Big Data in Healthcare Market by Component (Hardware, Services, Software), Deployment Mode (Cloud, On-Premises), Application, End User - Global Forecast 2025-2032
Description
The Big Data in Healthcare Market was valued at USD 2.01 billion in 2024 and is projected to grow to USD 2.56 billion in 2025, with a CAGR of 28.95%, reaching USD 15.41 billion by 2032.
An authoritative orientation to healthcare data dynamics that frames the intersection of technology, governance, and clinical decision-making for leaders
The convergence of clinical care, operational efficiency, and advanced analytics is reshaping how health systems, payers, life sciences organizations, and technology vendors approach data. This executive summary synthesizes critical forces transforming how large-scale health data is collected, processed, and applied to improve outcomes, reduce waste, and enable novel care models. Stakeholders today must navigate a complex environment where technological capability intersects with regulatory expectations and shifting care delivery models.
Across clinical environments and administrative operations, the strategic value of data now depends on the ability to integrate disparate sources, apply advanced analytics, and translate insights into consistent workflow changes. As a result, leaders are prioritizing investments that enable end-to-end data orchestration, robust governance, and measurable impact on clinical quality and financial performance. This introduction sets the stage for a deeper exploration of the shifts, segmentation intelligence, regional influences, competitive dynamics, and practical recommendations contained in the full research deliverable.
Identifying the pivotal technological, regulatory, and care-delivery transformations that are redefining how health data is generated, governed, and monetized
The healthcare data landscape is undergoing rapid, structural change driven by advances in analytics, evolving care models, and heightened expectations for interoperability. Machine learning and real-time streaming analytics are moving from experimental pilots into clinical and operational use, enabling predictive alerts, prescriptive care pathways, and automated resource allocation. In parallel, the maturation of integrated data platforms is enabling previously siloed datasets-clinical records, imaging, genomics, claims, and remote monitoring-to be combined in ways that produce clinically actionable insights at scale.
Regulatory and ethical considerations are also reshaping the landscape. Data privacy frameworks and standards encourage safer sharing while imposing new compliance demands that affect architecture choices and vendor selection. At the same time, value-based care incentives and population health initiatives are pushing analytics upstream into patient engagement and risk stratification workflows. Collectively, these shifts create both urgency and opportunity: organizations that align strategy, governance, and technology will be positioned to unlock efficiencies, improve patient outcomes, and establish defensible operational advantages.
A forward-looking analysis of how trade policy adjustments influence procurement, infrastructure strategy, and vendor behavior across the healthcare analytics supply chain
The introduction of tariffs and trade adjustments has multifaceted implications for healthcare analytics ecosystems, affecting hardware costs, vendor sourcing, and supply chain resilience. Increased import duties on networking equipment and servers can raise the total cost of on-premises infrastructure procurement, prompting CIOs and procurement teams to reassess capital allocation and lifecycle planning. As a consequence, organizations may accelerate considerations of hybrid or cloud-first deployment strategies to mitigate upfront capital exposure and to access economies of scale offered by hyperscale providers.
Beyond direct procurement impacts, tariffs can create ripple effects in vendor roadmaps and partnership models. Hardware-focused suppliers may reconfigure manufacturing footprints or shift toward bundled services and software subscriptions to preserve margins, which in turn influences integration timelines and support models for health systems. Given these dynamics, healthcare organizations should prioritize contingency planning that includes diversified supplier pools, total-cost-of-ownership analyses, and contractual provisions to protect against supply disruptions and price volatility. Proactive procurement strategies and architectural flexibility will be key to maintaining project timelines and ensuring continuity of care delivery under changing trade conditions.
A refined segmentation framework that reveals how component choices, deployment architectures, application priorities, and end-user needs determine adoption pathways and value realization
Rigorous segmentation clarifies where investment and operational attention deliver the most strategic value across components, deployment modes, applications, and end users. When considering components, portfolios typically span hardware, services, and software; hardware focus commonly includes networking equipment and servers while services often separate into managed services and professional services, and software offerings range from integrated platforms to standalone applications. This component-level view highlights where technical debt may constrain adoption and where modular software can accelerate time to value.
