Healthcare M&A data migration: achieve safety, speed and value

How can organizations migrate data to address rapid integration, reduce one-time costs and facilitate operational M&A objectives?

In brief

• Healthcare’s data growth has made migration essential to protect clinical safety, support compliance and capture deal value.
• Early governance, clear mapping and stakeholder approval are key to reducing risks and facilitating smooth transitions.
• AI can improve the speed and accuracy of healthcare data migration when used with the appropriate governance and human inputs along the way.

Healthcare data migration isn’t just a behind-the-scenes information technology (IT) task in healthcare mergers and acquisitions (M&A) — it’s essential for deal success, patient safety and operational integration. Yet, 55% of healthcare chief information officers (CIOs) cite data integration as one of their most important challenges, according to the EY 2024 CIO Sentiment Survey.

Shifting healthcare transaction environment

The healthcare M&A landscape is rapidly evolving, shaped by technological advancements and growing demands for seamless access to both patient and revenue data. Organizations are tasked with navigating compressed deal timelines, increasing the pressure to finalize transactions and exit transitional services agreements (TSAs) efficiently by completing integration and minimizing related costs.

In their pursuit of portfolio enhancement, hospitals are also expanding into a multitude of care settings. Each presents distinct data structures, volumes and system interoperability, which affect the overall complexity and level of effort of data migration.

Data complexity by healthcare settings

In all the care settings, data migration has a far-reaching impact across clinical, regulatory and operational areas:

Framework for data migration

The most successful healthcare integrations begin with planning before closing, with a critical focus on day one through day 90 for operational readiness and stabilization. Data and technology integration support operations through this period with quick wins and temporary data access while also creating the strategy for long-term technology integration.

Data integration maturity curve

These integrations begin by establishing a cross-functional data governance structure early in the deal. It’s important to appoint a data governance body that includes IT, clinical operations, legal, privacy, compliance and finance.

Why early governance matters

It’s critical that the governance body (or framework) answers foundational questions early:

1. What data is required for clinical safety on day one?
2. What are the legal or regulatory retention requirements?
3. Which systems will remain, be retired or be consolidated?
4. How will we treat commingled data in cases of divestitures?
5. Which records require cleansing before they can be migrated?

A structured data governance framework provides decision trees, domain-specific guidelines and advisory board structures that make sure that each function participates in defining authoritative data sources and separation principles.

Governance is not bureaucracy — it’s risk reduction at enterprise scale.

A proven approach to data migration for EHRs and clinical systems

Once governance is in place, the organization can move into a structured data migration approach: discover, build, separate and migrate. While the process is fundamental, new AI and automation capabilities can add significant time savings and improve quality through data discovery and accelerate complex analysis while reducing risk of error. However, the use of AI still requires a governance-first approach with human-led interaction to maintain required data quality validations and achieve operational and regulatory acceptance of the data post-migration.

Key data migration lessons from an academic medical center carve-out

We advised a leading academic medical center in its acquisition of two local community hospitals and clinics that were divested by another national health system. The facilities were previously operating on a legacy EHR, which required careful identification and planning of data migration to the health system’s Epic environment and archiving to meet operational and regulatory requirements. Additional complexities with the carve-out led to three lessons learned:

1. Obtain approval from the data governance board and stakeholders on the specific data types and the duration of each. Changes to the migration can cause timeline shifts and additional expenses. Examples of data types and duration include 15 years of patient demographics, encounters, lab and radiology reports.
2. Start contractual negotiations early with the legacy system data provider and technology vendors to define data types, formats and data requirements. Remember that moving from different EHR platforms is exceedingly more complicated for data mapping and conversion.
3. Develop clear and concise end-user communications on what providers will be able to access in the new systems at go-live and in subsequent days. Final data migration is often an iterative process, and the end-user experience may be cumbersome until all data is migrated and archived.

EY-Parthenon practice Shashi Shrimali and Shiny Sulaiman contributed to this article.