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市場調查報告書
商品編碼
2065228

醫療保健預測分析市場預測至2034年—按組件、部署模式、技術、應用、最終用戶和地區分類的全球分析

Healthcare Predictive Analytics Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球醫療保健預測分析市場預計將在 2026 年達到 168 億美元，到 2034 年達到 732 億美元，在預測期內以 20.2% 的複合年成長率成長。

醫療預測分析運用統計演算法、機器學習模型和先進的資料探勘技術，對醫療資料集進行分析，旨在預測未來的臨床事件、財務結果和營運績效。透過識別歷史和即時病患資料中的模式和相關性，這些解決方案能夠幫助醫療機構預測再入院率、預判病患病情惡化、識別高風險族群、最佳化資源分配、偵測詐欺行為並支持精準醫療計畫。

不斷發展的價值導向醫療模式給醫療機構帶來了壓力。

隨著計量型模式轉變為基於價值的補償模式的加速，醫療機構面臨越來越大的壓力，需要投資於預測分析能力，以識別高成本患者群體並開展有針對性的預防干預。責任醫療組織 (ACO)、一次性付款計劃和管理式醫療計劃都需要複雜的風險分層工具，以履行品質報告義務和對支付方的財務責任。能夠識別存在可預防住院、慢性病併發症或醫療服務中斷風險患者的預測模型，可以實現主動式護理管理，從而在改善治療效果的同時降低整體醫療成本。過度再入院和未能達到品質基準所帶來的經濟處罰，進一步凸顯了醫療機構投資預測分析能力的緊迫性。

模型可解釋性方面的挑戰以及臨床醫生對引入預測工具的信心障礙。

儘管預測分析工具在研究環境中展現了卓越的預測能力，但其在臨床實踐中的應用往往受到臨床醫生對演算法可解釋性和模型輸出臨床相關性的擔憂所限制。 「黑箱」機器學習預測缺乏透明的解釋性證據，因此常被接受實證臨床推理訓練而非統計模式識別訓練的醫生所質疑。警報疲勞是另一個相關挑戰，因為過多的預測警報會加重臨床工作流程的負擔，降低醫生對更具實用性、優先順序更高的預測結果的關注。醫療機構若要實施預測分析，必須大力投資於臨床醫師培訓、模型解釋工具（例如SHAP解釋）以及工作流程整合設計，才能達到必要的採用率，以便充分發揮已實施預測模型的臨床和營運價值。

將預測分析應用於醫藥供應鏈韌性與庫存最佳化

預測分析不僅在臨床應用領域，在醫療供應鏈管理、採購最佳化和藥品庫存管理等領域也日益受到關注。醫療系統和藥品福利管理機構正在實施需求預測模型，這些模型基於患者群體分析和外部市場數據，預測藥品消費模式、醫療設備使用率和供應鏈中斷風險。疫情暴露出的供應鏈脆弱性凸顯了醫療採購系統在缺乏預測性可視性方面的營運缺陷，促使經營團隊強烈希望投資於該領域的分析技術。將預測性供應鏈分析與電子健康記錄和臨床決策支援平台相結合，正在建立一個互聯互通的營運智慧環境，從而同時最佳化醫療服務的臨床和物流環節。

訓練資料品質的限制以及預測模型效能隨時間推移而下降。

醫療分析模型的預測準確性從根本上取決於模型開發中所用訓練資料的品質、完整性和代表性。缺失值、文件不一致、編碼差異以及患者群體隨時間的變化都會逐漸降低模型的預測性能，導致風險分層不準確，並可能造成臨床資源錯配或漏診高風險患者。建立一套系統的模型監控、調整流程和管治框架來偵測和修正效能波動，在操作上十分複雜且耗費資源。對於在多個臨床和營運領域管理眾多已部署預測模型的醫療機構而言，這一點尤其突出。

