Healthcare legacy modernization cannot be treated like a standard application rewrite. For health plans, PBMs and Medicare-focused organizations, legacy systems are not just old technology. They are the operational backbone for claims adjudication, eligibility, enrollment, billing, reporting and member service. They often run on decades-old COBOL estates, support millions of members and encode years of product exceptions, reimbursement logic, eligibility determinations and regulatory requirements that cannot simply be “translated” and hoped for the best.
That is why healthcare modernization has historically moved so slowly. The real challenge is not deciding to modernize. It is proving that the new system will behave like the old one where it must, improving what should change and doing both without disrupting critical operations.
Today, AI is changing that equation.
At Publicis Sapient, we help healthcare organizations compress multi-year modernization programs into governed, measurable transformation using Sapient Slingshot and a human-in-the-loop delivery model. The goal is not faster code generation alone. It is safer modernization: extracting hidden business rules, generating traceable specifications and tests, accelerating COBOL-to-Java/React transformation and preserving compliance and operational continuity throughout the journey.
Healthcare organizations operate under a level of complexity and scrutiny that makes legacy transformation fundamentally different from modernization in less regulated industries.
Claims platforms may process billions of transactions annually. Enrollment and eligibility systems may carry decades of member history and plan-specific rules. Medicare environments must preserve CMS-driven logic across intake, eligibility, billing and reporting. Across the enterprise, systems are deeply interconnected, often poorly documented and dependent on a shrinking pool of specialists who understand how they actually work.
In many organizations, critical logic still lives in COBOL programs, copybooks, batch jobs and manual workarounds. That means important decisions about coverage, claims payment, enrollment status or premium billing may be embedded in code rather than in current business documentation.
The risks of changing that logic are not theoretical. A small defect can trigger improper denials, provider underpayments, eligibility drift, wrongful coverage termination, reporting issues or exposure of protected health information. Regulatory obligations around CMS and HIPAA raise the stakes further. In this environment, slowing down does not automatically make modernization safer. It often extends exposure by leaving brittle, expensive and hard-to-change systems in production longer.
Many modernization programs fail because they start with code conversion before they establish system understanding. In healthcare, that is backwards.
Before a claim flow is modernized, teams need to know what the existing system really does. Before enrollment logic is rewritten, they need to understand how plan rules, member states and billing workflows interact. Before services are decomposed into APIs and cloud-native components, they need to preserve the behaviors that protect compliance and continuity.
This is where behavioral equivalence matters.
For core claims and enrollment platforms, modernization is not complete when new code compiles. It is complete when the modernized system has been validated against the real behavior of the legacy one, with traceability across requirements, code and test evidence. In healthcare, “close enough” is not a standard. Teams need confidence that critical outcomes remain intact while the architecture moves forward.
AI now makes it possible to modernize large healthcare estates differently.
With Sapient Slingshot, Publicis Sapient applies AI across the software development lifecycle to make legacy systems more observable, more testable and more governable before change accelerates. Rather than treating modernization as a manual, screen-by-screen rewrite, we use AI to analyze legacy applications, surface hidden logic and create the artifacts transformation teams need to move with confidence.
That includes:
This changes the work around code, not just the code itself. It reduces dependence on tribal knowledge, shortens discovery and documentation cycles and gives business and technology teams a clearer baseline for decision-making.
Sapient Slingshot is built for enterprise modernization where context, governance and delivery rigor matter.
Unlike point coding tools, it combines a persistent enterprise context graph with specialized SDLC agents to help teams understand legacy systems, generate modern artifacts and maintain continuity across the modernization lifecycle. In healthcare, that means AI can work against a broader picture of the environment: business rules, data lineage, regulatory controls, cross-system dependencies and workflow relationships.
For healthcare organizations, this is critical because modernization rarely happens in isolation. Claims, eligibility, enrollment, billing and reporting workflows are interconnected. A change in one domain can affect another. By creating a living digital blueprint of the estate, Slingshot helps teams move beyond file-by-file analysis and toward structured, auditable modernization.
The result is not just speed. It is governed acceleration.
AI does not remove the need for expert judgment in healthcare. It makes that judgment more valuable.
Publicis Sapient’s approach keeps humans in the loop at critical validation points across discovery, specification, design, testing and release readiness. Engineers review outputs. Product and business stakeholders confirm intent. Domain experts validate rules that affect claims, eligibility and member outcomes. Compliance-sensitive decisions remain visible and reviewable.
This operating model is essential in regulated environments. It ensures AI is used to accelerate analysis, documentation, code transformation and testing, while accountability for quality and compliance stays with experienced teams.
For healthcare leaders, that means modernization can move faster without becoming a black-box exercise.
In one U.S. healthcare modernization effort, legacy COBOL claims applications had become a bottleneck after years of manual progress. Critical claims processes were still tied to thousands of green screens and costly mainframe infrastructure. Using Sapient Slingshot, Publicis Sapient helped convert legacy COBOL into maintainable Java and React, auto-generate functional specifications and test cases, and support a cloud-native deployment path with human validation throughout the process. The modernization moved 3x faster, reduced costs and accelerated a program that had previously advanced only incrementally.
In another healthcare example, a leading U.S. insurer processing billions of claims annually compressed a claims modernization roadmap from seven to 10 years down to roughly three years. By extracting business rules up front, generating validated specifications and continuously testing for behavioral parity, the organization reduced budget, lowered dependency on scarce SMEs and improved traceability from system behavior to modern implementation.
Publicis Sapient has also applied this model to Medicare enrollment modernization, where the challenge was preserving coverage integrity for millions while modernizing a decades-old platform. By sequencing transformation around intake, eligibility, billing and reporting, and validating each domain against legacy member-level outcomes, the organization preserved continuity while creating a more predictable phased roadmap.
The long-term value of healthcare modernization is not simply replacing COBOL with Java or moving screens into React. It is creating a more adaptable operating model.
Modern, modular and cloud-native services make it easier to launch change, improve member and provider experiences, strengthen security, connect data and support future AI use cases across operations. But that future only works if the modernization path itself is controlled.
Publicis Sapient helps healthcare organizations move from legacy constraints to cloud-native delivery by combining:
This is how healthcare organizations can modernize core claims and enrollment systems without pausing the business, compromising compliance or losing trust.
Healthcare leaders do not need another modernization story built around lift-and-shift ambition or unchecked AI automation. They need a model that understands the realities of the industry: millions of members, embedded business rules, regulatory oversight and no tolerance for operational disruption.
That is the model Publicis Sapient brings.
With Sapient Slingshot and human-in-the-loop delivery, we help healthcare organizations extract what matters from legacy systems, generate the specifications and tests required for confident transformation and accelerate modernization into a cloud-native future without losing control of the present.
Because in healthcare, modernization succeeds only when speed, compliance and continuity move together.