Top Health Tech Priorities for 2026, According to McKinsey, Deloitte and Samsung

LONDON — March 11, 2026 — Enterprises are centering 2026 health tech strategies on interoperable data platforms, clinically grounded AI, and outcomes-focused virtual care models, according to enterprise advisory analyses from McKinsey and Deloitte, alongside device ecosystem signals from Samsung.

Executive Summary

• Data platforms, EHR interoperability, and governed AI feature prominently in 2026 enterprise health tech roadmaps, per guidance from McKinsey and Deloitte.
• Provider and payer buyers emphasize workflow integration across Epic and Oracle Health alongside analytics cores from Snowflake and Databricks.
• Virtual care, remote monitoring, and wearables are shifting toward outcomes-based models, with ecosystems from Samsung, Apple, and clinical platforms like Teladoc Health.
• Compliance guardrails—HIPAA, GDPR, ISO 27001, SOC 2—and HL7 FHIR interoperability are baseline requirements, informed by guidance from HHS, GDPR, ISO, and HL7.

Key Takeaways

• Enterprises prioritize data liquidity, AI assurance, and workflow integration across EHRs and analytics platforms; buyers favor proven interoperability and governance, per Gartner research framing.
• Implementation success correlates with clinical safety cases, FHIR-first integration, and continuous validation of models and devices, according to WHO digital health guidance.
• Security posture centers on zero trust, least-privilege access, and continuous compliance monitoring, drawing from NIST SP 800-207 and SOC 2 controls.
• Ecosystem partnerships are decisive: providers pair EHR incumbents like Epic with cloud data stacks from Snowflake or Databricks for scalable analytics and AI.

Lead: What’s Driving 2026 Health Tech Priorities Reported from London — In a Q1 2026 technology assessment, enterprise advisors emphasized interoperability, data platform consolidation, and responsible AI as the near-term levers for clinical and operational ROI, reflecting persistent themes in McKinsey healthcare analyses and Deloitte health care briefings. Buyers continue to focus on streamlining clinical workflows across entrenched records systems from Epic and Oracle Health while pairing these with cloud-scale analytics cores from Snowflake or Databricks to unlock longitudinal insights. According to demonstrations at industry forums and provider showcases, remote monitoring and virtual care are evolving toward outcomes-based, risk-sharing models, supported by device ecosystems from Samsung and Apple, as well as end-to-end care platforms from Teladoc Health. Alignment with unbiased clinical evidence and clear reimbursement pathways remains crucial, which is consistent with guidelines published by the FDA Digital Health Center of Excellence and international best practices cataloged by the WHO. Per January 2026 vendor disclosures, buyers are seeking modular architectures that support HL7 FHIR-native data flows, model monitoring, and policy-driven data access aligned with HIPAA, GDPR, and SOC 2, a baseline indicated by HHS HIPAA frameworks, GDPR obligations, and AICPA guidance. The directional trend is to consolidate vendor portfolios while introducing targeted innovations from analytics and AI partners such as Palantir Foundry for Health where use cases warrant. Key Market Trends for Health Tech in 2026

Priority Area	Enterprise Focus	Adoption Signal	Reference
Interoperable Data Platforms	FHIR-first EHR integration and longitudinal analytics	High	HL7 FHIR, McKinsey
Clinical-Grade AI	Governed model lifecycle and bias mitigation	Rising	Stanford HAI, FDA
Virtual Care & RPM	Outcomes-based reimbursement alignment	Rising	Teladoc Health, WHO
Security & Compliance	Zero trust, SOC 2, ISO 27001 controls	High	NIST ZTA, ISO 27001
Clinician Workflow	Embedded decision support in EHR	High	Epic, Oracle Health
Data Partnerships	De-identified cohorts and RWE	Moderate	Snowflake, Databricks