新冠疫情的影響：

新冠疫情凸顯了預測分析在醫療緊急準備中的關鍵作用，大大加速了對醫院容量預測、病患病情惡化預測和資源需求建模平台的投資。在疫情爆發前就已部署預測分析能力的醫療系統，在應對需求激增、最佳化人工呼吸器和重症監護病床的分配以及在危機高峰期識別高風險患者並進行針對性干預方面，都處於更為有利的地位。各國政府和公共衛生機構對流行病學預測建模平台的投資也顯著增加。

在預測期內，臨床分析應用領域預計將佔據最大的市場佔有率。

在預測期內，臨床分析應用領域預計將佔據最大的市場佔有率。這主要得益於預測性臨床智慧在提供以價值為導向的醫療保健、提升病人安全以及促進實證社區健康管理方面發揮的基礎性作用。醫院和綜合醫療網路正在部署臨床預測模型，用於評估再入院風險、早期預警膿毒症、預測手術併發症以及管理慢性疾病。隨著人工智慧驅動的臨床決策支援以及與電子健康記錄記錄工作流程的整合不斷推進，預測分析正被大規模地融入日常臨床實踐中。

在預測期內，精準醫療應用領域預計將呈現最高的複合年成長率。

在預測期內，精準醫療應用領域預計將呈現最高的成長率，這主要得益於基因組數據、真實世界數據和先進機器學習演算法的整合，從而實現前所未有的個人化治療。整合多組體學數據以及臨床和數位生物標記數據的預測模型，正在支持腫瘤學、循環系統和罕見疾病計畫中更精準的患者分層、藥物反應預測以及基於生物標記的治療方法選擇。製藥公司對伴隨診斷項目和標靶治療研發的投入，正在推動對先進預測分析平台的需求。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率。這主要得益於該地區完善的價值導向型醫療保健基礎設施、高密度部署的數據豐富的整合醫療保健系統，以及提供企業級預測分析平台的成熟供應商生態系統。在美國，大規模的健康保險計劃和醫院在風險分層、護理管理和品質改進分析項目方面的投資，是該地區佔據主導地位的關鍵因素。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於醫療系統的快速數位化、政府對國家醫療智慧平台的投資，以及人們日益認知到預測分析是提升醫療系統效率的有效手段。該地區龐大的患者群體，加上電子健康記錄的廣泛應用和對醫療數據互通性的投資，正在建立一個豐富的分析數據環境，為臨床、營運和製藥領域採用先進的預測模型提供了支持。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球醫療保健預測分析市場：按組件分類

  • 軟體
    • 臨床分析軟體
    • 財務分析軟體
    • 營運分析軟體
    • 人口健康分析軟體
  • 硬體
  • 服務

第6章：全球醫療保健預測分析市場：依部署模式分類

  • 現場
  • 基於雲端的
  • 混合實現

第7章 全球醫療保健預測分析市場：依技術分類

  • 人工智慧（AI）
  • 機器學習（ML）
  • 自然語言處理（NLP）
  • 巨量資料分析
  • 資料探勘
  • 預測建模

第8章 全球醫療保健預測分析市場：按應用領域分類

  • 臨床分析
  • 財務分析
  • 營運分析
  • 團體健康管理
  • 精準醫療
  • 慢性病管理

第9章：全球醫療保健預測分析市場：依最終用戶分類

  • 醫院和醫療系統
  • 醫療保健支付方
  • 製藥和生物技術公司
  • 診斷檢查室
  • 門診部
  • 政府和公共衛生機構

第10章：全球醫療保健預測分析市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • IBM
  • Oracle Corporation
  • SAS Institute Inc.
  • Optum Inc.
  • Veradigm
  • Health Catalyst
  • Epic Systems Corporation
  • Medtronic plc
  • McKesson Corporation
  • Cognizant
  • Change Healthcare
  • Philips
  • Cerner Corporation
  • NXGN Management, LLC
  • Inovalon Holdings, Inc.
Product Code: SMRC36778

According to Stratistics MRC, the Global Healthcare Predictive Analytics Market is accounted for $16.8 billion in 2026 and is expected to reach $73.2 billion by 2034, growing at a CAGR of 20.2% during the forecast period. Healthcare Predictive Analytics encompasses the application of statistical algorithms, machine learning models, and advanced data mining techniques to healthcare datasets for the purpose of forecasting future clinical events, financial outcomes, and operational conditions. By identifying patterns and correlations within historical and real-time patient data, these solutions enable healthcare organizations to anticipate readmissions, predict patient deterioration, identify high-risk populations, optimize resource allocation, detect fraud, and support precision medicine programs.

