observational research, and partnerships with external organisations. Each project, he noted, depends on coordination between clinicians, analysts, and compliance teams, with infrastructure serving as the backbone.

His work in machine learning research also reflects this perspective. As a contributor to studies on early disease detection and mental health assessment, he has focused less on model performance and more on how outputs are used in practice.

“An algorithm that lives in a paper doesn’t save lives,” he said. “An algorithm that’s embedded into care teams, with clear escalation paths and governance, can.”

He stressed that this distinction is important in areas with limited access to specialised care, where delays in diagnosis can affect outcomes. In such settings, he said, the value of AI lies in whether health systems can act on predictions in a safe and consistent way.

For Abaneme, innovation in healthcare extends beyond advanced systems. He pointed to the role of machine learning in identifying patients at risk, the use of automation in reducing administrative work, and the need for stronger data systems that support decision-making.

However, he cautioned against measuring success based on technical capability alone.

“The question isn’t whether a tool is impressive,” he said. “It’s whether it improves access, timeliness, consistency, and equity in care delivery. Those are the metrics that matter. Everything else is a prototype.”

Alongside his work in data and systems, Abaneme has also focused on workforce development. He has supported research internships and mentorship programmes aimed at building capacity within local communities.

“Technology doesn’t build capacity, people do,” he said. “Our responsibility is to design systems that make people better at what they do, not smaller.”

He added that traditional measures of success, such as delivery timelines and budgets, are no longer enough on their own.

“Being on time and on budget is the baseline,” he said. “The real question is whether the work improved decisions, reduced inequities, and built institutional capability.”

As healthcare systems face rising demand and ongoing challenges linked to workforce pressure and the use of AI, Abaneme’s message points to a need for balance. While innovation will continue to shape the sector, its success will depend on how well it is integrated into care.

“The question is not whether innovation is coming to healthcare,” he said. “It already has. The real question is whether healthcare systems are prepared to use it in ways that improve care rather than simply modernising its appearance.”

“That’s where the next chapter of progress will be written,” he added. “And it will not be written by technology alone.”