Deployment choices further shape implementation risk and operating models. Cloud and on-premises remain core options, with cloud adoption manifesting as hybrid cloud, private cloud, and public cloud variants, and on-premises implementations commonly hosted privately or maintained in-house. These deployment distinctions influence integration complexity, security postures, and long-term operational cost structures. At the application level, healthcare organizations concentrate efforts on clinical analytics, operational analytics, population health management, and revenue cycle management; clinical analytics themselves encompass descriptive, predictive, and prescriptive modalities, operational analytics cover resource utilization, supply chain management, and workflow optimization, population health management addresses care coordination, patient engagement, and risk stratification, and revenue cycle management focuses on billing management and claims management. Finally, end users span healthcare providers, life sciences companies, patients and caregivers, and payers and insurers; providers segment into ambulatory services, clinics, and hospitals, life sciences organizations separate into pharmaceutical companies and research institutions, patient-facing services include home care and telehealth, and payers divide across government and private insurers. This layered segmentation framework helps leaders align product development, sales strategies, and implementation support with the specific needs of each constituency and use case.
An integrated view of regional divergences and complementarities that informs deployment strategy, compliance planning, and market entry for analytics-enabled healthcare solutions
Regional dynamics continue to influence technology adoption patterns, regulatory posture, and partnership models across the Americas, Europe, Middle East & Africa, and Asia-Pacific, with each geography presenting distinct enablers and constraints. In the Americas, payers and large integrated delivery networks drive demand for end-to-end analytics platforms that support value-based care initiatives and revenue cycle optimization, while a dense ecosystem of cloud providers and systems integrators accelerates adoption of hybrid architectures. Investment in digital front-door and telehealth capabilities remains strong as organizations seek to extend care beyond traditional settings.
Europe, the Middle East & Africa exhibit varied regulatory regimes and public-private health system structures that shape procurement and interoperability priorities. Stricter data protection rules and national data residency expectations often favor private cloud or on-premises deployments that prioritize sovereignty and compliance. Meanwhile, regional initiatives to modernize public health infrastructure encourage collaborative procurement and standardized data models. In the Asia-Pacific region, rapid digitization combined with population-scale projects fuels demand for scalable cloud solutions and advanced analytics, and many markets are investing in genomic and precision medicine programs that require high-throughput computing and secure data exchange frameworks. Collectively, regional contrasts underscore the importance of adaptable commercialization strategies that respect local regulation, procurement practices, and infrastructure readiness.
A concise profile of market-leading attributes and strategic partnerships that explain how top performers convert technical capability into reliable clinical and operational outcomes
Leading organizations in the healthcare data ecosystem are defined by their ability to combine domain expertise with engineering excellence, and by the investments they make in interoperability and trust. Companies that succeed typically offer comprehensive integration capabilities, long-term support commitments, and clear pathways for embedding analytics into clinician workflows. Strategic partners include cloud providers that supply elastic compute and managed services, software vendors that provide clinical and operational intelligence, and systems integrators that bridge technology with change management.
Competitive differentiation often emerges from proprietary data connectors, validated clinical models, and turnkey implementation accelerators that reduce time to impact. Additionally, companies that prioritize transparent governance frameworks and robust security certifications build stronger adoption momentum among cautious buyers. Collaboration between technology vendors and clinical leaders, supported by shared success metrics and outcome-based contracting models, further distinguishes organizations that can deliver measurable clinical and financial improvements. In short, market-leading firms blend technical depth, healthcare domain knowledge, and proven deployment methodologies to enable predictable value delivery.
Action-oriented strategic steps and governance priorities that accelerate adoption, reduce implementation risk, and ensure analytics initiatives drive measurable clinical and financial benefits
Leaders should pursue a pragmatic combination of technology choices, governance structures, and workforce strategies to convert analytics potential into operational improvements. First, prioritize integrated platforms that enable data harmonization across clinical records, imaging, claims, and patient-generated data while preserving modularity so that specific functions can be upgraded independently. Second, establish a formal data governance program with clear stewardship, quality metrics, and privacy controls, and couple governance with clinician-led validation processes to ensure models are clinically relevant and adopted at the point of care.
Invest in hybrid deployment models that balance the scalability of public cloud with on-premises or private cloud options where data sovereignty or latency constraints apply, and build procurement frameworks that allow for supplier diversity to mitigate supply chain or policy risks. Develop talent through cross-functional teams that combine data science, clinical informatics, and implementation specialists, and create partnership models with vendors that include outcome-based incentives. Finally, measure success through a limited set of operational and clinical KPIs that tie analytics initiatives to patient outcomes and financial performance, and iterate quickly based on those measurements to sustain momentum and stakeholder buy-in.
