What Leaders Should Prioritize in the New Year to Capitalize on New Technology
1. The Challenge: Data Overload, Fragmentation & Interoperability Headaches
In today’s healthcare environment, the volume, variety, and velocity of data being produced and exchanged are skyrocketing. Between traditional EHR (electronic health record) systems, clinical labs, imaging, remote-monitoring devices, telehealth interactions, external referrals, payer data, and unstructured documents (scanned faxes, PDFs, provider notes) — health systems are managing far more data than ever before. Yet for all that data, many organizations still struggle to use it effectively. One of the core obstacles: interoperability.
Fragmented Systems, Data Silos, and Legacy Technology
- Many hospitals, clinics, labs, and other providers operate on disparate systems, often legacy fax software built long before modern data-exchange standards existed. Data also arrives via email and other sources, none of which were designed to communicate with the others, resulting in fragmented data silos.
- Departments or facilities may use entirely different EHR platforms (or combinations of EHR, lab, imaging, ERP, billing, supply-chain, and document-management systems), each using their own proprietary data formats. This issue also can arise during mergers and acquisitions. That makes seamless exchange extremely difficult.
- Data standardization is often minimal or inconsistent. Providers may use different coding systems, terminologies, units of measurement, or data structures — which creates confusion, data loss, or misinterpretation when information is shared.
Data’s Growing Volume and Complexity
- Beyond structured EHR fields, modern healthcare involves unstructured data — free-text clinical notes, scanned documents, faxes, PDFs, images, referral letters, and more. This makes data integration even harder if systems are not built to handle unstructured or semi-structured content.
- As a result, many organizations find themselves with incomplete patient records, duplicated data entry, manual workarounds (e.g. faxing, re-keying data), and increased risk of errors — which affects both care quality and administrative overhead.
Resource Constraints, Skills Gaps & Organizational Friction
- Implementing interoperability isn’t just a technical issue — it’s also organizational. Many providers face limited budgets, insufficient staffing, or lack of in-house IT expertise.
- When interoperability tools are introduced (or required), clinician and staff adoption can be challenging. Required training on new workflows, additional documentation, or unfamiliar interfaces may lead to resistance or workarounds — all of which could undermine the value of the integration.
- Legacy, on-premises systems are often rigid and inflexible. They may heavily rely on outdated architectures that cannot easily adopt modern interoperability protocols, APIs, or scalable storage/compute models.
2. Why On-Premise and Legacy Infrastructure Can Hamstring Modernization — Especially AI & Cloud-Ready Workflows
To understand why cloud migration is more than a “nice to have,” goal, it helps to consider what is lost when an organization remains anchored in on-premises or legacy infrastructure.
Limited Scalability, Flexibility, and Integration Ability
- On-premises systems typically have fixed capacity: storage, computing power, network bandwidth — all constrained by what was bought and installed. As data volumes grow (especially unstructured documents, imaging, external referrals, and new data sources), these systems can quickly require costly expansions such as new servers and ports.
- Legacy systems often lack modern APIs, standard data-exchange protocols, or flexible architectures that can integrate with newer tools (analytics, AI/ML pipelines, cloud-native applications). As a result, adding new capabilities, such as AI-powered document processing or cross-facility data sharing, can be extremely challenging or require costly custom integration.
High Cost and Maintenance Overhead: Diverting Resources from Innovation
- Maintaining legacy infrastructure (servers, data centers, custom integrations, security patches, backups) is resource intensive. Some estimates show high ongoing operating and maintenance costs for legacy systems.
- Much of the IT budget can get consumed in “keeping the lights on,” which leaves limited funds available for innovation such as AI tools, cloud migration, improved workflows, interoperability upgrades, or advanced analytics.
Inhibiting AI, Automation & Modern Document Workflows
- AI-driven applications, including natural language processing (NLP) for clinical notes, automated document ingestion, intelligent routing of referrals, or predictive analytics, require consistent, high-quality data, often from multiple sources (structured EHR data + unstructured documents + external data). Cloud-based platforms can be far more adept at leveraging new technology than legacy, on-premises architecture by removing the barriers of inconsistent data standards and automatically standardizing and normalizing data from any source or format. This allows your AI tools can reliably access, interpret, and process information regardless of the originating system or standard.
