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市场调查报告书
商品编码
1751235

美国去识别零售药局健康资料市场规模、份额、趋势分析报告：按资料集类型和细分市场预测，2025-2030 年

U.S. Retail Pharmacy De-identified Health Data Market Size, Share & Trends Analysis Report By Dataset Type (DSCSA Data, Market Basket Data, Inventory Data, Prior Authorization Data), And Segment Forecasts, 2025 - 2030
出版日期: | 出版商: Grand View Research | 英文 130 Pages | 商品交期: 2-10个工作天内

价格

市场规模与趋势：

预计 2024 年美国零售药局去识别化健康数据市场价值将达到 29 亿美元，2025 年至 2030 年期间的复合年增长率将达到 7.88%。

这一增长主要源于对真实世界证据 (RWE) 和真实世界数据 (RWD) 日益增长的需求，以及基于价值的护理 (VBC) 和基于结果的报销模式的持续扩展。此外，遵守《药品供应链安全法案》(DSCSA) 等有利的监管措施也进一步刺激了市场扩张。 VBC 模型的快速应用正在重塑美国医疗保健，重新定义医疗结果的衡量、定价和奖励。

去识别化的健康资料对于临床研究至关重要，因为它使研究人员能够在分析大量资料集的同时保护病患隐私。这些数据能够识别趋势、评估治疗效果，并在不洩露个人识别资讯的情况下支持人群健康研究。利用去识别化的数据，研究人员能够提高研究质量，并促进医学知识和实践的发展。

例如，2023年4月，飞利浦与麻省理工学院（MIT）医学工程与科学研究所（IMES）合作开发了一个增强型重症加护资料集，以推进临床研究和人工智慧在医疗保健领域的应用。该数据集包含ICU患者的去识别化数据，并整合了全面的临床信息，旨在帮助研究人员和教育工作者深入了解重症加护，并改善患者预后。该倡议将推动人工智慧主导的医疗保健解决方案的创新，从而实现更精准的诊断和个人化治疗。

与 COVID-19 相关的治疗核准数量庞大且紧迫性促使对去识别化资料的需求庞大。付款人和医疗保健提供者利用这些数据集来简化获取途径、自动化管理工作流程并支援快速决策。这些发展也影响了政策的演变，以减少公共卫生紧急事件期间医疗保健服务中的摩擦。药品和医疗用品的普遍供不应求凸显了在药房层级提高即时库存数据可视性的需求。製药商、批发商和医疗科技公司等相关人员在预测分析和基于人工智慧的库存追踪方面投入了大量资金，以主动管理缺货并确保及时获得关键治疗方法。

目录

第一章调查方法与范围

第二章执行摘要

第三章 产业展望－市场变数、趋势与范围

  • 市场展望
    • 全球市场展望
  • 市场动态
    • 依资料集类型展望关键驱动因素和相关见解
    • 市场驱动因素分析
    • 市场限制因素分析
    • 市场机会分析
    • 市场问题分析
  • 买家分析
  • 监管趋势
  • 美国零售药局去识别化健康数据市场（具体到 5 个数据集 - 以零售药局为卖家）：按数据集类型、级别和定价模型细分
    • 药品供应链安全资料（DSCSA）：​​（类型1段）总体水准定价模型结构及相关分析
    • 市场篮子资料：（第1类细分）总体水准定价模型结构及相关分析
    • 库存资料：（第1类细分）总体水准定价模型结构及相关分析
    • 核准前资料：（第1类细分）整体水平定价模型结构及相关分析
    • 事件资料/药局处方笺索赔资料：（第 1 类细分）总体水准定价模型结构及相关分析
  • 产业分析工具
    • 波特五力分析
    • PESTLE分析
  • 零售药局的具体趋势
  • 技术进步
  • COVID-19影响分析

第四章美国零售药局去识别化健康资料市场（具体到五大资料集 - 以零售药局为卖家）：资料集类型预估与趋势分析

  • 细分仪表板
  • 美国零售药局匿名健康资料市场（5 个专业资料集 - 零售药局卖家）：资料集类型分析，2024 年和 2030 年
  • 去识别化的零售药局健康资料集：按资料集类型分類的功能预期和提供者参考实践
    • 资料完整性
    • 数据更新的近期性和频率
    • 数据的广度和深度
    • 数据效用
    • 时间序列数据
    • 附加价值服务
  • 药局作为数据卖家：评分矩阵
  • 医药供应链安全资料 (DSCSA) 市场：（第 1 类细分市场）
    • 药品供应链安全资料（DSCSA）市场估计与预测，2018-2030年
    • DSCSA 资料-按买家类型分類的市场预测：（第 2 类细分市场）
  • 市场篮子资料市场：（第 1 类细分市场）
    • 2018-2030年市场篮子资料市场估计与预测
    • 市场购物篮资料－按买家类型分類的市场预期：（第 2 类细分市场）
  • 库存资料市场: (第 1 类细分市场)
    • 2018-2030年库存资料市场估计与预测
    • 库存资料-按买家类型分類的市场预测：（第 2 类细分市场）
  • 预先核准的资料市场：（第 1 类细分市场）
    • 2018-2030 年预先核准资料市场估计与预测
    • 核准资料-按买家类型分類的市场预测：（第 2 类细分市场）
  • 事件/药局处方笺索赔资料市场（第 1 类细分市场）
    • 2018-2030 年剧集/药房处方笺索赔资料市场估计和预测
    • 剧集/药处方笺索赔资料-按买家类型分類的市场预测：（第 2 类细分市场）

