Artificial intelligence may dominate healthcare technology conversations, but Patrice Bordron, chief digital information officer at Community Health Systems in Franklin, Tennessee, believes interoperability will determine whether health systems can make AI useful at scale. Healthcare remains a loosely coordinated network of independent providers, payers and facilities, and years of investment in electronic health records have not eliminated disconnected data silos. Clinicians still duplicate information, reconcile records manually and move among multiple applications to complete a single workflow. Patients repeatedly provide the same details, face duplicate tests and experience delays when records do not follow them.
"AI may be getting most of the attention today, but interoperability remains one of the industry's most important challenges because it is the foundation that enables virtually everything else we hope to accomplish," Bordron said.
From digitization to usable data
Many healthcare organizations operate multiple clinical systems and EHRs that do not communicate effectively. Moving information from one system to another is not enough - the data must be accurate, usable and available within the clinician's workflow. That distinction grows more important as health systems deploy AI. Algorithms depend on complete, reliable and timely data. Fragmented information can deprive AI tools of the context needed to support safe clinical decisions. Health systems working to build reliable data foundations for AI can explore AI for Healthcare training to understand the data requirements that clinical AI demands.
At Community Health Systems, the response includes an application standardization initiative to create a more connected digital ecosystem. The work addresses variation in workflows and processes while strengthening enterprise architecture and data governance. New technologies are evaluated through an interoperability lens: tools should connect with the broader environment rather than introduce another isolated system. "Our goal is not simply to exchange more data, but to make data more usable and actionable while standardizing workflows, improving cybersecurity, and enabling caregivers to spend more time caring for patients and less time navigating technology," Bordron said.
Rationalizing the application portfolio
Over time, healthcare organizations accumulate bolt-on products designed to solve individual problems. Each addition may have seemed reasonable, but the collective result is a sprawling portfolio with overlapping capabilities, inconsistent workflows, higher licensing and support costs, and more interfaces to maintain. Additional systems, credentials and data connections also expand the cyberattack surface. Bordron said executives should resist the tendency to answer every new problem by buying another application. They should first determine whether a core platform or strategic partner can provide the capability, whether an existing product already performs a similar function, and whether the proposed tool will fit the organization's long-term architecture.
Simplification does not mean ignoring legitimate local needs. It means balancing those needs against the benefits of enterprise standardization, scalability and supportability. Working closely with EHR vendors and other strategic partners to expand capabilities within core platforms can reduce fragmentation, strengthen security and create more consistent experiences across facilities.
Interoperability as an executive capability
Bordron believes the C-suite should treat interoperability as a strategic capability rather than a technical project delegated to the IT department. Because healthcare delivery will depend increasingly on technology, executive teams should develop their mission and operating vision with technology in mind. For leaders looking to build this strategic alignment, AI for Executives & Strategy training offers guidance on integrating technology and care delivery goals. The harder work is often organizational. Health systems frequently automate existing processes without questioning whether those processes should continue in their current form, which can embed old inefficiencies in new systems.
"The real challenge is not technology adoption; it is having the courage to redesign workflows and operating models to fully realize technology's potential," Bordron said. Successful organizations, he added, will not be those that adopt the most technology. They will be those willing to simplify, standardize and redesign how care is delivered so that existing technology produces measurable value.
The discipline to simplify
Bordron's argument reflects a broader shift in the health IT agenda. The initial era of digitization asked how quickly an organization could implement new systems. The next phase is more selective: which systems deserve to remain, which workflows should be standardized, and which investments can deliver more value with less complexity. Interoperability therefore requires both architectural discipline and operational courage. Data standards and interfaces matter, but so do governance, application portfolio decisions and the willingness to retire tools that no longer justify their cost or burden.
The payoff is not simply a cleaner technology stack. A more integrated environment can reduce repetitive work, improve cybersecurity, create a more consistent patient experience and give AI tools a stronger foundation of reliable data. "Healthcare's challenge is no longer digitization - it is simplification," Bordron said.
Why this matters for healthcare professionals
Interoperability is not a back-end IT problem. It directly shapes the daily experience of clinicians, patients and administrators. When systems do not communicate, clinicians spend more time on data reconciliation and less time with patients. Patients face redundant tests and delays. For healthcare leaders, the message is that simplifying the technology stack and redesigning workflows can reduce administrative burden, improve cybersecurity and create the data conditions that clinical AI needs to function safely. The shift from digitization to simplification means professionals at every level should advocate for integration, usability and the courage to retire tools that add complexity without delivering value.
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