- In addition, outdated infrastructure often keeps document-heavy healthcare workflows (referrals, consults, lab orders/results, imaging reports, scans) on paper or in static PDF/fax-based exchange. That means AI tools that rely on digital ingestion or structured data are effectively blocked from being used, limiting automation and making staff do manual data entry or reconciliation. This undermines efficiency gains, data quality, and scalability.
3. Cloud as Strategic Foundation: Top Initiatives For Healthcare Leaders To Prioritize
Given all the challenges above, many healthcare leaders are now seeing cloud migration not merely as an IT upgrade but as foundational to future-facing care delivery, data management, and digital transformation. Below are five of the most important initiatives they are likely to prioritize:
Initiative 1: Migrate Data, Analytics, and AI Workloads to the Cloud
By moving data warehouses, analytics platforms, and AI/ML workloads to the cloud, organizations gain scalable compute and storage capacity. This enables advanced analytics, predictive modeling, and AI-powered document processing — all without being constrained by on-premises hardware. Cloud platforms can handle large-scale, compute-heavy workloads (e.g., NLP, LLMs, imaging AI, large-scale data integration), which are increasingly prevalent in modern healthcare.
Initiative 2: Adopt Hybrid or Multi-Cloud Architectures (“Cloud-Smart” Strategy)
Recognizing that not all workloads are equal, many leaders will adopt a “cloud-smart” strategy as a bridge rather than “cloud-everything” all at once. That means some systems (especially sensitive or legacy-critical ones) may stay on-premise or in a private cloud, while data-heavy, compute-heavy, or new-application workloads go to a public cloud. Hybrid or multi-cloud architectures allow flexibility, compliance control, and gradual transition. This reduces risk while gaining cloud benefits.
Initiative 3: Modernize Infrastructure for Security, Compliance, and Resilience
Migrating to cloud presents an opportunity to build a more secure, robust, and resilient IT infrastructure, one with advanced encryption, identity and access management, audit trails, and disaster-recovery capabilities. For healthcare, where patient data is highly sensitive and regulated, cloud migration can help meet compliance requirements, reduce risk of data loss or downtime, and support business continuity more effectively.
Initiative 4: Move Critical Operational Systems (EHR modules, ERP, Supply-Chain, Document & Fax Workflows) to Cloud-Enabled Platforms
Cloud migration isn’t only about data warehousing or analytics. Many organizations also will prioritize shifting core operational systems. That includes EHR modules, revenue-cycle/billing systems, and other crucial document and fax workflows. By moving these to cloud-native or cloud-hosted environments, organizations can enable seamless integrations, interoperability, real-time data exchange, and future-ready document handling — reducing reliance on outdated fax machines, manual processes, or isolated paper-based workflows.
Initiative 5: Enable Digital Health Innovation — Telehealth, Remote Monitoring, Virtual Care & Distributed Workflows
Cloud provides the underlying infrastructure to support modern digital-health models: telemedicine, remote patient monitoring, home-based care, virtual consults, cross-facility coordination, collaborative care networks, and more. As demand increases for patient-centric, distributed, and decentralized care, cloud-based infrastructure can be essential. Moreover, cloud enables real-time data sharing, longitudinal patient records across settings, scalable storage, and the integration of diverse data sources (EHR, lab, imaging, IoT devices, documents) — making whole-person care easier to deliver and coordinate.
4. Why These Priorities Matter — The Value of Cloud-Based Infrastructure for Healthcare’s Future
Unlocking the Potential of AI and Automation
By migrating data and document workflows to the cloud, healthcare organizations can unlock AI-driven automation for tasks like document ingestion, clinical note parsing, referral management, coding, prior authorization, and even population-health analytics. Cloud makes it easier to apply scalable AI models across large datasets, integrate data from multiple sources, and manage unstructured data — something nearly impossible with fragmented, on-premises systems.
Breaking Down Data Silos and Enabling Interoperability
Cloud-based systems (especially those built with modern data-exchange standards) can help break down data silos. When data is centralized (or federated but integrated), and accessible through APIs or standardized interfaces, providers across departments, facilities, and care settings can more easily share patient information. That improves care coordination, reduces duplication (e.g., duplicate tests), reduces errors, and supports more comprehensive, and efficient, longitudinal care.
Improving Operational Efficiency and Reducing Cost Over Time
While cloud migration requires investment — both technological and organizational — over time it can reduce the burden of maintaining multiple legacy systems, expensive hardware, and complex custom integrations. Cloud infrastructure can reduce total cost of ownership, simplify maintenance, and free up IT resources to focus on innovation rather than patching, upgrading, and maintaining outdated systems.
