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

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

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한글목차

시장 규모와 동향 :

미국의 소매 약국 비식별화 건강 데이터 시장 규모는 2024년에 29억 달러로 추계되며, 2025-2030년에 CAGR 7.88%로 성장할 것으로 예측됩니다. 이러한 성장은 주로 가치 기반 진료(VBC) 및 결과 기반 상환 모델의 지속적인 확대와 함께 실제 세계 증거(RWE) 및 실제 세계 데이터(RWD)에 대한 수요 증가로 인해 발생할 것으로 예측됩니다. 또한 의약품 공급망 보안법(DSCSA)에 대한 대응과 같은 유리한 규제적 조치들이 시장 확대에 더욱 박차를 가하고 있으며, VBC 모델의 빠른 도입은 의료 성과 평가, 가격 책정 및 인센티브 부여 방식을 재정의함으로써 미국 의료를 재구성하고 있습니다.

비식별화된 건강 데이터는 연구자들이 환자의 프라이버시를 보호하면서 대규모 데이터세트를 분석할 수 있으므로 임상 연구에 필수적입니다. 이 데이터는 개인의 신원을 훼손하지 않고 추세를 파악하고, 치료 효과를 평가하며, 집단 건강 연구를 지원합니다. 비식별화 데이터를 활용함으로써 연구자들은 연구 결과의 질을 높이고 의학 지식과 진료의 진보를 촉진할 수 있습니다.

예를 들어 2023년 4월 Philips와 매사추세츠공과대학(MIT) 의공학 및 과학 연구소(IMES)는 헬스케어 분야의 임상 연구와 AI 용도를 촉진하기 위해 강화된 중환자 치료 데이터세트를 공동 개발했습니다. 이 데이터세트에는 중환자실 환자의 비식별화된 데이터와 종합적인 임상 정보가 통합되어 있으며, 연구자와 교육자들이 중환자 치료에 대한 인사이트를 높이고 환자 결과를 개선할 수 있도록 돕습니다. 이 구상은 AI 기반 헬스케어 솔루션의 혁신을 촉진하여 보다 정확한 진단과 개인화된 치료에 기여합니다.

COVID-19와 관련된 치료 승인 건수와 긴급성으로 인해 비식별화 데이터에 대한 수요가 크게 증가했습니다. 지불자와 의료 서비스 프로바이더는 이러한 데이터세트를 활용하여 접근 경로를 간소화하고, 관리 워크플로우를 자동화하며, 신속한 의사결정을 지원하고 있습니다. 이러한 발전은 공중보건 응급상황에서 의료 서비스 제공의 마찰을 줄이기 위한 정책적 진전에도 영향을 미쳤습니다. 의약품 및 의료용품공급 부족이 확산되면서 약국 수준의 실시간 재고 데이터에 대한 가시성을 높여야 할 필요성이 부각되었습니다. 제약사, 도매업체, 헬스케어 기업 등 이해관계자들은 재고 부족을 사전에 관리하고 중요한 치료제에 대한 적시 접근을 보장하기 위해 예측 분석 및 AI 기반 재고 추적에 많은 투자를 하고 있습니다.

목차

제1장 조사 방법과 범위

제2장 개요

제3장 업계 전망 - 시장 변수, 동향, 범위

• 시장 계통 전망
  • 세계 시장 전망
• 시장 역학
  • 데이터세트 유형별 주요 촉진요인과 관련 인사이트 전망
  • 시장 성장 촉진요인 분석
  • 시장 성장 억제요인 분석
  • 시장 기회 분석
  • 시장이 해결해야 할 과제 분석
• 바이어 분석
• 규제 동향
• 미국의 소매 약국 비식별화 건강 데이터 시장(5 데이터세트 특유 - 소매 약국을 판매자로 대상) : 데이터세트 유형별, 레벨별, 가격결정 모델 상세
  • 의약품 공급망 보안 데이터(DSCSA) : (유형 1부문) 전체적인 레벨의 가격 설정 모델 구조와 관련 분석
  • 마켓 바스켓 데이터 : (유형 1 부문) 전체적인 레벨의 가격 설정 모델 구조와 관련 분석
  • 재고 데이터 : (유형 1 부문) 전체적인 레벨의 가격 설정 모델 구조와 관련 분석
  • 사전 승인 데이터 : (유형 1 부문) 전체적인 레벨의 가격 설정 모델 구조와 관련 분석
  • 에피소드 데이터/약국 처방전 청구 데이터 : (유형 1부문) 전체적인 레벨의 가격 설정 모델 구조와 관련 분석
• 업계 분석 툴
  • Porter's Five Forces 분석
  • PESTLE 분석
• 소매 약국 특유의 동향
• 기술의 진보
• COVID-19의 영향 분석

