3 min
Data Leaders Uncovered

What Healthcare Providers Must Get Right After a Merger

Part of the Data Leaders Uncovered series.

Applied AI

Data Analytics

Data Engineering

Introduction

Healthcare providers continue consolidating—multi-site groups, specialty networks, PE-backed platforms, and value-based care organizations. Growth often comes through acquisition, but the real test of value creation begins after the deal closes, when clinical operations, financial systems, and patient experience must function as one.

This edition of Data Leaders Uncovered features Micheal McAughey, a data and strategy leader whose experience spans Fresenius Medical Care and other major organizations in chronic kidney care. His work inside a complex, multi-entity healthcare merger reveals what actually breaks, what must be rebuilt, and how providers and investors should think about data as the foundation for clinical and financial performance.

1. When Systems Collide After a Merger—and How the Real Work Begins With Integration

Healthcare rollups bring diverse operational histories together—different EHRs, documentation patterns, coding logic, and data models. As Micheal described it, “you get as many versions of the truth as you have companies involved.” What looks like an acquisition on paper becomes, in practice, a deep integration exercise where every core system has to relearn how to work.

Integrating these environments requires substantial foundational work:

  • Standardizing patient identity across inherited systems
  • Rebuilding ingestion pipelines for clinical, claims, and operational data
  • Normalizing labs, diagnoses, and classification logic
  • Establishing a unified cloud and analytics architecture
  • Consolidating workflows into shared operational platforms
  • Defining common metrics and reporting standards

These steps don’t eliminate complexity overnight, but they create the first opportunity for the organization to behave like a single platform instead of a collection of inherited parts. A healthcare merger doesn’t function until the data does.

2. Data as an Extension of the Patient

Micheal emphasized a principle that should guide modern healthcare organizations: data isn’t an asset—it’s an extension of the patient. The integrity and accuracy of that data directly affects patient well-being. A missing diagnosis code, inconsistent documentation, or a delayed lab feed can alter a risk score, disrupt outreach, or delay an intervention. The stakes are clinical, not just technical.

This also has financial implications. Fragmented or inconsistent data undermines claim accuracy, performance under value-based contracts, and operational efficiency. For PE-backed operators, data integrity becomes a direct contributor to EBITDA—because poor data flows quietly erode both outcomes and margins.

3. AI Only Works When You’re Ready for It

As AI accelerates, many organizations want to deploy it quickly. Micheal’s perspective is clear: AI only adds value when the underlying data environment is trustworthy. Without aligned identity, stable ingestion, and clear definitions, AI merely amplifies fragmentation.

AI readiness—not AI adoption—is the strategic objective. Once foundations are in place, AI becomes genuinely useful in relieving administrative burden. Automated documentation, coding assistance, ambient note-taking, and authorization support free clinicians to focus on care rather than administrative overhead. These benefits are real, but they depend entirely on reliable inputs.

4. What Integrated Data Enables for a PE-Backed Healthcare Services Platform

Once the data environment is unified, a healthcare platform gains capabilities that support both clinical improvement and scalable operations. Integrated data enables:

  • Population-level risk stratification across newly acquired entities
  • Targeted, data-driven patient engagement with appropriate cadence and channel
  • Unified clinical and operational workflows for care teams
  • Consistent KPI measurement and reporting across acquisitions
  • Faster integration of future acquisitions and shorter time-to-value

This is where the investment thesis becomes tangible. When data flows cleanly, outcomes improve, operational teams execute with confidence, and each incremental acquisition plugs in faster.

5. The Vendor Challenge: Healthcare Doesn’t Need Templates—It Needs Context

Micheal highlighted a recurring challenge: many vendors arrive with a fixed framework that assumes a level of data maturity healthcare organizations often do not have. It is common for organizations to lack foundational elements such as a complete data dictionary, aligned coding practices, or stable ingestion pipelines. Dropping in a rigid template under these conditions often creates friction instead of progress.

Healthcare mergers also bring uneven capabilities across entities—different EHR workflows, documentation habits, claims processes, and operational cultures. A templated approach cannot absorb this variability. Effective data work depends on understanding the clinical and operational realities of each part of the organization.

Micheal’s message is that vendors should start with diagnostics, not prescriptions. They need to understand how clinicians document care, how claims data enters the system, how information moves through operational workflows, and where foundational gaps exist. Without this context, even advanced tools become obstacles rather than accelerators.

The right partners remove complexity, adapt to the organization’s starting point, and help build the missing foundations before layering on scale or automation.

Conclusion: In Healthcare M&A, Data Integration Is the First Strategic Investment—Not the Last

Healthcare organizations don’t struggle because they lack modern tools. They struggle because their data doesn’t operate as one system. AI, automation, improved outcomes, and operational leverage all depend on foundational steps: unifying the data, standardizing definitions, and building pipelines that can be trusted.

If providers and PE platforms want AI to work—and want the value creation thesis of each deal to materialize—the sequence is straightforward:

Integrate first. Govern tightly. Build trust in the data. Then deploy AI.

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