A transparent and rigorous mixed-methods research design that blends primary interviews, secondary analysis, expert validation, and scenario testing to ensure practical relevance
The research approach combines structured primary inquiry with rigorous secondary synthesis and validation to ensure findings reflect both practice and emerging trends. Primary research consisted of in-depth interviews and workshops with clinical leaders, CIOs, health system operations executives, payers, and vendor executives, which surfaced firsthand experiences about deployment challenges, integration barriers, and success factors. Secondary research involved systematic review of policy developments, technology whitepapers, vendor documentation, and peer-reviewed literature to contextualize primary insights and to track shifts in regulatory expectations and technology capability.
Findings were cross-validated through expert panels and scenario analysis to test assumptions under differing procurement, regulatory, and technological conditions. Segmentation logic was applied iteratively, mapping components, deployment modes, application areas, and end-user needs to observed adoption behaviors and procurement preferences. Quality assurance included data triangulation, documentation of evidence sources, and a review cycle with external subject-matter experts to reduce bias and ensure practical relevance for decision-makers evaluating strategic investments in healthcare data capabilities.
A concise synthesis that ties together governance, clinician engagement, and pragmatic deployment choices to enable scalable and sustainable analytics-driven care improvements
Sustained progress in healthcare depends on marrying technical maturity with organizational readiness and trust. Analytics and big data technologies can deliver meaningful improvements in clinical decision-making, operational efficiency, and population health only when supported by committed governance, clinician engagement, and adaptable deployment strategies. Organizations that invest in interoperability, robust privacy safeguards, and outcome-linked procurement will be best positioned to translate pilot successes into enterprise-scale programs.
As the landscape evolves, leaders should maintain an agile posture-balancing innovation with risk management-and focus on high-impact use cases that demonstrate clear clinical or operational returns. By combining a disciplined approach to data governance, thoughtful vendor selection, and continuous measurement of outcomes, healthcare organizations can harness data to improve patient care while managing costs and regulatory expectations. This conclusion reinforces the practical steps that stakeholders can take to convert strategy into measurable improvement.
Note: PDF & Excel + Online Access - 1 Year
An authoritative orientation to healthcare data dynamics that frames the intersection of technology, governance, and clinical decision-making for leaders
The convergence of clinical care, operational efficiency, and advanced analytics is reshaping how health systems, payers, life sciences organizations, and technology vendors approach data. This executive summary synthesizes critical forces transforming how large-scale health data is collected, processed, and applied to improve outcomes, reduce waste, and enable novel care models. Stakeholders today must navigate a complex environment where technological capability intersects with regulatory expectations and shifting care delivery models.
Across clinical environments and administrative operations, the strategic value of data now depends on the ability to integrate disparate sources, apply advanced analytics, and translate insights into consistent workflow changes. As a result, leaders are prioritizing investments that enable end-to-end data orchestration, robust governance, and measurable impact on clinical quality and financial performance. This introduction sets the stage for a deeper exploration of the shifts, segmentation intelligence, regional influences, competitive dynamics, and practical recommendations contained in the full research deliverable.
Identifying the pivotal technological, regulatory, and care-delivery transformations that are redefining how health data is generated, governed, and monetized
The healthcare data landscape is undergoing rapid, structural change driven by advances in analytics, evolving care models, and heightened expectations for interoperability. Machine learning and real-time streaming analytics are moving from experimental pilots into clinical and operational use, enabling predictive alerts, prescriptive care pathways, and automated resource allocation. In parallel, the maturation of integrated data platforms is enabling previously siloed datasets-clinical records, imaging, genomics, claims, and remote monitoring-to be combined in ways that produce clinically actionable insights at scale.
Regulatory and ethical considerations are also reshaping the landscape. Data privacy frameworks and standards encourage safer sharing while imposing new compliance demands that affect architecture choices and vendor selection. At the same time, value-based care incentives and population health initiatives are pushing analytics upstream into patient engagement and risk stratification workflows. Collectively, these shifts create both urgency and opportunity: organizations that align strategy, governance, and technology will be positioned to unlock efficiencies, improve patient outcomes, and establish defensible operational advantages.
