미국의 소매 약국 비식별화 건강 데이터 시장 규모, 점유율, 동향 분석 리포트 : 데이터세트 유형별, 부문별 예측(2025-2030년)

Market Size & Trends:

The U.S. retail pharmacy de-identified health data market size was estimated at USD 2.90 billion in 2024 and is expected to grow at a CAGR of 7.88% from 2025 to 2030. This growth is primarily driven by the rising demand for real-world evidence (RWE) and real-world data (RWD), alongside the continued expansion of value-based care (VBC) and outcome-based reimbursement models. Additionally, favorable regulatory initiatives, such as compliance with the Drug Supply Chain Security Act (DSCSA), are further fueling market expansion. The rapid adoption of VBC models is reshaping the U.S. healthcare landscape by redefining how care outcomes are evaluated, priced, and incentivized.

De-identified health data is essential for clinical research as it allows researchers to analyze large datasets while protecting patient privacy. This data identifies trends, evaluates treatment effectiveness, and supports population health studies without compromising individual identities. By leveraging de-identified data, researchers can enhance the quality of their findings and facilitate advancements in medical knowledge and practice.

For instance, in April 2023, Philips and MIT's Institute for Medical Engineering and Science (IMES) collaborated to develop an enhanced critical care dataset to advance clinical research and AI applications in healthcare. This dataset includes de-identified data from ICU patients and integrates comprehensive clinical information to support researchers and educators in gaining insights into critical care and improving patient outcomes. The initiative fosters innovation in AI-driven healthcare solutions, contributing to more accurate diagnostics and personalized treatments.

The volume and urgency of treatment approvals related to COVID-19 drove significant demand for de-identified data. Payers and providers utilized these datasets to streamline access pathways, automate administrative workflows, and support rapid decision-making. These developments also informed the evolution of policies to reduce friction in care delivery during public health emergencies. Widespread drug and medical supply shortages highlighted the need for enhanced visibility into real-time inventory data at the pharmacy level. Stakeholders, including pharmaceutical manufacturers, wholesalers, and health tech companies, invested heavily in predictive analytics and AI-based inventory tracking to proactively manage stockouts and ensure timely access to critical therapies.

U.S. Retail Pharmacy De-identified Health Data Market Report Segmentation

This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2030. For this study, Grand View Research has segmented the U.S. Retail Pharmacy de-identified health data market report on the basis of dataset type:

• Dataset Type Outlook (Revenue, USD Million; 2018 - 2030)
• DSCSA Data
  • By Buyer Type:
  • Pharmaceutical Manufacturers
  • Drug Distributors
  • Regulatory Tech Vendors (e.g., TraceLink, LSPedia)
  • Healthcare SaaS Vendors (compliance and recall management tools)
  • Others (Federal Agencies e.g., FDA, etc.)
• Market Basket Data
  • By Buyer Type:
  • CPG & Pharma Brands
  • Marketing & AdTech Firms
  • Health Insurers & PBMs
  • Retail Analytics Platforms
  • Others (Data Aggregators (e.g., NielsenIQ, IRI), etc.)
• Prior Authorization Data
  • By Buyer Type:
  • Payers & PBMs
  • Pharma Market Access Teams
  • Health IT Providers
  • Consulting & Policy Firms
  • Others (Advocacy Groups, etc.)
• Inventory Data
  • By Buyer Type:
  • Pharma Manufacturers
  • Distributors/Wholesalers
  • AI/ML Inventory Optimization Vendors
  • Others (Clinical Supply Vendors, etc.)
• Episodic Data / Pharmacy Rx Claims Data
  • By Buyer Type:
  • Value-based Payers & ACOs
  • Pharma Outcomes Teams
  • Real-world Evidence Vendors
  • CMS & Government Organizations
  • Others (AI/ML Healthtech Firms, etc.)

