米国の小売薬局の非識別化健康データ市場規模、シェア、動向分析レポート：データセットタイプ別、セグメント別予測、2025年～2030年

市場規模と動向：

米国の小売薬局の非識別化健康データ市場規模は2024年に29億米ドルと推計され、2025年から2030年にかけてCAGR 7.88％で成長すると予測されます。

この成長は主に、バリュー・ベース・ケア（VBC）とアウトカムベースの償還モデルの継続的な拡大とともに、リアル・ワールド・エビデンス（RWE）とリアル・ワールド・データ（RWD）に対する需要の高まりによってもたらされます。さらに、医薬品供給連鎖安全保障法（DSCSA）への対応など、規制面での有利な取り組みが市場拡大にさらに拍車をかけています。VBCモデルの急速な導入は、医療成果の評価、価格設定、インセンティブ付与の方法を再定義することで、米国のヘルスケアを再構築しています。

非識別化された健康データは、研究者が患者のプライバシーを保護しながら大規模なデータセットを分析することを可能にするため、臨床研究にとって不可欠です。このデータは、個人のアイデンティティを損なうことなく、動向を特定し、治療効果を評価し、集団健康研究をサポートします。非識別化データを活用することで、研究者は調査結果の質を高め、医学知識と診療の進歩を促進することができます。

例えば、2023年4月、フィリップスとマサチューセッツ工科大学（MIT）の医用工学・科学研究所（IMES）は、ヘルスケアにおける臨床研究とAIアプリケーションを推進するため、強化されたクリティカルケアデータセットを共同で開発しました。このデータセットにはICU患者の非識別化データが含まれ、包括的な臨床情報が統合されているため、研究者や教育者がクリティカルケアに関する洞察を深め、患者の転帰を改善できるよう支援します。このイニシアチブは、AI主導のヘルスケアソリューションにおけるイノベーションを促進し、より正確な診断と個別化治療に貢献します。

COVID-19に関連する治療承認の量と緊急性は、非識別化データに対する大きな需要を促しました。支払者と医療提供者はこれらのデータセットを活用して、アクセス経路を合理化し、管理ワークフローを自動化し、迅速な意思決定をサポートしました。このような開発は、公衆衛生上の緊急事態における医療提供の摩擦を軽減するための政策の進展にも影響を与えました。医薬品や医療品の供給不足が広まったことで、薬局レベルでのリアルタイムの在庫データの可視性を高める必要性が浮き彫りになりました。製薬メーカー、卸売業者、ヘルステック企業などの利害関係者は、在庫切れを事前に管理し、重要な治療法へのタイムリーなアクセスを確保するために、予測分析とAIベースの在庫追跡に多額の投資を行いました。

目次

第1章 調査手法と範囲

第2章 エグゼクティブサマリー

第3章 業界展望- 市場変数、動向、範囲

• 市場系統の見通し
  • 世界市場の見通し
• 市場力学
  • データセットタイプ別の主要促進要因と関連洞察の展望
  • 市場促進要因分析
  • 市場抑制要因分析
  • 市場機会分析
  • 市場課題分析
• バイヤー分析
• 規制動向
• 米国の小売薬局の非識別化健康データ市場（5つのデータセットに特有-小売薬局を販売者として対象とするもの）：データセットタイプ別、レベル別、価格設定モデルの詳細
  • 医薬品サプライチェーンセキュリティデータ（DSCSA）：（タイプ1セグメント）全体的なレベルの価格設定モデル構造と関連分析
  • マーケットバスケットデータ：（タイプ1セグメント）全体的なレベルの価格設定モデル構造と関連分析
  • 在庫データ：（タイプ1セグメント）全体的なレベルの価格設定モデル構造と関連分析
  • 事前承認データ：（タイプ1セグメント）全体的なレベルの価格設定モデル構造と関連分析
  • エピソードデータ/薬局処方箋請求データ：（タイプ1セグメント）全体的なレベルの価格設定モデル構造と関連分析
• 業界分析ツール
  • ポーターのファイブフォース分析
  • PESTLE分析
• 小売薬局特有の動向
• 技術の進歩
• COVID-19の影響分析

第4章 米国の小売薬局の非識別化健康データ市場（5つのデータセットに特有-小売薬局を販売者として対象とする場合）：データセットタイプの推定・動向分析

• セグメントダッシュボード
• 米国の小売薬局匿名化健康データ市場（5つのデータセットに特化- 小売薬局が販売者）：データセットタイプ分析、2024年および2030年
• 小売薬局対応の非識別化健康データセット：機能の期待値とプロバイダーの参照プラクティス（データセットの種類別）
  • データの整合性
  • データの最新性と更新頻度
  • データの幅と深さ
  • データの有用性
  • データの経時性
  • 付加価値サービス
• データ販売者としての薬局：スコアマトリックス
• 医薬品サプライチェーンセキュリティデータ（DSCSA）市場：（タイプ1セグメント）
  • 医薬品サプライチェーンセキュリティデータ（DSCSA）市場推計・予測、2018年～2030年
  • DSCSAデータ- 購入者タイプ別の市場予測：（タイプ 2セグメント）
• マーケットバスケットデータ市場：（タイプ1セグメント）
  • マーケットバスケットデータ市場推計・予測、2018年～2030年
  • マーケットバスケットデータ- 購入者タイプ別の市場期待：（タイプ2セグメント）
• 在庫データ市場：（タイプ1セグメント）
  • 在庫データ市場推計・予測、2018年～2030年
  • 在庫データ- 購入者タイプ別の市場予測：（タイプ 2セグメント）
• 事前承認データ市場：（タイプ1セグメント）
  • 事前承認データ市場推計・予測、2018年～2030年
  • 事前承認データ- 購入者タイプ別の市場予測：（タイプ 2セグメント）
• エピソード/薬局処方箋請求データ市場：（タイプ1セグメント）
  • エピソード/薬局処方箋請求データ市場推計・予測、2018年-2030年
  • エピソード/薬局処方箋請求データ- 購入者タイプ別の市場予測：（タイプ2セグメント）

第5章 競合情勢

• Participants'Overview
• 財務実績
  • 公開会社
  • 非公開会社
• 競合との比較分析とベンチマーク
  • CVSヘルス
  • ウォルマート
  • ウォルグリーン
  • ザ・クローガー社
  • アルバートソン
  • ユナイテッドヘルスグループ（オプタム）
  • ヒューマナ
  • ブライトスプリングヘルスサービス
  • ライトエイド株式会社
  • HEB LP
  • コストコホールセールコーポレーション
  • センテネ株式会社
  • コニンクリケ・アホールド・デレーズNV
  • オーロラ・ヘルスケア（アドボケイト・ヘルスの一部門）
  • ビッグYフーズ株式会社
  • ブルックシャー・ブラザーズ
  • ウェイクファーンフードコーポレーション
  • パブリックス
  • CUB（ユナイテッドナチュラルフーズ株式会社の子会社）
• 参入企業
• 企業市場シェア分析、2024年（％）
  • Dscsaデータセットによる企業市場シェア分析
  • マーケットバスケットデータデータセットによる企業市場シェア分析
  • 在庫データセットによる企業市場シェア分析
  • エピソードデータ/薬局処方箋請求データによる企業市場シェア分析
  • 事前承認による企業市場シェア分析
• 戦略マッピング
  • 新サービスの開始
  • パートナーシップとコラボレーション
  • 地域拡大
  • その他

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

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