Market Dynamics:

Driver:

Expanding value-based care models compelling healthcare organizations

The accelerating shift from fee-for-service to value-based reimbursement models is compelling healthcare organizations to invest in predictive analytics capabilities that identify high-cost patient populations and enable targeted pre-emptive interventions. Accountable care organizations, bundled payment programs, and managed care plans require sophisticated risk stratification tools to fulfil quality reporting obligations and demonstrate financial stewardship to payers. Predictive models identifying patients at risk of preventable hospitalizations, chronic disease complications, or care gaps are enabling proactive care management outreach that improves outcomes while reducing total cost of care. The financial penalties associated with excess readmissions and quality benchmark failures further reinforce the organizational imperative to invest in predictive analytics capabilities.

Restraint:

Model interpretability challenges and clinician trust barriers to predictive tool adoption

Despite demonstrated predictive performance in research settings, the adoption of predictive analytics tools in clinical practice is frequently constrained by clinician concerns about algorithm interpretability and the clinical coherence of model outputs. Black-box machine learning predictions lacking transparent explanatory rationale are often viewed with skepticism by physicians who are trained in evidence-based clinical reasoning rather than statistical pattern recognition. Alert fatigue is a related challenge, as dense predictive alert systems can overwhelm clinical workflows and reduce engagement with actionable high-priority predictions. Healthcare organizations implementing predictive analytics must invest substantially in clinician education, model interpretability tools such as SHAP explanations, and workflow integration design to achieve the adoption rates necessary to realize the clinical and operational value of deployed predictive models.

Opportunity:

Application of predictive analytics to pharmaceutical supply chain resilience and inventory optimization

Predictive analytics is gaining traction beyond clinical applications in healthcare supply chain management, procurement optimization, and pharmaceutical inventory control. Health systems and pharmacy benefit managers are deploying demand forecasting models that predict medication consumption patterns, device utilization rates, and supply chain disruption risks based on patient population analytics and external market data. Pandemic-driven supply chain vulnerabilities highlighted the operational fragility of healthcare procurement systems operating without predictive visibility, creating strong executive motivation for analytics investment in this domain. The integration of predictive supply chain analytics with electronic health records and clinical decision support platforms is creating interconnected operational intelligence environments that simultaneously optimize clinical and logistical dimensions of healthcare delivery.

Threat:

Training data quality limitations and predictive model performance degradation over time

The predictive accuracy of healthcare analytics models is fundamentally dependent on the quality, completeness, and representativeness of the training data used in model development. Missing values, documentation inconsistencies, coding variability, and patient population shifts over time can progressively erode model predictive performance, leading to inaccurate risk stratifications that misallocate clinical resources or miss high-risk patients. Establishing systematic model monitoring, recalibration pipelines, and governance frameworks that detect and address performance drift is operationally complex and resource-intensive, particularly for healthcare organizations managing large portfolios of deployed predictive models across multiple clinical and operational domains.

Covid-19 Impact:

The COVID-19 pandemic demonstrated the essential role of predictive analytics in healthcare emergency preparedness, dramatically accelerating investment in hospital capacity forecasting, patient deterioration prediction, and resource demand modeling platforms. Health systems that had deployed predictive analytics capabilities prior to the pandemic were significantly better positioned to manage surge capacity, optimize ventilator and ICU bed allocation, and identify high-risk patients for targeted intervention during peak crisis periods. Government and public health agency investment in epidemiological predictive modeling platforms expanded substantially.