Table of Content

Chapter 1 Methodology and Scope

• 1.1 Market Segmentation & Scope
  • 1.1.1 Estimates And Forecast Timeline
• 1.2 Objectives
  • 1.2.1 Objective - 1
  • 1.2.2 Objective - 2
• 1.3 Segment Definitions
  • 1.3.1 DATASET TYPE
• 1.4 Research Methodology
  • 1.4.1 DSCSA (DRUG Supply Chain Security Act): Research Scope And Assumption
    • 1.4.1.1 Volume Estimation: DSCSA De-identified Data
    • 1.4.1.2 CAGR Calculation (2025-2030)
  • 1.4.2 Prior Authorization: Research Scope And Assumption
    • 1.4.2.1 Volume Estimation: Prior Authorization Data
    • 1.4.2.2 CAGR Calculation (2025-2030)
  • 1.4.3 Market Basket Data: Research Scope And Assumption
    • 1.4.3.1 Volume Estimation: Market Basket Data
    • 1.4.3.2 CAGR Calculation (2025-2030)
  • 1.4.4 Episodic Data / Pharmacy Rx Claims Data: Research Scope And Assumption
  • 1.4.5 Inventory Data: Research Scope And Assumption
    • 1.4.5.1 Market Share and Assumption
  • 1.4.6 Information Procurement
    • 1.4.6.1 Purchased database
    • 1.4.6.2 GVR'S internal database
    • 1.4.6.3 Primary research
      • 1.4.6.3.1 Details of the primary research
• 1.5 Information or Data Analysis
  • 1.5.1 Data Analysis Models
• 1.6 Market Formulation & Validation
• 1.7 List of Secondary Sources
• 1.8 List of Abbreviations

Chapter 2 Executive Summary

• 2.1 Market Snapshot
• 2.2 Dataset Type - Segment Snapshot
• 2.3 Competitive Landscape Snapshot

