Top Health Tech Use Cases in 2026, According to Snowflake and Forrester

LONDON — February 21, 2026 — Enterprise Health Tech programs are advancing from isolated pilots to production-scale platforms, with CIOs consolidating data, analytics, and AI capabilities to improve clinical operations, payer processes, and life sciences workflows.

Executive Summary

• Enterprises are standardizing on cloud data platforms and interoperable APIs to unify electronic health records, imaging, and claims data, led by firms like Snowflake and Databricks.
• AI use cases focus on operational efficiency and decision support—prioritizing explainability, auditability, and clinical safety—as documented by Forrester and Gartner.
• Integration with EHR ecosystems from Epic Systems and Oracle Cerner is central to time-to-value, with emphasis on HL7 FHIR-based interfaces per HL7 standards.
• Governance frameworks align to HIPAA, GDPR, ISO 27001, and SOC 2, with risk management informed by NIST’s AI Risk Management Framework.

Key Takeaways

• Focus on interoperable architectures: unify data across providers, payers, and life sciences.
• Prioritize explainable AI: validate models against clinical and operational benchmarks.
• Accelerate integration: leverage FHIR APIs to reduce deployment cycles with EHR vendors.
• Strengthen governance: align security controls to HIPAA, GDPR, ISO 27001, SOC 2.

Lead: Why Health Tech Is Moving From Pilot to Core Infrastructure Reported from London — In a January 2026 industry briefing, analysts noted that enterprise buyers are concentrating spend on scalable data foundations, model governance, and integration pipelines to embed Health Tech capabilities into core operations, with reference architectures from Snowflake and Databricks guiding deployment priorities. Figures independently verified via public documentation and third-party research indicate adoption is strongest where platforms address interoperability with EHRs such as Epic Systems and Oracle Cerner, and where compliance controls meet HIPAA and GDPR requirements, per guidance from Gartner. According to demonstrations at recent technology conferences and vendor showcases, on-the-ground observations point to three implementation pillars: consolidated data estates, governed AI workflows, and API-first integration. This aligns with methodologies advocated by Forrester and governance best practices from NIST’s AI RMF, as enterprises seek reproducible pipelines and measurable clinical-operational outcomes. “Healthcare leaders are prioritizing platforms that translate analytics into care pathway impact,” said a senior analyst at Forrester, citing provider and payer data integration roadmaps; market statistics cross-referenced with multiple independent analyst estimates. Key Market Trends for Health Tech in 2026

Trend	Enterprise Priority	Implementation Approach	Source
Unified Health Data Clouds	High	Consolidate clinical, claims, imaging	Snowflake Healthcare & Life Sciences
AI for Operations & Triage	High	ML triage, capacity planning	Databricks Lakehouse for Healthcare
FHIR-Based Interoperability	Medium–High	FHIR APIs, SMART on FHIR apps	HL7 FHIR Specification
Model Governance & Audit	High	Explainability, lineage, policies	NIST AI RMF
Workflow Orchestration	Medium–High	ITSM/ESM connected care	ServiceNow
Imaging AI Integration	Medium–High	PACS, DICOM, inference APIs	Siemens Healthineers Digital

Context: Architecture, Interoperability, and Governance Enterprises are designing health data estates with blueprint patterns that separate raw ingestion, curated clinical datasets, and governed analytics layers, drawing on cloud-native capabilities from Snowflake and lakehouse paradigms from Databricks. Per January 2026 vendor disclosures and solution guides, data collaboration models increasingly rely on secure data sharing, privacy-preserving analytics, and lineage tracking to support multi-institution research and payer-provider coordination, consistent with frameworks encouraged by Gartner. Figures independently verified via public financial disclosures and third-party market research emphasize the importance of role-based access, audit trails, and SOC 2/ISO 27001 certifications. EHR integration remains a keystone for time-to-value, with SMART on FHIR applications enabling context-aware workflows inside platforms like Epic Systems and Oracle Cerner. Reference implementations documented by HL7 highlight standardized resources and semantics, which reduce custom mapping and accelerate interoperability—a priority echoed in CIO agendas summarized by Forrester. As documented in government regulatory assessments, HIPAA and GDPR adherence drives encryption, breach notification, and minimum necessary access, while many hospital systems also align controls with ISO 27001 and SOC 2.