A forward-looking analysis of how trade policy adjustments influence procurement, infrastructure strategy, and vendor behavior across the healthcare analytics supply chain
The introduction of tariffs and trade adjustments has multifaceted implications for healthcare analytics ecosystems, affecting hardware costs, vendor sourcing, and supply chain resilience. Increased import duties on networking equipment and servers can raise the total cost of on-premises infrastructure procurement, prompting CIOs and procurement teams to reassess capital allocation and lifecycle planning. As a consequence, organizations may accelerate considerations of hybrid or cloud-first deployment strategies to mitigate upfront capital exposure and to access economies of scale offered by hyperscale providers.
Beyond direct procurement impacts, tariffs can create ripple effects in vendor roadmaps and partnership models. Hardware-focused suppliers may reconfigure manufacturing footprints or shift toward bundled services and software subscriptions to preserve margins, which in turn influences integration timelines and support models for health systems. Given these dynamics, healthcare organizations should prioritize contingency planning that includes diversified supplier pools, total-cost-of-ownership analyses, and contractual provisions to protect against supply disruptions and price volatility. Proactive procurement strategies and architectural flexibility will be key to maintaining project timelines and ensuring continuity of care delivery under changing trade conditions.
A refined segmentation framework that reveals how component choices, deployment architectures, application priorities, and end-user needs determine adoption pathways and value realization
Rigorous segmentation clarifies where investment and operational attention deliver the most strategic value across components, deployment modes, applications, and end users. When considering components, portfolios typically span hardware, services, and software; hardware focus commonly includes networking equipment and servers while services often separate into managed services and professional services, and software offerings range from integrated platforms to standalone applications. This component-level view highlights where technical debt may constrain adoption and where modular software can accelerate time to value.
Deployment choices further shape implementation risk and operating models. Cloud and on-premises remain core options, with cloud adoption manifesting as hybrid cloud, private cloud, and public cloud variants, and on-premises implementations commonly hosted privately or maintained in-house. These deployment distinctions influence integration complexity, security postures, and long-term operational cost structures. At the application level, healthcare organizations concentrate efforts on clinical analytics, operational analytics, population health management, and revenue cycle management; clinical analytics themselves encompass descriptive, predictive, and prescriptive modalities, operational analytics cover resource utilization, supply chain management, and workflow optimization, population health management addresses care coordination, patient engagement, and risk stratification, and revenue cycle management focuses on billing management and claims management. Finally, end users span healthcare providers, life sciences companies, patients and caregivers, and payers and insurers; providers segment into ambulatory services, clinics, and hospitals, life sciences organizations separate into pharmaceutical companies and research institutions, patient-facing services include home care and telehealth, and payers divide across government and private insurers. This layered segmentation framework helps leaders align product development, sales strategies, and implementation support with the specific needs of each constituency and use case.
An integrated view of regional divergences and complementarities that informs deployment strategy, compliance planning, and market entry for analytics-enabled healthcare solutions
Regional dynamics continue to influence technology adoption patterns, regulatory posture, and partnership models across the Americas, Europe, Middle East & Africa, and Asia-Pacific, with each geography presenting distinct enablers and constraints. In the Americas, payers and large integrated delivery networks drive demand for end-to-end analytics platforms that support value-based care initiatives and revenue cycle optimization, while a dense ecosystem of cloud providers and systems integrators accelerates adoption of hybrid architectures. Investment in digital front-door and telehealth capabilities remains strong as organizations seek to extend care beyond traditional settings.
Europe, the Middle East & Africa exhibit varied regulatory regimes and public-private health system structures that shape procurement and interoperability priorities. Stricter data protection rules and national data residency expectations often favor private cloud or on-premises deployments that prioritize sovereignty and compliance. Meanwhile, regional initiatives to modernize public health infrastructure encourage collaborative procurement and standardized data models. In the Asia-Pacific region, rapid digitization combined with population-scale projects fuels demand for scalable cloud solutions and advanced analytics, and many markets are investing in genomic and precision medicine programs that require high-throughput computing and secure data exchange frameworks. Collectively, regional contrasts underscore the importance of adaptable commercialization strategies that respect local regulation, procurement practices, and infrastructure readiness.