Chapter 3 Industry Outlook - Market Variables, Trends & Scope

• 3.1 Market Lineage Outlook
  • 3.1.1 Global Market Outlook
• 3.2 Market Dynamics
  • 3.2.1 Outlook Of Key Drivers And Related Insights By Dataset Type
  • 3.2.2 Market Driver Analysis
    • 3.2.2.1 Increasing demand for real-world evidence (RWE) and real-world data (RWD)
    • 3.2.2.2 Favorable regulatory support for drug supply chain transparency (DSCSA Compliance)
    • 3.2.2.3 Growth of value-based care and outcome-based reimbursement models
  • 3.2.3 Market Restraint Analysis
    • 3.2.3.1 Stringent Privacy regulations and legal risk exposure
    • 3.2.3.2 Lack of data quality and data standardization
  • 3.2.4 Market Opportunity Analysis
    • 3.2.4.1 Integration with digital health, AI, and analytics platforms
  • 3.2.5 Market Challenge Analysis
    • 3.2.5.1 Ethical concerns and public distrust in data commercialization
• 3.3 Buyer Analysis
• 3.4 Regulatory Trends
• 3.5 U.S. Retail Pharmacy de-identified health data market (Specific to the Five Datasets - Retail Pharmacy as Seller): By Dataset Type Level Pricing Model details
  • 3.5.1 Drug Supply Chain Security Data (Dscsa): (Type 1 Segment) Overall Level Pricing Model Structure And Related Analysis
    • 3.5.1.1 Pricing Model Overview
      • 3.5.1.1.1 Model 1: Compliance-Tiered Licensing (Most Common)
      • 3.5.1.1.2 Model 2: Subscription-Based Access to Serialized Data Streams
      • 3.5.1.1.3 Model 3: Project-based or On-demand Query Models
    • 3.5.1.2 Price Range Analysis
      • 3.5.1.2.1 Retail Pharmacies as Sellers Example: CVS Health (ExtraCare Insights Platform)
      • 3.5.1.2.2 Retail Pharmacies as Sellers Example: Walgreens
  • 3.5.2 Market Basket Data: (Type 1 Segment) Overall Level Pricing Model Structure And Related Analysis
    • 3.5.2.1 Pricing Model Overview
      • 3.5.2.1.1 Model 1: Tiered Pricing Model (Most Common) (By Data Volume and Granularity)
      • 3.5.2.1.2 Model 2: Subscription-Based Access
      • 3.5.2.1.3 Model 3: Pay-per-Use or Custom Reports
    • 3.5.2.2 Price Range Analysis
      • 3.5.2.2.1 Retail Pharmacies as Sellers Example: CVS Health (ExtraCare Insights Platform)
      • 3.5.2.2.2 Retail Pharmacies as Sellers Example: Walgreens (Retail Analytics + Loyalty Program Data)
      • 3.5.2.2.3 Retail Pharmacies as Sellers Example: Rite Aid (Retail Pharmacy Analytics)
  • 3.5.3 Inventory Data: (Type 1 Segment) Overall Level Pricing Model Structure And Related Analysis
    • 3.5.3.1 Pricing Model Overview
      • 3.5.3.1.1 Model 1: Tiered Pricing Model (By Data Freshness and Geographic Depth)
      • 3.5.3.1.2 Model 2: Subscription-Based Access Data Feeds
      • 3.5.3.1.3 Model 3: Pay-per-Use or Targeted Alert Modules
    • 3.5.3.2 Price Range Analysis
      • 3.5.3.2.1 Retail Pharmacies as Sellers Example: CVS Health
      • 3.5.3.2.2 Retail Pharmacies as Sellers Example: Walgreens Boots Alliance
  • 3.5.4 Prior Authorization Data: (Type 1 Segment) Overall Level Pricing Model Structure And Related Analysis
    • 3.5.4.1 Pricing Model Overview
      • 3.5.4.1.1 Model 1: Event-based Data Feed Pricing (Most Common)
      • 3.5.4.1.2 Model 2: Subscription + Dashboard Access
      • 3.5.4.1.3 Model 3: Formulary Access Strategy Packages
    • 3.5.4.2 Price Range Analysis
      • 3.5.4.2.1 Retail Pharmacies as Sellers Example: CVS Health (Caremark (PBM arm) and MinuteClinic)
      • 3.5.4.2.2 Retail Pharmacies as Sellers Example: Walgreens
  • 3.5.5 Episodic Data / Pharmacy Rx Claims Data: (Type 1 Segment) Overall Level Pricing Model Structure And Related Analysis
    • 3.5.5.1 Pricing Model Overview
      • 3.5.5.1.1 Model 1: De-Identified Episodic Journey Files (Static Delivery)
      • 3.5.5.1.2 Model 2: Subscription-Based +Dashboard Or API
      • 3.5.5.1.3 Model 3: Custom Value-Based Care Packages
    • 3.5.5.2 Price Range Analysis
      • 3.5.5.2.1 Retail Pharmacies as Sellers Example: CVS Health MinuteClinic and HealthHUBs
      • 3.5.5.2.2 Retail Pharmacies as Sellers Example: Walgreens Health Corners
• 3.6 Industry Analysis Tools
  • 3.6.1 Porter's Five Forces Analysis
  • 3.6.2 Pestle Analysis
• 3.7 Retail-Pharmacy Specific Trends
• 3.8 Technological Advancements
• 3.9 COVID-19 Impact Analysis

Chapter 4 U.S. Retail Pharmacy de-identified health data market (Specific to the Five Datasets - Retail Pharmacy as Seller): Dataset Type Estimates & Trend Analysis

