From strategy to execution

The biggest trends shaping healthcare today are patient-centricity and interoperability. Whether you're a hospital, insurer, or medical manufacturer, you must adapt, evolve, and strive to shape better patient experiences in a collaborative and integrated care ecosystem. And the key to this is good-quality data with centralized self-service capabilities.

As the industry leans into these transformative trends, data migration emerges as an integral facilitator, ensuring the smooth flow of critical information across the healthcare continuum. Join us as we unravel the role of data migration in the healthcare sector's pursuit of innovation and agility through application modernization and cloud migration. 

Strategy and scenarios to unlock successful data migration 

Data migration involves meticulous stages of data preparation, extraction, transformation, and the definitive move of high-quality data. This pivotal data transition from old applications to new ones constitutes an irreversible journey. Consequently, healthcare organizations face numerous challenges due to siloed processes and formats, low-quality or inaccurate data, and governance barriers. 
Data modernization and application transformation can help institutions overcome such challenges, ensuring interoperability and connectivity across the healthcare value chain. The primary objective is to retire obsolete data repositories and achieve the intended business transformation. 

Health leaders must commence with quality benchmarking to set the tone for a robust migration process. They must prioritize business goals over technical considerations, where data quality, schedule adherence, and budget allocations are paramount. 

Enterprises can follow two primary strategies based on the pace and nature of data migration. These are: 

• Big bang: This strategy entails a swift, comprehensive transfer of all data assets within a short migration window and minimal application usage, such as holidays or weekends.
• Trickle: This strategy adopts an incremental approach, facilitating a parallel run of old and new applications with minimal or no downtime. 

As organizations embark on application modernization or cloud journeys, they encounter a spectrum of scenarios. Such migration events, as listed below, are intricately linked to the organizations' technical architecture:

• Transfer between data repositories: Assets are transferred from one data repository to another—for instance, the transition from a data warehouse to a data lake.
• Platform-as-a-Service (PaaS) or Software-as-a-Service (SaaS) migration: Migration of on-premises assets to a target cloud environment. For instance, data transfer from operating systems to a PaaS model or from a tenant in a multi-tenant SaaS model.
• Migration across disparate application systems: Here, the source and target technologies differ, for instance, the transfer of clinical data from wearables to an integrated data ecosystem. This necessitates a thoughtful approach to bridge the gaps.

Today, the world is witnessing the accelerated evolution of business, operating, and infrastructure models arising from the exponential influx of data. Health firms must elevate their overall application effectiveness and be nimble to adapt to these demands. 

Healthcare data revolution: A cloud-driven approach to modernizing data and application

In the dynamic realm of healthcare, data modernization and cloud migration demand a meticulously crafted approach for enduring success. At CitiusTech, the factory model execution (have assets to accelerate the migration in factory model) is at the core of this transformative journey, enabling leaders to revolutionize healthcare systems. By employing a phased approach, organizations can prioritize risk reduction, maintain critical processes, and preserve data integrity. They can also ensure a frictionless migration through early identification and mapping of regulatory requirements. 

This proven process guarantees consistent success, laying the foundation for seamless and innovative healthcare data management. 

Below is the process to ensure seamless data migration.

1. Pre-game or discovery: At this stage, estimating the time required to load and validate all organizational data is essential. Enterprises must ensure security and compliance throughout the data migration process, with a focus on complying to regulations like the Health Insurance Portability and Accountability Act (HIPAA), the Patient Safety and Quality Improvement Act (PSQIA), and the Health Information Technology for Economic and Clinical Health (HITECH) Act. Migration specialists must also carry out the following steps:
   
   • Track and report on data quality for completeness, integrity, and correctness
   • Secure handling of Patient Health Information (PHI) and Personal Identifiable Information (PII)
   • Define data synchronization for co-existence in business-critical applications (applicable for only Trickle approach)
     Finalize infrastructure requirements, including hardware and software tools
2. Design, development, and execution: This sprint-driven stage involves an environment conducive to design, implementation, and testing, ensuring a sanitized and security-hardened setting in strict compliance with healthcare-related data regulations and privacy policies. There are five key processes involved:
   • Data Extraction: Information is retrieved from the source and transferred to the staging area using cloud migration or ETL tools.
   • Data Cleansing: Executing cleansing activities in the staging area, including domain checking, integrity enforcement, and de-duplication.
   • Data Conversion: Transforming data in staging into an intermediate logical model using mapping, filtering, separating, and combining operations including data quality.
   • Data Preparation: Involves thorough benchmarking and risk reduction activities before data migration.
   • Data Loading: Moving information from intermediate to physical database tables in batches or small chunks based on the plan.
     
     
3. Iterative execution and validation: In this stage, health data is classified into small subgroups to ensure smooth mappings, transformations, and cleaning procedures. Repeated testing and validation are carried out until high accuracy, integrity, and quality are achieved. There are three main phases involved:
   
   • Pre-migration:
     • Viability assessment of migration for screening impact
     • Collaborative execution by business and IT teams, especially migration tech teams and cloud architects
     • Conduct risk monitoring
       
       
   • Refresh and regression testing:
     • During execution, the source data requires refreshing based on requests from the testing and development teams. Typically, the data is refreshed before each testing cycle and the go-live phase. This stage includes:
       • Design a set of reconciliation reports driven by the utility to facilitate comprehensive regression testing following a data refresh
         
         
   • Factory model migration:
     • Process mapping to eliminate duplicated effort and address pain points
     • Leveraging utilities/accelerators/automated scripts for repeated migration steps
     • Gauge the time required to load and validate the new health systems
       
       
   • Post-migration testing:
     • Black box testing against business cases and non-functional testing like health security or care quality performance
     • Targeted testing to evaluate pre-migration application compatibility to guide testers on possible damages or gaps
       

       Figure 1: A typical data migration solution

4. Go-live strategy: This most crucial phase spans 3-4 weeks and centers on ensuring safe patient care, functional billing processes, and providing robust 24/7 support for end-users. Migration teams also carry out the following steps:

• • Load essential permissions and settings
  • Rigorously test the migration mirroring a live care environment and implement data migration policies and security rules
  • Meticulously scrutinize the data in the new system, actively ensuring accuracy and fortifying the groundwork for operational success

With a well-sculpted migration strategy , realistic expectations, proper tools, and processes, health leaders can effortlessly align their business goals, unlock revenue streams, and sync with evolving customer expectations while saving costs. 

A blueprint for tomorrow: Embracing cloud and data transformation in healthcare 

In recognizing the evolving landscape, health organizations increasingly consider integrating cloud transformation into their strategic initiatives. Looking ahead to 2026, a substantial shift is expected, with approximately 70% of health centers^[1] adopting a cloud-based approach to enhance their operations. In this context, CitiusTech offers thoughtful guidance for those contemplating or navigating this transformative journey.

Our well-defined assets & utilities are designed to migrate data in a factory model seamlessly. Healthcare organizations can foster business awareness and assess their data estate without disruptions with our platform-agnostic automation-first suite of frameworks, accelerators, and solutions. How? Our recommended framework enables enterprises to streamline data migration and modernize processes through the swift definition, design, and deployment of foundational cloud platforms. Together, we can pioneer a future built on improved care, patient experience, and exciting opportunities. 

References

1. Achieving successful cloud migration in healthcare (cionews.co.in)