A concise profile of market-leading attributes and strategic partnerships that explain how top performers convert technical capability into reliable clinical and operational outcomes
Leading organizations in the healthcare data ecosystem are defined by their ability to combine domain expertise with engineering excellence, and by the investments they make in interoperability and trust. Companies that succeed typically offer comprehensive integration capabilities, long-term support commitments, and clear pathways for embedding analytics into clinician workflows. Strategic partners include cloud providers that supply elastic compute and managed services, software vendors that provide clinical and operational intelligence, and systems integrators that bridge technology with change management.
Competitive differentiation often emerges from proprietary data connectors, validated clinical models, and turnkey implementation accelerators that reduce time to impact. Additionally, companies that prioritize transparent governance frameworks and robust security certifications build stronger adoption momentum among cautious buyers. Collaboration between technology vendors and clinical leaders, supported by shared success metrics and outcome-based contracting models, further distinguishes organizations that can deliver measurable clinical and financial improvements. In short, market-leading firms blend technical depth, healthcare domain knowledge, and proven deployment methodologies to enable predictable value delivery.
Action-oriented strategic steps and governance priorities that accelerate adoption, reduce implementation risk, and ensure analytics initiatives drive measurable clinical and financial benefits
Leaders should pursue a pragmatic combination of technology choices, governance structures, and workforce strategies to convert analytics potential into operational improvements. First, prioritize integrated platforms that enable data harmonization across clinical records, imaging, claims, and patient-generated data while preserving modularity so that specific functions can be upgraded independently. Second, establish a formal data governance program with clear stewardship, quality metrics, and privacy controls, and couple governance with clinician-led validation processes to ensure models are clinically relevant and adopted at the point of care.
Invest in hybrid deployment models that balance the scalability of public cloud with on-premises or private cloud options where data sovereignty or latency constraints apply, and build procurement frameworks that allow for supplier diversity to mitigate supply chain or policy risks. Develop talent through cross-functional teams that combine data science, clinical informatics, and implementation specialists, and create partnership models with vendors that include outcome-based incentives. Finally, measure success through a limited set of operational and clinical KPIs that tie analytics initiatives to patient outcomes and financial performance, and iterate quickly based on those measurements to sustain momentum and stakeholder buy-in.
A transparent and rigorous mixed-methods research design that blends primary interviews, secondary analysis, expert validation, and scenario testing to ensure practical relevance
The research approach combines structured primary inquiry with rigorous secondary synthesis and validation to ensure findings reflect both practice and emerging trends. Primary research consisted of in-depth interviews and workshops with clinical leaders, CIOs, health system operations executives, payers, and vendor executives, which surfaced firsthand experiences about deployment challenges, integration barriers, and success factors. Secondary research involved systematic review of policy developments, technology whitepapers, vendor documentation, and peer-reviewed literature to contextualize primary insights and to track shifts in regulatory expectations and technology capability.
Findings were cross-validated through expert panels and scenario analysis to test assumptions under differing procurement, regulatory, and technological conditions. Segmentation logic was applied iteratively, mapping components, deployment modes, application areas, and end-user needs to observed adoption behaviors and procurement preferences. Quality assurance included data triangulation, documentation of evidence sources, and a review cycle with external subject-matter experts to reduce bias and ensure practical relevance for decision-makers evaluating strategic investments in healthcare data capabilities.
A concise synthesis that ties together governance, clinician engagement, and pragmatic deployment choices to enable scalable and sustainable analytics-driven care improvements
Sustained progress in healthcare depends on marrying technical maturity with organizational readiness and trust. Analytics and big data technologies can deliver meaningful improvements in clinical decision-making, operational efficiency, and population health only when supported by committed governance, clinician engagement, and adaptable deployment strategies. Organizations that invest in interoperability, robust privacy safeguards, and outcome-linked procurement will be best positioned to translate pilot successes into enterprise-scale programs.
