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市場調查報告書
商品編碼
1751235

美國去識別零售藥局健康資料市場規模、佔有率、趨勢分析報告：按資料集類型和細分市場預測，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個工作天內

價格

市場規模與趨勢：

預計 2024 年美國零售藥局去識別化健康數據市場價值將達到 29 億美元，2025 年至 2030 年期間的複合年成長率將達到 7.88%。

這一成長主要源於對真實世界證據 (RWE) 和真實世界數據 (RWD) 日益成長的需求，以及基於價值的護理 (VBC) 和基於結果的報銷模式的持續擴展。此外，遵守《藥品供應鏈安全法案》(DSCSA) 等有利的監管措施也進一步刺激了市場擴張。 VBC 模型的快速應用正在重塑美國醫療保健，重新定義醫療結果的衡量、定價和獎勵。

去識別化的健康資料對於臨床研究至關重要，因為它使研究人員能夠在分析大量資料集的同時保護病患隱私。這些數據能夠識別趨勢、評估治療效果，並在不洩露個人識別資訊的情況下支持人群健康研究。利用去識別化的數據，研究人員能夠提高研究質量，並促進醫學知識和實踐的發展。

例如，2023年4月，飛利浦與麻省理工學院（MIT）醫學工程與科學研究所（IMES）合作開發了一個增強型重症加護資料集，以推進臨床研究和人工智慧在醫療保健領域的應用。該資料集包含ICU患者的去識別化數據，並整合了全面的臨床資訊，旨在幫助研究人員和教育工作者深入了解重症加護，並改善患者預後。該舉措將推動人工智慧主導的醫療保健解決方案的創新，從而實現更精準的診斷和個人化治療。

與 COVID-19 相關的治療核准數量龐大且緊迫性促使對去識別化資料的需求龐大。付款人和醫療保健提供者利用這些資料集來簡化獲取途徑、自動化管理工作流程並支援快速決策。這些發展也影響了政策的演變，以減少公共衛生緊急事件期間醫療保健服務中的摩擦。藥品和醫療用品的普遍供不應求凸顯了在藥房層級提高即時庫存數據可視性的需求。製藥商、批發商和醫療科技公司等相關人員在預測分析和基於人工智慧的庫存追蹤方面投入了大量資金，以主動管理缺貨並確保及時獲得關鍵治療方法。

目錄

第1章調查方法與範圍

第2章執行摘要

第3章 產業展望－市場變數、趨勢與範圍

  • 市場展望
    • 全球市場展望
  • 市場動態
    • 依資料集類型展望關鍵促進因素和相關見解
    • 市場促進因素分析
    • 市場限制因素分析
    • 市場機會分析
    • 市場問題分析
  • 買家分析
  • 監管趨勢
  • 美國零售藥局去識別化健康數據市場（具體到 5 個資料集 - 以零售藥局為賣家）：按資料集類型、級別和定價模型細分
    • 藥品供應鏈安全資料（DSCSA）：​​（類型1段）總體水準定價模型結構及相關分析
    • 市場籃子資料：（第1類細分）總體水準定價模型結構及相關分析
    • 庫存資料：（第1類細分）總體水準定價模型結構及相關分析
    • 核准前資料：（第1類細分）整體水平定價模型結構及相關分析
    • 事件資料/藥局處方箋索賠資料：（第 1 類細分）總體水準定價模型結構及相關分析
  • 產業分析工具
    • 波特五力分析
    • PESTLE分析
  • 零售藥局的具體趨勢
  • 技術進步
  • COVID-19影響分析

第4章美國零售藥局去識別化健康資料市場（具體到五大資料集 - 以零售藥局為賣家）：資料集類型預估與趨勢分析

  • 細分儀表板
  • 美國零售藥局匿名健康資料市場（5 個專業資料集 - 零售藥局賣家）：資料集類型分析，2024 年和 2030 年
  • 去識別化的零售藥局健康資料集：按資料集類型分類的功能預期和提供者參考實踐
    • 資料完整性
    • 數據更新的近期性和頻率
    • 數據的廣度和深度
    • 數據效用
    • 時間序列數據
    • 附加價值服務
  • 藥局作為數據賣家：評分矩陣
  • 醫藥供應鏈安全資料 (DSCSA) 市場：（第 1 類細分市場）
    • 藥品供應鏈安全資料（DSCSA）市場估計與預測，2018-2030年
    • DSCSA 資料-按買家類型分類的市場預測：（第 2 類細分市場）
  • 市場籃子資料市場：（第 1 類細分市場）
    • 2018-2030年市場籃子資料市場估計與預測
    • 市場購物籃資料－按買家類型分類的市場預期：（第 2 類細分市場）
  • 庫存資料市場: (第 1 類細分市場)
    • 2018-2030年庫存資料市場估計與預測
    • 庫存資料-按買家類型分類的市場預測：（第 2 類細分市場）
  • 預先核准的資料市場：（第 1 類細分市場）
    • 2018-2030 年預先核准資料市場估計與預測
    • 核准資料-按買家類型分類的市場預測：（第 2 類細分市場）
  • 事件/藥局處方箋索賠資料市場（第 1 類細分市場）
    • 2018-2030 年劇集/藥房處方箋索賠資料市場估計和預測
    • 劇集/藥處方箋索賠資料-按買家類型分類的市場預測：（第 2 類細分市場）

第5章 競爭態勢

  • Participants'Overview
  • 財務表現
    • 上市公司
    • 私人公司
  • 競爭分析和基準測試
    • CVS 健康
    • 沃爾瑪
    • 沃爾格林
    • 克羅格公司
    • 艾伯森
    • 聯合健康集團（Optum）
    • Humana
    • 光明春天健康服務
    • 來愛德公司
    • HEB LP
    • 好市多批發公司
    • 森特納有限公司
    • 荷蘭皇家阿霍德德爾海茲公司
    • Aurora Healthcare（Advocate Health 的一個部門）
    • 大Y食品有限公司
    • 剪切機兄弟
    • 韋克芬食品公司
    • Publix
    • CUB（聯合天然食品有限公司的子公司）
  • 參與企業
  • 2024年公司市場佔有率分析（%）
    • 使用 Dscsa 資料集進行公司市場佔有率分析
    • 使用市場籃子資料集對公司市場佔有率進行分析
    • 使用庫存資料集進行公司市場佔有率分析
    • 基於事件數據/藥房處方箋索賠數據的公司市場佔有率分析
    • 預先核准的企業市場佔有率分析
  • 戰略地圖
    • 推出新服務
    • 夥伴關係和合作
    • 區域擴張
    • 其他
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