How Healthcare Organizations Can Use Data Without Increasing Risk
Healthcare organizations are under sustained pressure to improve care quality and operational efficiency while complying with strict regulatory requirements.
According to the OECD, healthcare spending accounts for roughly 9 to 10 percent of GDP across developed economies, with administrative costs representing a significant and growing share of total expenditure. In the United States, administrative costs alone are estimated at 25 to 30 percent of total healthcare spending according to research published in Health Affairs.
At the same time, healthcare data volumes are growing rapidly. A study cited by IDC estimates that healthcare data grows at a compound annual rate above 30 percent, driven by medical imaging, electronic health records, and connected medical devices.
Yet most healthcare organizations struggle to turn this data into timely, reliable decisions.
Why Centralized Data Architectures Fail in Healthcare
Healthcare data is highly fragmented. Clinical data, imaging data, laboratory results, billing systems, and operational platforms are typically delivered by different vendors and deployed as isolated systems.
The Office of the National Coordinator for Health Information Technology (ONC) has repeatedly identified lack of interoperability as a systemic barrier to data-driven healthcare. Centralized data warehouse and lake initiatives often fail because they require extensive data duplication, complex integration, and long delivery timelines.
Research by McKinsey shows that large healthcare data integration programs frequently take multiple years to deliver value, while increasing compliance exposure due to the movement and replication of sensitive patient data.
These limitations became highly visible during the COVID-19 pandemic. Reports from the U.S. Government Accountability Office (GAO) and the CDC documented delays in data availability, inconsistent reporting across jurisdictions, and limited cross-system visibility during peak demand periods.
A Federated Approach Aligned With Healthcare Constraints
A federated data approach enables analytics and AI across distributed healthcare systems without requiring data centralization.
Instead of moving data into a single platform, computation is executed where the data already resides. Only authorized results are shared across systems.
This model directly aligns with regulatory principles such as:
- Data minimization under GDPR
- HIPAA requirements for access control and auditability
- National data residency laws for health data
The Brookings Institution has highlighted federated and decentralized analytics as a viable strategy for regulated sectors where data movement is constrained, including healthcare and public health systems.
What This Enables for Healthcare Decision Makers
From a buyer perspective, the value is operational, not architectural.
A federated data platform supports:
- Cross-department analytics without duplicating patient data
- Faster operational reporting for admissions, capacity, and utilization
- Secure collaboration across hospitals, research institutions, and partners
- Analytics and AI initiatives that comply with privacy and residency requirements
According to HIMSS Analytics, organizations with higher data interoperability and analytics maturity demonstrate:
- Faster clinical decision support adoption
- Lower reporting latency
- Improved ability to respond to operational stress events
These improvements are not achieved by replacing core systems, but by enabling controlled access across them.
Where Scalytics Fits
Scalytics Federated is designed for environments where healthcare data cannot be centralized.
It connects to existing clinical, operational, and analytical platforms and enables distributed analytics, federated learning, and AI workloads across hospitals, data centers, and cloud environments.
This allows healthcare organizations to:
- Use sensitive data without moving it
- Apply analytics and AI consistently across systems
- Enforce security and governance policies centrally
- Scale analytics without increasing regulatory exposure
Scalytics operates above existing systems and does not require replacing electronic health records, imaging platforms, or operational software.
When a Federated Model Is the Right Choice
A federated approach is relevant when:
- Data movement is restricted by regulation or policy
- Multiple organizations or departments must collaborate
- Centralized analytics initiatives have stalled or failed
- Compliance and security teams limit data replication
Federated data management is not a shortcut. Governance, data quality, and organizational readiness remain critical. However, it provides a realistic path forward in healthcare environments where traditional architectures are no longer viable.
Key Takeaway for Healthcare Buyers
Healthcare data challenges are structural, not technical.
Industry research, regulatory guidance, and recent crisis response all point to the same conclusion: centralized data architectures struggle in regulated, distributed healthcare environments.
Federated data management enables analytics, AI, and collaboration without increasing compliance risk or operational disruption.
That is the problem Scalytics Federated is built to solve.
Research and Sources
OECD Health Statistics – Healthcare spending share of GDP and system efficiency indicators: https://www.oecd.org/en/topics/health/health-data.html
Health Affairs – Administrative costs in the U.S. health care system: https://www.healthaffairs.org/doi/10.1377/hlthaff.2019.00107
IDC – The Digitization of the World and healthcare data growth: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
Office of the National Coordinator for Health IT (ONC) – Interoperability and information blocking reports: https://www.healthit.gov/data/data-briefs | https://www.healthit.gov/topic/interoperability
McKinsey & Company – Data transformation and analytics in healthcare: https://www.mckinsey.com/industries/healthcare/our-insights
U.S. Government Accountability Office (GAO) – COVID-19 data and public health reporting challenges: https://www.gao.gov/covid-19
Centers for Disease Control and Prevention (CDC) – Public Health Data Modernization Initiative: https://www.cdc.gov/surveillance/data-modernization/index.html
Brookings Institution – Data governance, privacy, and analytics in regulated environments: https://www.brookings.edu/topics/data-governance/
HIMSS Analytics – Interoperability and data analytics maturity models: https://www.himssanalytics.org/ | https://www.himss.org/resources/interoperability