第五章 竞争态势

  • Participants'Overview
  • 财务表现
    • 上市公司
    • 私人公司
  • 竞争分析和基准测试
    • CVS 健康
    • 沃尔玛
    • 沃尔格林
    • 克罗格公司
    • 艾伯森
    • 联合健康集团（Optum）
    • Humana
    • 光明春天健康服务
    • 来爱德公司
    • HEB LP
    • 好市多批发公司
    • 森特纳有限公司
    • 荷兰皇家阿霍德德尔海兹公司
    • Aurora Healthcare（Advocate Health 的一个部门）
    • 大Y食品有限公司
    • 剪切机兄弟
    • 韦克芬食品公司
    • Publix
    • CUB（联合天然食品有限公司的子公司）
  • 参与企业
  • 2024年公司市场占有率分析（%）
    • 使用 Dscsa 资料集进行公司市场占有率分析
    • 使用市场篮子资料集对公司市场占有率进行分析
    • 使用库存资料集进行公司市场占有率分析
    • 基于事件数据/药房处方笺索赔数据的公司市场占有率分析
    • 预先核准的企业市场占有率分析
  • 战略地图
    • 推出新服务
    • 伙伴关係和合作
    • 区域扩张
    • 其他的
Product Code: GVR-4-68040-569-0

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

List of Tables

  • TABLE 1 List of secondary sources
  • TABLE 2 List of abbreviations
  • TABLE 3 Key commercial drivers, its impact, and insights
  • TABLE 4 State-wise distribution of retail pharmacies in the U.S. (2024)
  • TABLE 5 Buyer landscape at each dataset level

List of Figures

  • FIG. 1 U.S. Retail Pharmacy de-identified health data market segmentation
  • FIG. 2 Market research process
  • FIG. 3 Data triangulation techniques
  • FIG. 4 Primary research pattern
  • FIG. 5 Market research approaches
  • FIG. 6 Value-chain-based sizing & forecasting
  • FIG. 7 Market formulation & validation
  • FIG. 8 Market snapshot
  • FIG. 9 Dataset Type -Segment snapshot
  • FIG. 10 Competitive landscape snapshot
  • FIG. 11 Global De-identified Health Data vs U.S. Retail Pharmacy de-identified health data market (Specific to the Five Datasets - Retail Pharmacy as Seller) outlook, 2024, USD Billion
  • FIG. 12 U.S. Retail Pharmacy de-identified health data market dynamics
  • FIG. 13 U.S. Retail Pharmacy de-identified health data market : Porter's five forces analysis
  • FIG. 14 U.S. Retail Pharmacy de-identified health data market : PESTLE analysis
  • FIG. 15 U.S. Retail Pharmacy de-identified health data market , Dataset Type Outlook Key Takeaways (USD million)
  • FIG. 16 U.S. Retail Pharmacy de-identified health data market : Dataset Type Movement Analysis, 2024 & 2030 (USD Million)
  • FIG. 17 Drug Supply Chain Security Data (DSCSA) Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 18 Pharmaceutical Manufacturers Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 19 Commercial Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 20 R&D Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 21 Drug Distributors Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 22 Regulatory Tech Vendors Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 23 Healthcare SaaS Vendors Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 24 Others Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 25 Market Basket Data Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 26 CPG & Pharma Brands Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 27 Marketing & AdTech Firms Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 28 Health Insurers & PBMs Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 29 Retail Analytics Platforms Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 30 Others (Data Aggregators (e.g., NielsenIQ, IRI), etc.) Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 31 Inventory Data Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 32 Pharma Manufacturers Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 33 Commercial Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 34 R&D Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 35 Distributors/Wholesalers Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 36 AI/ML Inventory Optimization Vendors Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 37 Others (Clinical Supply Vendors, etc.) Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 38 Prior Authorization Data Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 39 Payers & PBMs Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 40 Pharma Market Access Teams Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 41 Commercial Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 42 R&D Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 43 Health IT Providers Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 44 Consulting & Policy Firms Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 45 Others (Advocacy Groups, etc.) Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 46 Episodic / Pharmacy Rx Claims Data Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 47 Value-based Payers & ACOs Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 48 Pharma Outcomes Teams Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 49 Commercial Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 50 R&D Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 51 Real-world Evidence Vendors Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 52 Health IT Providers Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 53 Others (AI/ML Healthtech Firms, etc.) Market Estimates and Forecasts, 2018 - 2030 (USD Million)
  • FIG. 54 Company categorization
  • FIG. 55 Strategic framework