Health plans have invested in technology across risk adjustment, quality management, utilization management, and other core functions for years. Those investments often sit across different systems, teams, data pipelines, and vendor relationships.
The decision to buy a chart retrieval tool, a gap closure platform, and a prior authorization intake system often does not reach the executive team as one decision. It arrives as three, spread across budget cycles and owners, each clearing finance on its own terms and rarely evaluated against the others.
The result is often a health plan technology stack that no one deliberately designed as a whole.
As health plans increase their investment in AI, that fragmentation matters more. AI can automate individual tasks inside a point solution. But creating more connected, AI-enabled operations requires something broader: trusted data, reusable infrastructure, and the ability to orchestrate workflows across existing systems rather than adding another isolated layer.
Why health plan technology became fragmented
Risk adjustment, quality management, and utilization management evolved as separate functions, each with its own teams, workflows, regulatory requirements, and technology decisions. RADV requirements shape risk operations. HEDIS and Stars drive quality programs. Prior authorization requirements and turnaround times shape UM.
The systems purchased to support those functions were not necessarily the wrong decisions at the time.
What executives are left managing years later, however, is often a portfolio of vendor contracts, internal applications, and data pipelines that were never evaluated as one architecture. No CFO deliberately set out to create multiple disconnected foundations for the same member population. The environment accumulated one reasonable decision at a time, showing up as duplicated data movement, parallel integrations, fragmented workflows, additional vendor management, and leadership teams looking at different versions of the same member population.
Point solutions still have an important role. A specialized system may solve a particular problem exceptionally well, and replacing technology that is already working does not automatically create value. The better question is whether new investments can connect with what is already there. A more flexible health plan architecture should allow plans to preserve existing investments, add capabilities where they are needed, and create greater connectivity across the environment over time.
The cost of a fragmented payer technology stack
Fragmentation is not only an IT concern. It can affect operational and financial priorities executives already track closely.
Stars and quality performance. Crossing key Star thresholds can materially affect Quality Bonus Payment eligibility and a plan's competitive position, with the effect landing in the payment year that follows the measurement period. A gap missed this cycle costs a full year of standing, not just a short delay. When member data, clinical activity, and gap closure workflows sit across different systems, teams can spend significant effort finding, reconciling, and moving information before they can act on it.
RAF accuracy and revenue integrity. For CY2026, the 2024 CMS-HCC model, commonly referred to as V28, is fully phased in for non-PACE Medicare Advantage organizations. Coding accuracy determines how well risk-adjusted revenue reflects documented member acuity, while CMS has significantly expanded RADV oversight. When chart retrieval, coding, validation, and clinical review operate across disconnected workflows, complete and supported diagnoses may surface later than they should.
Utilization management performance and compliance. CMS-0057-F requires impacted payers to send prior authorization decisions within 72 hours for expedited requests and seven calendar days for standard requests for applicable non-drug services, with specific reasons for denials. Meeting those expectations depends on giving review teams timely access to the clinical and member context they need without repeatedly reconstructing the case across multiple systems.
Competitive pressure from AI. BCG estimates that AI could reduce payer administrative costs by up to 40%, while cautioning that cost savings alone will not protect incumbents from AI-powered competitors. It also estimates that roughly 70% of the effort required to become an AI-first health plan lies in rewiring the organization rather than implementing the technology. Adding another disconnected application does not address that 70%.
The broader issue is whether the health plan technology environment makes data, governance, and AI reusable across functions, or requires teams to rebuild those foundations every time a new use case is introduced.
What a connected health plan changes
A connected health plan runs on an underlying environment that works together, whether or not every workflow sits on the same application. That starts with a trusted, governed view of the member that can support workflows across the enterprise while allowing individual teams to retain the systems and processes that continue to serve them well.
Consider a member with an undocumented HCC, an open quality gap, and a pending authorization for a related procedure. In a fragmented environment, different teams may have to assemble that member's context independently from separate sources. In a connected environment, those signals can be understood against the same longitudinal member context while each function continues to apply its own clinical, coding, and regulatory rules.
The distinction matters most at the edges. Unifying only two of the three functions, most often risk and quality, still leaves UM answering to its own systems and its own clock, which is exactly where compliance risk concentrates fastest under CMS-0057-F. Putting information from multiple systems onto the same dashboard may improve visibility. But greater connectivity comes from making trusted data and context reusable across workflows, regardless of which application a particular team uses to complete its work.
That becomes even more important as health plans adopt AI agents. An AI agent operating inside one isolated application can make a task faster. AI working across a connected data and workflow foundation can support a broader operating model, while still interacting with existing systems and keeping appropriate human oversight in place.
A modular approach to connected health plan operations
Galaxy is designed to support connected workflows across health plan operations on a common healthcare data and AI foundation. Innovaccer was named the 2026 Best in KLAS winner for Data Analytics Platforms for Payer and is trusted by 7 of the top 10 payers.
A connected approach does not mean asking a health plan to replace the technology it already runs. Galaxy is modular by design. Plans can adopt the capabilities they need, integrate them with existing workflows and infrastructure, and expand over time as priorities change.
A plan may begin with one high-value workflow. Another may use Galaxy to augment an existing system that remains central to its operations. Others may connect several functions to the same data and AI foundation while continuing to use specialized applications elsewhere.
For executive teams, that creates a different way to think about health plan technology investment. The question becomes less about choosing between a point solution and a wholesale platform replacement, and more about whether each new investment strengthens the architecture already in place.
Questions health plans should ask before adding another point solution
Does this investment solve only today's use case, or create something we can reuse tomorrow? A specialized capability may still be the right choice. But plans should understand whether the data, integrations, governance, and AI built around that capability can support additional workflows or remain isolated inside one application.
Can this work with the systems we already rely on? Modernization should not require abandoning technology that continues to perform well. A connected platform should be able to augment existing systems, exchange data with them, and support new workflows without requiring an enterprise-wide replacement.
Does this change what teams can act on, or only what executives can see? Bringing information from multiple functions onto one dashboard can improve visibility. But the deeper value comes when trusted data can actually be reused across workflows without another team having to extract, reconcile, and move it.
Can AI operate across the environment, or only inside individual applications? AI layered onto fragmented systems may accelerate individual tasks while leaving the underlying operating model unchanged. A shared data and AI foundation makes it possible to reuse context, governance, and intelligence across more workflows while continuing to work with existing applications.
Does this architecture give us more flexibility over time? Plans should not have to know today exactly what their technology environment will look like several years from now. A modular architecture allows them to start with the highest-priority use case, preserve existing investments, and add capabilities as business needs evolve.
The real cost of creating a more connected health plan
Creating greater connectivity across a health plan is real work. It touches data governance, integration, operating processes, and teams whose workflows may have been built around the current environment for years.
Coders, clinical reviewers, quality teams, and operational leaders need to trust the data they are using. Leadership needs to decide where shared infrastructure creates value, where specialized systems should remain, and how governance should operate across AI-enabled workflows. That is as much an operating-model decision as a technology one.
The comparison, though, is not between change and no change. Maintaining a fragmented environment also carries a cost: repeated integrations, duplicated data preparation, parallel governance, additional vendor management, and another technology build every time a new operational use case emerges.
Health plans do not need a single cutover to get there. They can start where the pressure is highest, connect what creates immediate value, and expand from evidence, whether that means strengthening a quality program, improving risk adjustment operations ahead of increased audit scrutiny, or augmenting a utilization management workflow struggling to meet new turnaround expectations.
For an executive team, that reframes the underwriting question. Instead of asking what needs to be replaced, ask where the same data, integration, and AI foundation is being rebuilt more than once.