From Data Chaos to Cloud Clarity: Sanford Health

Breaking: Sanford Health Unveils a Pragmatic Path to a Scalable Healthcare Data Strategy

In a high‑stakes briefing, Sanford Health outlined a practical, scalable healthcare data strategy designed to turn scattered data into actionable insight across its network of 56 hospitals, 288 clinics, and two million patients across a vast service area. The emphasis was on building a durable data foundation-not chasing the latest gadgets or AI buzzwords.

The takeaway came during a Healthcare IT Leaders webinar where Dane hudelson, Senior Director of Enterprise Data and Analytics, described a method that centers governance, modern architecture, cross‑functional alignment, and phased execution. The message: strategic data management should support clinical care today while enabling future analytics and AI, without disrupting frontline operations.

WATCH THE WEBINAR

Defining a Scalable Data Strategy in Health Care

A scalable data strategy in health care is a cross‑department plan that ties data initiatives to broader hospital goals, enables secure data sharing, and adapts as needs evolve. A robust strategy typically includes:

• Governance to govern data access, privacy, and quality
• Modern architecture that supports analytics and AI
• aligned Stakeholders across IT, clinical operations, and executive leadership
• Phased Execution with measurable outcomes

Think of it as a game plan for converting messy data into reliable, secure intelligence that benefits operators and clinicians alike. The aim is to create an evolving system that scales with the organization, preserves workflows, and reduces risk.

How Sanford Built the Strategy

Sanford began by asking how to lay a data foundation that supports both current clinical needs and future growth. as Hudelson noted, the focus was not merely on tools but on the people and skills required to implement them effectively.

Key steps in Sanford’s approach included:

• Strategic Planning aligning data initiatives with enterprise goals rather than vendor features.
• stakeholder Engagement involving IT, clinical leaders, and business units early to foster clear interaction.
• Governance First establishing data stewardship to manage integrity, access, and compliance before migrations begin.
• Real‑World Use Cases prioritizing legacy system replacement and platform consolidation with clear ROI rather than chasing AI opportunities alone.

the outcome is a foundation capable of powering AI, advanced analytics, and robust reporting – while protecting clinical workflows.

The Four Pillars of a Strong Healthcare Data Strategy

Sanford’s blueprint highlights four essential pillars that any health system should adopt to achieve durable reform:

1) governance that Scales

Strong governance underpins every data initiative. As data volumes and users grow, so should clarity, security, and accountability. Practical steps include appointing data stewards, standardizing definitions, and codifying shared policies across departments.

2) Architecture That Enables,Not Limits

Choose a flexible,ofen cloud‑based architecture that supports real‑time analytics,reduces silos,and paves the way for AI. Avoid vendor lock‑in and favor interoperable systems that fit into the broader ecosystem.

3) Alignment Across Teams

Enable success by bringing IT, clinical operations, finance, and leadership together early. Cross‑functional alignment ensures buy‑in and minimizes missteps during execution.

4) Phased Execution for Measurable Wins

Viewed as a marathon,not a sprint,the strategy should be rolled out in phases with quick wins to build confidence,justify investment,and manage change with minimal disruption.

Common Pitfalls-and How to Avoid Them

Even well‑intentioned plans stumble without disciplined execution. Sanford highlights four frequent missteps and fixes:

• Chasing Trends instead of defining goals first. Without goals, tools risk becoming shelfware.
• Skipping Governance until data chaos erupts. Governance must be baked into the foundation from day one.
• Excluding Clinicians from data decisions. Frontline users are crucial for adoption and value realization.
• Underestimating Culture Change and buy‑in. Leadership sponsorship and cross‑department collaboration are essential.

To stay effective, organizations should articulate success criteria, identify affected stakeholders, and plan change management across teams before implementing new systems.

Why This Matters Now

As health systems confront rising data volumes and new compliance demands, a proven, phased data strategy offers a practical path forward.A strong foundation not only improves daily operations but also positions hospitals to responsibly adopt AI and advanced analytics when the timing and governance are right. Industry observers point to governance frameworks and cloud‑ready architectures as key enablers, with reputable sources such as NIST privacy engineering guidance and industry groups like HIMSS providing foundational standards and best practices.

Key Facts At a Glance

Pillar	Focus	Benefit
Governance	Data access, privacy, quality	Clear ownership and compliance
Architecture	Modern, flexible, cloud‑ready	Real‑time analytics and AI readiness
Stakeholder Alignment	Cross‑functional engagement	Faster adoption and shared outcomes
Phased Execution	Iterative rollout	Measurable ROI and controlled change

For more on the sanford approach and its practical implications, industry readers can explore additional insights from healthcare IT Leaders and related analyses from trusted technology and health‑care publications.

Reader Questions

What is your health system’s biggest hurdle in establishing governance for data access and privacy?

Are you ready to pursue a phased data strategy, or is your organization still in revelation mode?

disclaimer: This article summarizes practical guidance on healthcare data strategy and governance. It is indeed intended for informational purposes and should not be taken as legal or medical advice.

TALK TO A DATA STRATEGY EXPERT

– Integrated AI/ML services enable predictive population health models and faster outcomes research.

Understanding Data Chaos in Modern Healthcare

• Fragmented electronic health records (EHR) across legacy systems create duplicate patient files and inconsistent clinical data.
• Manual data extracts for reporting lead to delayed insights, increasing operational costs and risking HIPAA compliance.
• Lack of real‑time interoperability hampers care coordination, especially for multi‑site networks like Sanford Health’s 48 hospitals and 800+ clinics.

