Why AI Has Reignited the Build vs. Buy Debate in Healthcare Technology

The “build vs. buy” debate in healthcare information technology has never really died, but it is abundantly clear that the emergence of AI has given it a new life. 

With rapid advances in large language models and automation tools, many development teams believe they finally have everything they need to build sophisticated data management infrastructure, and clinical intelligence engines, internally. After all, if AI can summarize notes, generate code, and extract insights from text, then building complex healthcare functionality should become faster, cheaper, and easier to sustain.

Reality, however, has been far less forgiving.

Organizations moving from demos to deployment have discovered that AI changes the surface of the problem without reducing the underlying complexity. Clinical intelligence remains one of the most challenging domains in healthcare technology to design, implement, and sustain. In many cases, AI accelerates experimentation while increasing long-term cost, risk, and maintenance obligations. This gap between expectation and execution is pushing healthcare leaders to revisit the build vs. buy decision with fresh urgency.

Reliability and the Criticality of a Deterministic Source of Truth

Meaningful clinical intelligence depends on far more than the text generation or pattern recognition that AI tools deliver, although they accomplish these tasks exceptionally well. Instead, maximizing the benefits of AI in clinical settings requires a deeply structured, clinically validated foundation that aligns with how clinicians document care, how payers evaluate risk, and how regulators enforce standards. That foundation includes terminologies, mappings, evidence-based rules logic, and governance processes that must evolve continuously.

AI tools can assist with isolated tasks such as drafting documentation or identifying candidate data points. They do not remove the need for curated clinical relationships, versioned logic, or traceable decisions. Without that foundation, outputs remain difficult to trust, validate, and scale. This is the deterministic layer that experts now point to as the critical piece. 

As teams begin to operationalize AI, they also encounter new layers of oversight, monitoring, and remediation. These requirements introduce additional cost and complexity rather than reducing them. In the end, instead of replacing foundational work, AI adds another dependency that must be managed alongside existing systems.

The Hidden Costs

Organizations often end up in this situation because the build vs. buy debate starts with development costs. Building internally appears attractive when teams focus on engineering hours and short-term budgets. Over time, however, the economics shift in ways that many organizations underestimate.

For example, coding systems update annually, quality measures evolve, interoperability expectations expand, and opportunities for new capabilities continually emerge. Each change requires updates across content, mappings, validation rules, and reporting logic. Internal teams become responsible for tracking these changes, implementing updates, testing downstream effects, and preserving historical accuracy.

As this work accumulates, maintenance becomes a primary operational function rather than a background task. Even organizations with strong informatics expertise find it difficult to keep pace indefinitely. If key architects or clinical experts leave, continuity suffers and risk increases. AI does not remove this burden. In many environments, it compounds it by introducing additional models, workflows, and governance requirements that must also be maintained.

The implications of these constant updates become more visible over time. Small inconsistencies can simultaneously affect documentation quality, reporting accuracy, reimbursement outcomes, and analytics. The larger the footprint, the greater the exposure. Errors that once affected a limited use case can expand to the point where they influence enterprise performance and regulatory posture.

This dynamic also affects speed. Teams focused on maintaining foundational logic spend less time on differentiation. While internal resources are devoted to sustaining infrastructure, competitors move ahead with features that improve user experience, analytics, and market responsiveness. Opportunity cost becomes just as meaningful as direct expense.

Never-Ending Maintenance 

One of the least discussed factors in the build vs. buy decision is how maintenance costs behave over time. Building a system once is expensive. Keeping it current year after year is where budgets are won or lost.

Internal systems concentrate cost and responsibility within a single organization. Every update requires local expertise, testing cycles, and validation. Every mistake is owned and needs to be fixed by the team that built it.

Shared technology foundations follow a different economic model. With many organizations relying on the same underlying capabilities, the cost per unit to maintain and enhance those capabilities decreases. Updates are implemented once and benefit everyone. Improvements reflect real-world usage across a broad range of environments rather than a single set of assumptions.

This shared ownership model also accelerates learning. Patterns emerge earlier. Edge cases surface faster. Regulatory interpretations stabilize through repeated application. Each participant benefits from the group’s collective experience, reducing uncertainty and smoothing operational variability.

Build What You’re Good At

When organizations decide to buy rather than build, the advantage is not convenience. It is structural. Proven healthcare technology platforms are designed from the outset to absorb constant change, including new regulations, evolving clinical standards, and expanding use cases. That design intent matters because clinical intelligence requires continuous updates to terminology, logic, mappings, and governance, rather than a one-time implementation.

At a practical level, solutions that can handle this ongoing change tend to share several characteristics. For example, they are built on clinically validated data models grounded in real-world practice. They also include continuous maintenance of standards, mappings, and logic to keep systems current without repeated internal rework. Incorporating governance frameworks that support transparency, traceability, and audit readiness are also essential. 

Sustaining these characteristics over time is where the best build vs. buy decision becomes clearer. Attempting to build and maintain this level of clinical intelligence internally concentrates cost, risk, and operational burden within a single organization. Buying proven, widely adopted foundations spreads those responsibilities across a broader ecosystem, lowers the per-unit cost of maintenance, and reduces exposure as complexity grows. This shift allows internal teams to focus on innovation and differentiation rather than devoting scarce resources to infrastructure upkeep.

The Sustainable Path Forward

Certainly, AI is changing, and will continue to change, how healthcare information technology is built. Yet it has not significantly altered the challenges organizations have faced for decades when creating everything from scratch. 

Given the pressure to innovate and launch, organizations should continue to create their own solutions, build what differentiates them, but rely on shared, clinically validated foundations for core intelligence while directing internal teams toward differentiation and meeting their customers’ needs. 

By planning for maintenance as a shared responsibility and distributing risk across widely adopted platforms, organizations reduce long-term cost, limit exposure, and avoid locking scarce resources into infrastructure work that does not drive competitive advantage.