• 4.1 Segment Dashboard
• 4.2 U.S. Retail Pharmacy De-identified Health Data Market (Specific to the Five Datasets - Retail Pharmacy as Seller): Dataset Type Analysis, 2024 & 2030 (USD Million)
• 4.3 Retail Pharmacy- Enabled De-Identified Health Datasets: Feature Expectations and Provider Reference Practices (By Dataset Type)
  • 4.3.1 Data Integrity
  • 4.3.2 Data Recency & Update Frequency
  • 4.3.3 Data Breadth & Depth
  • 4.3.4 Data Usability
  • 4.3.5 Data Longitudinality
  • 4.3.6 Value Added Services
• 4.4 Retail Pharmacies as Data Sellers: Score Matrix
• 4.5 Drug Supply Chain Security Data (DSCSA) Market: (Type 1 segment)
  • 4.5.1 Drug Supply Chain Security Data (Dscsa) Market Estimates And Forecasts, 2018 - 2030 (USD Million)
  • 4.5.2 DSCSA Data - Market Expectations By Buyer Type: (Type 2 Segment)
    • 4.5.2.1 Pharmaceutical Manufacturers Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.5.2.2 Drug Distributors Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.5.2.3 Regulatory Tech Vendors (e.g., TraceLink, LSPedia) Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.5.2.4 Healthcare SaaS Vendors Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.5.2.5 Others (Federal Agencies e.g., FDA, etc.) Market estimates and forecasts, 2018 - 2030 (USD Million)
• 4.6 Market Basket Data Market: (Type 1 segment)
  • 4.6.1 Market Basket Data Market Estimates And Forecasts, 2018 - 2030 (USD Million)
  • 4.6.2 Market Basket Data -Market Expectations By Buyer Type: (Type 2 Segment)
    • 4.6.2.1 CPG & Pharma Brands Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.6.2.2 Marketing & AdTech Firms Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.6.2.3 Health Insurers & PBMs Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.6.2.4 Retail Analytics Platforms Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.6.2.5 Others (Data Aggregators (e.g., NielsenIQ, IRI), etc.)) Market estimates and forecasts, 2018 - 2030 (USD Million)
• 4.7 Inventory Data Market: (Type 1 segment)
  • 4.7.1 Inventory Data Market Estimates And Forecasts, 2018 - 2030 (USD Million)
  • 4.7.2 Inventory Data - Market Expectations By Buyer Type: (Type 2 Segment)
    • 4.7.2.1 Pharma Manufacturers Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.7.2.2 Distributors/Wholesalers Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.7.2.3 AI/ML Inventory Optimization Vendors Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.7.2.4 Others (Clinical Supply Vendors, etc.) Market estimates and forecasts, 2018 - 2030 (USD Million)
• 4.8 Prior Authorization Data Market: (Type 1 segment)
  • 4.8.1 Prior Authorization Data Market Estimates And Forecasts, 2018 - 2030 (USD Million)
  • 4.8.2 Prior Authorization Data - Market Expectations By Buyer Type: (Type 2 Segment)
    • 4.8.2.1 Payers & PBMs Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.8.2.2 Pharma Market Access Teams Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.8.2.3 Health IT Providers Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.8.2.4 Consulting & Policy Firms Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.8.2.5 Others (Advocacy Groups, etc.) Market estimates and forecasts, 2018 - 2030 (USD Million)
• 4.9 Episodic / Pharmacy Rx Claims Data Market: (Type 1 segment)
  • 4.9.1 Episodic / Pharmacy Rx Claims Data Market Estimates And Forecasts, 2018 - 2030 (USD Million)
  • 4.9.2 Episodic / Pharmacy Rx Claims Data - Market Expectations By Buyer Type: (Type 2 Segment)
    • 4.9.2.1 Value-based Payers & ACOs Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.9.2.2 Pharma Outcomes Teams Market Access Teams Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.9.2.3 Real-world Evidence Vendors Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.9.2.4 CMS & Government Organizations Market estimates and forecasts, 2018 - 2030 (USD Million)
    • 4.9.2.5 Others (AI/ML Healthtech Firms, etc.) Market estimates and forecasts, 2018 - 2030 (USD Million)