Context: Market Structure and Interoperability Imperatives At the core of 2026 enterprise strategy is data liquidity—connecting EHRs, claims, imaging, and device telemetry into governed platforms—aligned with global interoperability frameworks such as HL7 FHIR and national policies curated by the U.S. ONC. Providers balancing entrenched systems like Epic with cloud-native data fabrics from Snowflake or Databricks are setting the baseline for scalable analytics and model deployment. Imaging and diagnostics remain pivotal, with modality and informatics portfolios from Siemens Healthineers, Philips, and GE HealthCare enabling multimodal data integration and AI-aided workflows. International policy frameworks from the WHO and national regulators such as the FDA increasingly emphasize clinical evidence, safety case documentation, and post-market surveillance of digital tools. Patient engagement and remote monitoring are shaped by multi-device ecosystems from Samsung and Apple, alongside virtual care providers like Teladoc Health. Cloud services targeted at healthcare, including AWS HealthLake, complement on-prem and hybrid deployments as enterprises adapt workloads to regulatory and latency constraints.

Analysis: AI, Data Platforms, and Governance for Real-World ROI

From rules-based triage to probabilistic decision support, AI is moving into operational workflows only where governance and safety are explicit, reflecting maturity models in Gartner’s industry guidance and academic scrutiny by Stanford HAI. Enterprise buyers are layering model monitoring, dataset lineage, and drift detection atop EHR-integrated workflows supported by Epic and Oracle Health, while centralizing analytics on Snowflake or Databricks with compliant data zones. “AI in clinical contexts must be demonstrably safe, fair, and accountable; we see demand focusing on transparent validation and continuous monitoring,” said a senior partner at McKinsey, echoing themes in enterprise transformation programs. This aligns with risk guidance from the NIST zero trust architecture and healthcare compliance mandates managed under ISO 27001 and SOC 2 control frameworks. “Provider organizations ask for measurable impact on throughput and quality, not pilots for their own sake,” said an executive leader at Siemens Healthineers, consistent with case studies that emphasize radiology reading efficiency and care pathway optimization. Academic literature continues to assess real-world efficacy and bias in models, with methodological depth in venues like ACM Computing Surveys and IEEE Transactions on Cloud Computing informing enterprise diligence. “Interoperability is the non-negotiable; FHIR-native APIs reduce integration debt and accelerate time-to-value,” noted a senior analyst at Forrester. This builds on broader Health Tech trends, where first-mile data quality and last-mile workflow embedding determine adoption and outcomes. Company Positions: Platforms, Capabilities, and Differentiators EHR and clinical systems: Epic and Oracle Health anchor core workflows and data capture. The differentiator in 2026 procurement is how effectively these systems expose FHIR endpoints, embed decision support, and interoperate with analytics and AI platforms, reflecting integration guidance from the ONC and standards at HL7. Imaging and diagnostics: Siemens Healthineers, Philips, and GE HealthCare combine modality breadth with AI-enabled workflows and vendor-neutral archives. Enterprise buyers evaluate them on integration depth with EHRs, AI safety practices, and lifecycle support for models, reflecting clinician-centric adoption frameworks explored by Gartner and Stanford HAI. Virtual care and devices: Teladoc Health focuses on integrated virtual care programs, while ecosystems from Samsung and Apple provide wearables and SDKs for remote monitoring. Buyers scrutinize clinical validation, privacy controls, and reimbursement alignment with frameworks outlined by the FDA and global bodies like the WHO. Data and analytics: Snowflake, Databricks, and Palantir differentiate on governed data sharing, rapid cohort creation, and MLOps support. Healthcare-cloud services like AWS HealthLake provide domain-specific storage and interoperability features; enterprise choices hinge on compliance, cost-to-serve, and existing skill sets. Company Comparison