Enabling Scalability, Flexibility, and Future-Proofing
Healthcare needs are evolving rapidly: more data, more care models (remote, virtual, hybrid), regulatory changes, patient expectations, new technologies (AI, IoT, wearables), collaboration across providers, payers, and partners. Cloud infrastructure offers the scalability and flexibility to support those changes without requiring repeated heavy investments in hardware or custom infrastructure. This will make organizations more agile and future-ready.
Supporting Better Patient Care, Coordination, and Experience
Ultimately, the promise of cloud migration isn’t just technological — it’s about better care. When data flows smoothly across systems, when care teams have access to complete, up-to-date patient records, when operations (billing, referrals, scheduling) run efficiently, and when digital tools support care rather than hinder it — patients receive more coordinated, timely, and effective care. Cloud provides the architecture to make that possible.
5. The Considerations — Why Cloud Migration Needs Strategy, Governance, and Careful Execution
- Security, Compliance & Regulatory Risk: Patient data is highly sensitive, and regulations such as HIPAA demand strict controls around data privacy, access, consent, audit, and breach handling. Cloud migration requires strong governance, encryption, identity management, role-based access controls, and compliance processes.
- Cost, Disruption, and Complexity of Migration: Moving decades of legacy data (such as EHRs, documents, imaging, and billing records) to the cloud requires thoughtful planning, as it may involve costs, time investment, and managing differences in data quality, formatting, and standardization. While some service interruptions or workflow adjustments may occur, these challenges can be addressed with careful execution.
- Organizational Resistance, Skills Gaps, and Change Management: IT staff and clinicians may lack cloud experience; integration requires new skills and processes. Clinician and staff adoption may lag if workflows are disrupted or tools are hard to use.
- Interoperability Doesn’t Automagically Arise: Simply moving to the cloud doesn’t guarantee interoperability. Organizations still need to adopt shared data standards (such as FHIR and HL7), implement governance for data exchange, and ensure consistent data quality and structure. If this doesn’t happen, data silos merely shift location.
- Balancing Legacy Needs and Innovation — The Case for Hybrid Approaches: Some systems (especially legacy, highly customized, or critical operational systems) may not be appropriate for cloud migration — or may require extensive rework. A hybrid approach (on-premise + cloud, or private + public cloud) is often the most realistic path.
6. What This Means for Healthcare Leaders — Strategic Recommendations
- Treat cloud migration as a strategic foundation, not a point project. View it as core infrastructure enabling future digital health, not just as an IT cost-saving or modernization effort.
- Prioritize migration of data/analytics workloads, document workflows, and any systems that benefit from scalability or real-time integration — especially those that support AI and automation. These will yield high value (efficiency, data insights, scalability) and set the stage for future innovation.
- Adopt a hybrid or “cloud-smart” approach. Not sure if you’re ready for a full cloud migration? A hybrid setup is a good steppingstone. Migrate workloads to the cloud that benefit most and do so incrementally while maintaining an on-premises presence until it’s no longer needed.
- Invest in governance, data standardization, and interoperability protocols (e.g. APIs, shared data models, standard terminologies). Without these, cloud simply relocates silos rather than dissolving them.
- Plan for change management, training, and stakeholder buy-in. Ensure IT staff, clinicians, and administrative teams are part of the process. Provide training and support so new workflows are adopted effectively, and data is used correctly.
- Leverage cloud migration as an enabler for AI-based innovation, document automation, remote care, and future-ready workflows. The real payoff comes from what cloud infrastructure enables — better data use, efficiency, patient care — not just the move itself.
7. Conclusion
Healthcare is at an inflection point. The sheer volume and complexity of data, from EHRs to imaging to referrals to unstructured documents, combined with the rising demand for digital health, remote care, AI-driven workflows, and better interoperability, mean that legacy, on-premises infrastructure is increasingly insufficient.
For healthcare leaders, migrating IT operations to the cloud is not just an upgrade — it’s a strategic necessity if they want to modernize care delivery, leverage AI, reduce administrative burden, and build scalable, resilient, and interoperable systems. That said, this migration must be done thoughtfully with strong governance, data standardization, hybrid approaches where needed, and organizational change management.
In doing so, health systems can transform fragmented, siloed, legacy-bound IT into a modern, cloud-enabled foundation for the future — one that supports better patient care, data-driven insights, operational efficiency, and innovation.