Chapter 5 Competitive Landscape

• 5.1 Participants' Overview
• 5.2 Financial Performance
  • 5.2.1 Public Companies
  • 5.2.2 Private Companies
• 5.3 Competitor Comparison Analysis & Benchmarking
  • 5.3.1 CVS HEALTH
    • 5.3.1.1 CVS Health- Estimated Pricing Models by Dataset Type
  • 5.3.2 WALMART
    • 5.3.2.1 Walmart - Estimated Pricing Models by Dataset Type
  • 5.3.3 WALGREENS
    • 5.3.3.1 Walgreens - Estimated Pricing Models by Dataset Type
    • 5.3.3.2 Walgreens Comparative Analysis Across Datasets (vs. Retail/Specialty Peers)
  • 5.3.4 THE KROGER CO.
    • 5.3.4.1 THE KROGER CO.- Estimated Pricing Models by Dataset Type
  • 5.3.5 ALBERTSON
    • 5.3.5.1 Albertson - Estimated Pricing Models by Dataset Type
  • 5.3.6 UNITEDHEALTH GROUP (OPTUM)
    • 5.3.6.1 UNITEDHEALTH GROUP (OPTUM) - Estimated Pricing Models by Dataset Type
  • 5.3.7 HUMANA
    • 5.3.7.1 HUMANA- Estimated Pricing Models by Dataset Type
  • 5.3.8 BRIGHTSPRING HEALTH SERVICES
    • 5.3.8.1 BrightSpring Health Services - Estimated Pricing Models by Dataset Type
  • 5.3.9 RITE AID CORP
    • 5.3.9.1 Rite Aid Corp - Estimated Pricing Models by Dataset Type
  • 5.3.10 H-E-B LP
    • 5.3.10.1 H-E-B LP - Estimated Pricing Models by Dataset Type
  • 5.3.11 COSTCO WHOLESALE CORPORATION
    • 5.3.11.1 COSTCO WHOLESALE CORPORATION- Estimated Pricing Models by Dataset Type
  • 5.3.12 CENTENE CORPORATION
    • 5.3.12.1 Centene Corporation- Estimated Pricing Models by Dataset Type
  • 5.3.13 KONINKLIJKE AHOLD DELHAIZE N.V.
    • 5.3.13.1 KONINKLIJKE AHOLD DELHAIZE N.V.- Estimated Pricing Models by Dataset Type
  • 5.3.14 AURORA HEALTH CARE (A PART OF ADVOCATE HEALTH)
    • 5.3.14.1 Aurora Health Care (a part of Advocate Health).- Estimated Pricing Models by Dataset Type
  • 5.3.15 BIG Y FOODS, INC.
    • 5.3.15.1 BIG Y FOODS, INC.- Estimated Pricing Models by Dataset Type
  • 5.3.16 BROOKSHIRE BROTHERS
    • 5.3.16.1 BROOKSHIRE BROTHERS - Estimated Pricing Models by Dataset Type
  • 5.3.17 WAKEFERN FOOD CORP.
    • 5.3.17.1 Wakefern Food Corp - Estimated Pricing Models by Dataset Type
  • 5.3.18 PUBLIX
    • 5.3.18.1 PUBLIX - Estimated Pricing Models by Dataset Type
  • 5.3.19 CUB (SUBSIDIARY OF UNITED NATURAL FOODS, INC.)
    • 5.3.19.1 Cub (subsidiary of United Natural Foods, Inc.) - Estimated Pricing Models by Dataset Type
• 5.4 Participant Categorization
• 5.5 Company Market Share Analysis, 2024 (%)
  • 5.5.1 Company Market Share Analysis, By Dscsa Dataset
  • 5.5.2 Company Market Share Analysis By Market Basket Data Dataset
  • 5.5.3 Company Market Share Analysis By Inventory Dataset
  • 5.5.4 Company Market Share Analysis By Episodic Data / Pharmacy Rx Claims Data
  • 5.5.5 Company Market Share Analysis By Prior Authorization
• 5.6 Strategy Mapping
  • 5.6.1 New Service Launch
  • 5.6.2 Partnerships And Collaboration
  • 5.6.3 Regional Expansion
  • 5.6.4 Others