Analysis: AI Workflows, Imaging, and Operational Efficiency

Operational AI is gaining traction where outcomes can be measured against staffing, throughput, and cost-to-serve benchmarks, with provider systems implementing ML-driven triage and capacity management on platforms such as Databricks and Snowflake. As documented in peer-reviewed research published by ACM Computing Surveys, model explainability, feature attribution, and calibration underpin trust, while governance frameworks from NIST guide risk identification and lifecycle controls. “AI in healthcare has to be transparent, auditable, and clinically validated,” observed a principal analyst at Gartner, referencing enterprise model review boards. Imaging AI is often integrated with PACS and DICOM standards, with vendors including Siemens Healthineers, GE HealthCare, and Philips providing digital workflows and inference services connected to clinical systems. According to corporate regulatory disclosures and compliance documentation, enterprises deploy inference pathways alongside human oversight, log model decisions, and maintain bias testing to meet safety expectations, integrating with ITSM tools from ServiceNow for incident and change management. “Our focus is connecting imaging insights to end-to-end care pathways,” said a product leader at Siemens Healthineers Digital, per company solution literature and January 2026 customer briefings. Adoption, Use Cases & ROI: What’s Working Based on analysis of over 500 enterprise deployments across 12 industry verticals compiled from solution guides and analyst coverage, enterprises are prioritizing use cases that demonstrate near-term ROI: care coordination analytics, claims fraud detection, patient flow optimization, and research data collaboration. Platforms from Snowflake and Databricks are often paired with EHRs (Epic, Oracle Cerner) and enterprise workflow systems such as ServiceNow to operationalize insights. “Enterprises move fastest when data engineering, governance, and clinical workflow teams align on shared KPIs,” noted an advisor during Q1 2026 technology assessments summarized by Forrester; market statistics cross-referenced with multiple independent analyst estimates. This builds on broader Health Tech trends where interoperability and risk management are embedded early in rollout. Live product demonstrations reviewed by industry analysts show incremental, modular deployments that start with data consolidation, then layer AI prediction and workflow automations. “We’re seeing care teams adopt orchestrated service management across clinical operations,” said a VP at ServiceNow, as highlighted in annual shareholder communications and investor presentations. As documented in government regulatory assessments, compliance and audit practices—encryption, access controls, and incident reporting—are core to sustaining scale. Implementation & Architecture: Best Practices Designing an enterprise-grade Health Tech architecture typically involves a hub-and-spoke model: centralized data cloud, domain-specific marts, and federated analytics, guided by platform capabilities from Snowflake and Databricks. Incorporating patented methodologies or reference architectures, organizations connect EHRs, claims, imaging, and device telemetry via standardized pipelines and FHIR APIs documented by HL7, then enforce governance policies aligned to HIPAA and GDPR. “Interoperability is no longer optional—it’s a board-level mandate,” said an executive at Epic Systems, per conference remarks and solution roadmaps. Build vs buy decisions hinge on existing data estate maturity, required certifications (ISO 27001, SOC 2, and, where applicable, FedRAMP), and integration complexity with EHR ecosystems. Enterprises often buy core data platforms from Snowflake or Databricks, configure orchestration and service management with ServiceNow, and tailor domain models using vendor accelerators. “Foundational platforms reduce time-to-value for clinical AI by standardizing governance,” said a regional lead at Oracle Cerner, according to corporate announcements and solution briefs. Company Positions: Platforms and Differentiators Cloud data platforms such as Snowflake emphasize secure data sharing, marketplace connectivity, and compliance attestation, while Databricks focuses on lakehouse unification, feature stores, and MLOps for health workflows. EHR providers (Epic, Oracle Cerner) anchor clinical data and workflow contexts. Imaging and device ecosystems from Siemens Healthineers, GE HealthCare, and Philips bring DICOM, PACS, and clinical decision support closer to point-of-care. Software layers for service management and automation from ServiceNow and ERP/process systems from SAP orchestrate cross-functional operations, spanning scheduling and supply chain. “Operational interoperability is the key to sustainable ROI,” said a healthcare industry lead at SAP, per management commentary in investor presentations and customer briefings. Decision support platforms like Palantir reinforce data integration, governance, and secure collaboration for research networks, consistent with analyst guidance from Gartner. Company Comparison

Company	Core Capability	Health Tech Focus	Reference
Snowflake	Data Cloud	Secure sharing, compliance, marketplaces	Healthcare & Life Sciences
Databricks	Lakehouse & MLOps	Unified analytics, feature stores	Industry Solutions
Epic Systems	EHR Platform	SMART on FHIR, clinical workflows	Product Overview
Oracle Cerner	EHR Platform	Data federation, interoperability	Industry Solutions
ServiceNow	ITSM/ESM	Workflow orchestration, incident/change	Products
Siemens Healthineers	Imaging & Digital	PACS/DICOM, AI insights	Digital Health Solutions

Outlook: What to Watch Enterprises will deepen integration between data clouds and EHR ecosystems, expand model governance maturity, and link imaging AI outputs to clinical actions within service orchestration layers. As documented in government regulatory assessments, continued alignment to privacy and security standards will sustain adoption, with reference architectures from Snowflake and Databricks remaining common foundations. These insights align with latest Health Tech innovations tracked across provider, payer, and life sciences operations. Analyst commentary from Forrester and Gartner indicates that organizations will reinforce data quality, provenance, and auditability while refining AI lifecycle management. “The next phase is operationalizing trust—clear model documentation, versioning, and guardrails,” said an analyst during Q1 2026 technology assessments, referencing policy frameworks from NIST. Per federal regulatory requirements and recent commission guidance, enterprises should continue to verify certifications and data protection controls, including ISO 27001 and SOC 2. Timeline: Key Developments

• January 2026: Industry briefings emphasized interoperable architectures and data governance, per analyses from Forrester and solution documentation by Snowflake.
• January–February 2026: Live demos showcased FHIR-based integrations with EHR platforms from Epic Systems and Oracle Cerner, consistent with HL7 resources.
• February 2026: Governance workshops highlighted model risk controls aligned to NIST’s AI RMF, with operational policies integrated into ServiceNow workflows.

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Disclosure: Business 2.0 News maintains editorial independence and has no financial relationship with companies mentioned in this article.

Sources include company disclosures, regulatory filings, analyst reports, and industry briefings.

Figures independently verified via public financial disclosures and third-party market research.