The clinical analytics application segment is expected to be the largest during the forecast period

The clinical analytics application segment is expected to account for the largest market share during the forecast period, driven by the foundational role of predictive clinical intelligence in enabling value-based care delivery, patient safety improvement, and evidence-based population health management. Hospitals and integrated delivery networks are deploying clinical predictive models for readmission risk stratification, sepsis early warning, surgical complication prediction, and chronic disease management. The growing integration of AI-powered clinical decision support with electronic health record workflows is embedding predictive analytics into routine clinical practice at scale.

The precision medicine application segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the precision medicine application segment is predicted to witness the highest growth rate, fueled by the convergence of genomic data, real-world evidence, and advanced machine learning algorithms that are enabling unprecedented levels of therapeutic personalization. Predictive models integrating multi-omics data with clinical and digital biomarker streams are supporting more accurate patient stratification, drug response prediction, and biomarker-guided treatment selection across oncology, cardiology, and rare disease programs. Pharmaceutical company investment in companion diagnostic programs and targeted therapy development is driving demand for sophisticated predictive analytics platforms.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by the region's extensive value-based care infrastructure, high density of data-rich integrated health systems, and sophisticated vendor ecosystem offering enterprise-grade predictive analytics platforms. The United States drives regional dominance through large health plan and hospital investment in risk stratification, care management, and quality improvement analytics programs.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapidly digitizing health systems, government investment in national health intelligence platforms, and growing recognition of predictive analytics as a healthcare system efficiency enabler. The scale of the regional patient population, combined with expanding electronic health record adoption and health data interoperability investments, is creating rich analytical data environments that will support sophisticated predictive modeling deployments across clinical, operational, and pharmaceutical applications.

Key players in the market

Some of the key players in Healthcare Predictive Analytics Market include IBM, Oracle Corporation, SAS Institute Inc., Optum Inc., Veradigm, Health Catalyst, Epic Systems Corporation, Medtronic plc, McKesson Corporation, Cognizant, Change Healthcare, Philips, Cerner Corporation, NXGN Management LLC, and Inovalon Holdings Inc.

Key Developments:

In March 2026, IBM announced the launch of an enhanced IBM Watson Health predictive analytics suite incorporating new large language model-powered clinical risk summarization capabilities designed for hospital care management and population health programs. The updated platform provides AI-generated narrative risk explanations alongside quantitative risk scores, targeting improved clinician engagement with predictive alert outputs across integrated health system deployments.

In January 2026, Optum Inc. announced the expansion of its predictive analytics platform with new pharmaceutical adherence risk models designed for specialty pharmacy and prescription drug plan operators. The models integrate claims, clinical, and behavioral data to predict patients at high risk of medication non-adherence, enabling targeted pharmacy care management outreach programs that aim to improve clinical outcomes and reduce total healthcare costs.

Components Covered:

  • Software
  • Hardware
  • Services

Deployment Modes Covered:

  • On-Premise
  • Cloud-Based
  • Hybrid Deployment

Technologies Covered:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Big Data Analytics
  • Data Mining
  • Predictive Modeling

Applications Covered:

  • Clinical Analytics
  • Financial Analytics
  • Operational Analytics
  • Population Health Management
  • Precision Medicine
  • Chronic Disease Management

End Users Covered:

  • Hospitals & Health Systems
  • Healthcare Payers
  • Pharmaceutical & Biotechnology Companies
  • Diagnostic Laboratories
  • Ambulatory Care Centers
  • Government & Public Health Agencies

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Healthcare Predictive Analytics Market, By Component

  • 5.1 Software
    • 5.1.1 Clinical Analytics Software
    • 5.1.2 Financial Analytics Software
    • 5.1.3 Operational Analytics Software
    • 5.1.4 Population Health Analytics Software
  • 5.2 Hardware
  • 5.3 Services

6 Global Healthcare Predictive Analytics Market, By Deployment Mode

  • 6.1 On-Premise
  • 6.2 Cloud-Based
  • 6.3 Hybrid Deployment

7 Global Healthcare Predictive Analytics Market, By Technology

  • 7.1 Artificial Intelligence (AI)
  • 7.2 Machine Learning (ML)
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Big Data Analytics
  • 7.5 Data Mining
  • 7.6 Predictive Modeling