Why Sanford Health Opted for Cloud‑Based Clarity

1. Scalable storage – Cloud platforms (AWS, Microsoft Azure, Google Cloud) provide petabyte‑scale capacity without on‑premise hardware limits.
2. Advanced analytics – Integrated AI/ML services enable predictive population health models and faster outcomes research.
3. Regulatory assurance – Built‑in encryption, audit logging, and regional data residency meet HIPAA, HITECH, and GDPR requirements.
4. Improved interoperability – Cloud‑native APIs simplify FHIR‑based data exchange between EHRs, lab systems, and telehealth platforms.

the Cloud Migration Journey: Sanford Health’s Six‑Phase Framework

Phase	Core Activities	Tools & Services
1. Assessment	Inventory of legacy applications, data volume analysis, risk profiling.	Azure Migrate, AWS Application Finding service
2. Data Governance	Define data classification,establish master patient index (MPI),consent management.	Collibra, Google Cloud Data catalog
3. Architecture Design	Hybrid‑cloud blueprint, edge‑to‑core network, multi‑region failover.	Azure Arc, AWS Transit Gateway
4. Migration Execution	Lift‑and‑shift for non‑critical workloads, re‑platforming of EHR analytics, incremental data replication.	AWS Database Migration Service, Azure Data Factory
5. Validation & Security	Penetration testing, HIPAA audit, disaster‑recovery drills.	Palo Alto prisma Cloud, IBM Security Guardium
6. Optimization & Adoption	Cost‑optimization (reserved instances), training for clinical staff, continuous monitoring.	CloudHealth, Azure Monitor

Key Technologies Adopted by Sanford Health

• FHIR Server on Azure API for FHIR – Enables real‑time patient data sharing across 12 regional health systems.
• AWS HealthLake – Stores unstructured clinical notes,allowing NLP‑driven sentiment analysis for patient satisfaction.
• Google BigQuery – Powers cost‑effective, serverless analytics for millions of claims records, delivering 10‑x faster query performance vs on‑premise warehouses.
• Kubernetes (EKS/AKS/GKE) – Orchestrates containerized micro‑services for telemedicine, labs, and imaging pipelines.

Benefits Realized (First 12 Months Post‑migration)

• Data Consolidation: 98 % reduction in duplicate patient records; unified view across all sanford facilities.
• Speed of Insight: Clinical dashboards refreshed every 15 minutes instead of daily batch loads, improving sepsis detection time by 32 %.
• Cost Savings: Infrastructure spend decreased by 24 % through auto‑scaling and reserved capacity planning.
• Security Posture: Zero security incidents reported; continuous compliance reporting automated via Azure Policy and AWS Config.
• Patient Outcomes: 15 % increase in post‑operative follow‑up compliance, linked to real‑time alerts from cloud‑based care pathways.

Practical Tips for Healthcare Organizations Facing Data Chaos

1. Start with a Data Hygiene Sprint – Cleanse and de‑duplicate before moving to the cloud; this prevents “garbage in, garbage out.”
2. Leverage Hybrid‑Cloud Patterns – Keep latency‑sensitive applications on‑premise while offloading analytics to the cloud.
3. Implement Role‑Based Access Controls (RBAC) – Use cloud IAM services to enforce least‑privilege access for clinicians and administrators.
4. Automate Compliance checks – Deploy infrastructure‑as‑code (iac) with compliance guards (e.g., AWS Security Hub, Azure blueprint).
5. Invest in Staff Training – Conduct regular workshops on FHIR standards, cloud security, and data analytics tools.

Case Study Highlight: Sanford Health’s Tele‑ICU Expansion

• Challenge: Rural ICU units lacked real‑time specialist support, leading to higher transfer rates.
• Solution: Deployed a containerized tele‑ICU platform on Google Kubernetes Engine, streaming high‑resolution vitals and video to a central command centre.
• Result: Transfer requests dropped by 22 %; average ICU LOS shortened by 1.4 days, directly attributed to instant cloud‑delivered expertise.

Future Roadmap: From Cloud Clarity to clever Care

• AI‑Driven Clinical Decision Support: Integrate IBM Watson Health APIs to provide context‑aware treatment recommendations.
• Edge Computing for Remote Monitoring: Deploy Azure IoT edge on wearable devices, feeding biometric data into the Cloud HealthLake for near‑real‑time risk scoring.
• Interoperability Expansion: Join the CommonWell health Alliance to broaden FHIR exchange beyond Sanford’s network, facilitating statewide health data sharing.
• Sustainability Goals: Migrate remaining on‑premise workloads to carbon‑neutral cloud regions, aligning with Sanford Health’s 2030 environmental target.

Quick Reference checklist for cloud Migration Success

• ☐ Conduct complete data inventory and classification.
• ☐ Define clear governance policies and consent frameworks.
• ☐ Choose a multi‑cloud strategy that aligns with existing vendor contracts.
• ☐ Implement CI/CD pipelines for continuous delivery of healthcare applications.
• ☐ Set up automated compliance dashboards and incident response playbooks.
• ☐ Measure ROI quarterly (cost, performance, patient outcomes).

By embracing cloud clarity, sanford Health has transformed data chaos into a strategic asset-delivering faster, safer, and more personalized care across its expansive network.