제4장 미국의 소매 약국 비식별화 건강 데이터 시장(5 데이터세트 특유 - 소매 약국을 판매자로 대상) : 데이터세트 유형 추정·동향 분석

• 부문 대시보드
• 미국의 소매 약국 익명화 건강 데이터 시장(5 데이터세트 특유 - 소매 약국이 판매자) : 데이터세트 유형 분석, 2024년 및 2030년
• 소매 약국 대응 비식별화 건강 데이터세트 : 기능 기대치와 프로바이더 참조 프랙티스(데이터세트 유형별)
  • 데이터 정합성
  • 데이터 최신 성과 & 업데이트 빈도
  • 데이터 폭과 깊이
  • 데이터 유용성
  • 데이터 시간 경과성
  • 부가가치 서비스
• 데이터 판매자로서의 약국 : 스코어 매트릭스
• 의약품 공급망 보안 데이터(DSCSA) 시장 : (유형 1부문)
  • 의약품 공급망 보안 데이터(DSCSA) 시장 추산·예측, 2018-2030년
  • DSCSA 데이터 - 구입자 유형별 시장 예측 : (유형 2 부문)
• 마켓 바스켓 데이터 시장 : (유형 1부문)
  • 마켓 바스켓 데이터 시장 추산·예측, 2018-2030년
  • 마켓 바스켓 데이터 - 구입자 유형별 시장 기대 : (유형 2 부문)
• 재고 데이터 시장 : (유형 1부문)
  • 재고 데이터 시장 추산·예측, 2018-2030년
  • 재고 데이터 - 구입자 유형별 시장 예측 : (유형 2 부문)
• 사전 승인 데이터 시장 : (유형 1부문)
  • 사전 승인 데이터 시장 추산·예측, 2018-2030년
  • 사전 승인 데이터 - 구입자 유형별 시장 예측 : (유형 2 부문)
• 에피소드/약국 처방전 청구 데이터 시장 : (유형 1부문)
  • 에피소드/약국 처방전 청구 데이터 시장 추산·예측, 2018-2030년
  • 에피소드/약국 처방전 청구 데이터 - 구입자 유형별 시장 예측 : (유형 2 부문)

제5장 경쟁 구도

• Participants'Overview
• 재무 실적
  • 공개 회사
  • 비공개 회사
• 경쟁사 비교 분석과 벤치마킹
  • CVS HEALTH
  • WALMART
  • WALGREENS
  • Walgreens Comparative Analysis Across Datasets (vs. Retail/Specialty
  • THE KROGER CO.
  • ALBERTSON
  • UNITEDHEALTH GROUP (OPTUM)
  • HUMANA
  • BRIGHTSPRING HEALTH SERVICES
  • RITE AID CORP
  • H-E-B LP
  • COSTCO WHOLESALE CORPORATION
  • CENTENE CORPORATION
  • KONINKLIJKE AHOLD DELHAIZE N.V.
  • AURORA HEALTH CARE (A PART OF ADVOCATE HEALTH)
  • BIG Y FOODS, INC.
  • BROOKSHIRE BROTHERS
  • WAKEFERN FOOD CORP.
  • PUBLIX
  • CUB (SUBSIDIARY OF UNITED NATURAL FOODS, INC.)
• 참여 기업
• 기업 시장 점유율 분석, 2024년(%)
  • Dscsa 데이터세트에 의한 기업 시장 점유율 분석
  • 마켓 바스켓 데이터 데이터세트에 의한 기업 시장 점유율 분석
  • 재고 데이터세트에 의한 기업 시장 점유율 분석
  • 에피소드 데이터/약국 처방전 청구 데이터에 의한 기업 시장 점유율 분석
  • 사전 승인에 의한 기업 시장 점유율 분석
• 전략 지도제작
  • 신서비스 개시
  • 파트너십과 협업
  • 지역 확대
  • 기타

KSA

영문 목차

영문목차

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