Company	Core Capability	Primary Buyer Segments	Notable Compliance/Standards
Epic	EHR and clinical workflow	Providers, IDNs	FHIR, HIPAA-aligned integration (HL7, HHS)
Oracle Health	EHR and data services	Providers, payers	FHIR, security certifications (ISO 27001, SOC 2)
Siemens Healthineers	Imaging and diagnostics	Hospitals, radiology networks	Interoperability with EHRs (Gartner frameworks)
Philips	Connected care and imaging	Providers, home care	Data privacy alignment (GDPR, HIPAA)
GE HealthCare	Imaging and command centers	Hospitals, national systems	Standards-driven integration (HL7)
Teladoc Health	Virtual care and RPM	Employers, payers, providers	Clinical and privacy frameworks (FDA)
Snowflake	Healthcare data cloud	Providers, payers, life sciences	Security certifications (ISO, SOC 2)
Databricks	Lakehouse analytics & ML	Providers, life sciences	Governed MLOps patterns (Stanford HAI)

Implementation and Architecture: How to Deploy at Enterprise Scale Architecture. A durable stack orients around four layers: data ingestion and normalization (FHIR-first), governed storage and access control, analytics and ML (with model registry, lineage, and monitoring), and last-mile workflow embedding into EHR, imaging, or care management tools. Reference patterns draw on HL7 FHIR, NIST zero trust, and certifications like ISO 27001 and SOC 2, implemented across platforms from Snowflake and Databricks to AWS HealthLake. Governance. Establish a data council co-led by clinical and data leaders; codify data use, retention, and de-identification; and create a model risk management program for AI that includes human factors testing and clinical safety cases. This aligns with safety orientation emphasized by the FDA and policy frameworks aggregated by the WHO, as well as maturity models explored by Forrester. Security. Apply least-privilege, policy-as-code access, and continuous compliance scanning; verify supply chain security for third-party models and devices. Based on analysis of cross-industry deployments, zero trust per NIST 800-207, combined with HIPAA and GDPR controls via HHS and GDPR, provides a basis for audit readiness, and is frequently supported by vendor attestations from Epic, Oracle Health, and ecosystem partners. Change management. Successful programs embed clinicians early, favor transparent model behavior, and measure impact using agreed benchmarks such as throughput, time-to-diagnosis, readmission rate, and patient satisfaction. According to enterprise case patterns summarized by Gartner and provider showcases with vendors like Siemens Healthineers and Philips, organizations that match technical investments with workflow redesign show stronger adoption—a theme consistent with our ongoing Health Tech coverage. Governance, Risk, and Regulation: Building Trust into Platforms Regulatory alignment spans data protection, medical device classification for software, and post-deployment monitoring for AI-in-health. Compliance anchors include HIPAA via HHS, GDPR via the EU framework, and ISO/SOC certifications via ISO and the AICPA, with guidance for software-as-a-medical-device oversight curated by the FDA. Vendors increasingly publish security and privacy documentation to support enterprise due diligence. According to corporate regulatory disclosures and compliance documentation, risk programs that integrate model risk management, privacy impact assessments, and continuous validation are becoming a de facto requirement for procurement. Peer-reviewed perspectives in venues such as ACM Computing Surveys and IEEE Transactions on Cloud Computing contribute methodologies that buyers can adapt to quantify bias, drift, and robustness. During recent investor briefings and public-facing strategy updates, platform executives reaffirmed the focus on outcomes and security. “The emphasis is squarely on clinical efficacy and operational impact with safety at the core,” said a senior executive at GE HealthCare, underscoring programmatic investments that reflect the same principles articulated across Philips and Siemens Healthineers product roadmaps. Outlook: What to Watch in 2026 and Beyond Expect the center of gravity to remain on data platforms and governable AI that demonstrably augments—not replaces—clinicians, with procurement decisions favoring clear safety cases and measurable value. Vendor ecosystems from Epic to Snowflake will continue to codify best practices around data model harmonization, secured data sharing, and ML lifecycle management supported by NIST and ISO. Device and virtual care ecosystems—anchored by Samsung, Apple, and care delivery platforms such as Teladoc Health—will likely see tighter integration with payer incentives and population health programs. As regulatory guidance evolves via the FDA and global institutions including the WHO, cross-border compliance and evidence standards are poised to shape enterprise-grade deployments.

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.

Related Coverage

• More Health Tech Analysis