As the landscape evolves, leaders should maintain an agile posture-balancing innovation with risk management-and focus on high-impact use cases that demonstrate clear clinical or operational returns. By combining a disciplined approach to data governance, thoughtful vendor selection, and continuous measurement of outcomes, healthcare organizations can harness data to improve patient care while managing costs and regulatory expectations. This conclusion reinforces the practical steps that stakeholders can take to convert strategy into measurable improvement.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
185 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of genomics and electronic health record data using AI-driven analytics for personalized medicine
- 5.2. Real-time predictive monitoring of patient vital signs through cloud-based big data pipelines
- 5.3. Leveraging social determinants of health datasets to drive targeted population health interventions
- 5.4. Applying deep learning to multi-modal imaging and laboratory big data for early disease detection
- 5.5. Implementing blockchain-enabled data governance models for secure patient information exchange
- 5.6. Utilizing real-world evidence from insurance claims big data to optimize clinical trial design and recruitment
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Big Data in Healthcare Market, by Component
- 8.1. Hardware
- 8.1.1. Networking Equipment
- 8.1.2. Servers
- 8.2. Services
- 8.2.1. Managed Services
- 8.2.2. Professional Services
- 8.3. Software
- 8.3.1. Integrated Platforms
- 8.3.2. Standalone Software
- 9. Big Data in Healthcare Market, by Deployment Mode
- 9.1. Cloud
- 9.1.1. Hybrid Cloud
- 9.1.2. Private Cloud
- 9.1.3. Public Cloud
- 9.2. On-Premises
- 10. Big Data in Healthcare Market, by Application
- 10.1. Clinical Analytics
- 10.1.1. Descriptive Analytics
- 10.1.2. Predictive Analytics
- 10.1.3. Prescriptive Analytics
- 10.2. Operational Analytics
- 10.2.1. Resource Utilization
- 10.2.2. Supply Chain Management
- 10.2.3. Workflow Optimization
- 10.3. Population Health Management
- 10.3.1. Care Coordination
- 10.3.2. Patient Engagement
- 10.3.3. Risk Stratification
- 10.4. Revenue Cycle Management
- 10.4.1. Billing Management
- 10.4.2. Claims Management
- 11. Big Data in Healthcare Market, by End User
- 11.1. Healthcare Providers
- 11.1.1. Ambulatory Services
- 11.1.2. Clinics
- 11.1.3. Hospitals
- 11.2. Life Sciences Companies
- 11.2.1. Pharmaceutical Companies
- 11.2.2. Research Organizations
- 11.3. Patients And Caregivers
- 11.3.1. Home Care
- 11.3.2. Telehealth Services
- 11.4. Payers And Insurers
- 11.4.1. Government Payers
- 11.4.2. Private Insurers
- 12. Big Data in Healthcare Market, by Region
- 12.1. Americas
- 12.1.1. North America
- 12.1.2. Latin America
- 12.2. Europe, Middle East & Africa
- 12.2.1. Europe
- 12.2.2. Middle East
- 12.2.3. Africa
- 12.3. Asia-Pacific
- 13. Big Data in Healthcare Market, by Group
- 13.1. ASEAN
- 13.2. GCC
- 13.3. European Union
- 13.4. BRICS
- 13.5. G7
- 13.6. NATO
- 14. Big Data in Healthcare Market, by Country
- 14.1. United States
- 14.2. Canada
- 14.3. Mexico
- 14.4. Brazil
- 14.5. United Kingdom
- 14.6. Germany
- 14.7. France
- 14.8. Russia
- 14.9. Italy
- 14.10. Spain
- 14.11. China
- 14.12. India
- 14.13. Japan
- 14.14. Australia
- 14.15. South Korea
- 15. Competitive Landscape
- 15.1. Market Share Analysis, 2024
- 15.2. FPNV Positioning Matrix, 2024
- 15.3. Competitive Analysis
- 15.3.1. Alphabet Inc.
- 15.3.2. Arcadia Solutions, LLC
- 15.3.3. CitiusTech, Inc.
- 15.3.4. Cognizant Technology Solutions Corporation
- 15.3.5. Cotiviti, Inc.
- 15.3.6. EXLService Holdings, Inc.
- 15.3.7. General Electric Company
- 15.3.8. Health Catalyst, Inc.
- 15.3.9. HealthEC, LLC
- 15.3.10. IBM Corporation
- 15.3.11. Innovaccer, Inc.
- 15.3.12. Inovalon, Inc.
- 15.3.13. IQVIA Holdings Inc.
- 15.3.14. Koninklijke Philips N.V.
- 15.3.15. MedeAnalytics, Inc.
- 15.3.16. Microsoft Corporation
- 15.3.17. Optum, Inc.
- 15.3.18. Oracle Corporation
- 15.3.19. SAP SE
- 15.3.20. SAS Institute Inc.
- 15.3.21. SAS Institute Inc.
- 15.3.22. Sophia Genetics SA
- 15.3.23. UnitedHealth Group Incorporated
- 15.3.24. Veradigm LLC
- 15.3.25. Wipro Limited
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