8 Global Healthcare Predictive Analytics Market, By Application

  • 8.1 Clinical Analytics
  • 8.2 Financial Analytics
  • 8.3 Operational Analytics
  • 8.4 Population Health Management
  • 8.5 Precision Medicine
  • 8.6 Chronic Disease Management

9 Global Healthcare Predictive Analytics Market, By End User

  • 9.1 Hospitals & Health Systems
  • 9.2 Healthcare Payers
  • 9.3 Pharmaceutical & Biotechnology Companies
  • 9.4 Diagnostic Laboratories
  • 9.5 Ambulatory Care Centers
  • 9.6 Government & Public Health Agencies

10 Global Healthcare Predictive Analytics Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 IBM
  • 13.2 Oracle Corporation
  • 13.3 SAS Institute Inc.
  • 13.4 Optum Inc.
  • 13.5 Veradigm
  • 13.6 Health Catalyst
  • 13.7 Epic Systems Corporation
  • 13.8 Medtronic plc
  • 13.9 McKesson Corporation
  • 13.10 Cognizant
  • 13.11 Change Healthcare
  • 13.12 Philips
  • 13.13 Cerner Corporation
  • 13.14 NXGN Management, LLC
  • 13.15 Inovalon Holdings, Inc.

List of Tables

  • Table 1 Global Healthcare Predictive Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Healthcare Predictive Analytics Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Healthcare Predictive Analytics Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global Healthcare Predictive Analytics Market Outlook, By Clinical Analytics Software (2023-2034) ($MN)
  • Table 5 Global Healthcare Predictive Analytics Market Outlook, By Financial Analytics Software (2023-2034) ($MN)
  • Table 6 Global Healthcare Predictive Analytics Market Outlook, By Operational Analytics Software (2023-2034) ($MN)
  • Table 7 Global Healthcare Predictive Analytics Market Outlook, By Population Health Analytics Software (2023-2034) ($MN)
  • Table 8 Global Healthcare Predictive Analytics Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 9 Global Healthcare Predictive Analytics Market Outlook, By Services (2023-2034) ($MN)
  • Table 10 Global Healthcare Predictive Analytics Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 11 Global Healthcare Predictive Analytics Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 12 Global Healthcare Predictive Analytics Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 13 Global Healthcare Predictive Analytics Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 14 Global Healthcare Predictive Analytics Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global Healthcare Predictive Analytics Market Outlook, By Artificial Intelligence (AI) (2023-2034) ($MN)
  • Table 16 Global Healthcare Predictive Analytics Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 17 Global Healthcare Predictive Analytics Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 18 Global Healthcare Predictive Analytics Market Outlook, By Big Data Analytics (2023-2034) ($MN)
  • Table 19 Global Healthcare Predictive Analytics Market Outlook, By Data Mining (2023-2034) ($MN)
  • Table 20 Global Healthcare Predictive Analytics Market Outlook, By Predictive Modeling (2023-2034) ($MN)
  • Table 21 Global Healthcare Predictive Analytics Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global Healthcare Predictive Analytics Market Outlook, By Clinical Analytics (2023-2034) ($MN)
  • Table 23 Global Healthcare Predictive Analytics Market Outlook, By Financial Analytics (2023-2034) ($MN)
  • Table 24 Global Healthcare Predictive Analytics Market Outlook, By Operational Analytics (2023-2034) ($MN)
  • Table 25 Global Healthcare Predictive Analytics Market Outlook, By Population Health Management (2023-2034) ($MN)
  • Table 26 Global Healthcare Predictive Analytics Market Outlook, By Precision Medicine (2023-2034) ($MN)
  • Table 27 Global Healthcare Predictive Analytics Market Outlook, By Chronic Disease Management (2023-2034) ($MN)
  • Table 28 Global Healthcare Predictive Analytics Market Outlook, By End User (2023-2034) ($MN)
  • Table 29 Global Healthcare Predictive Analytics Market Outlook, By Hospitals & Health Systems (2023-2034) ($MN)
  • Table 30 Global Healthcare Predictive Analytics Market Outlook, By Healthcare Payers (2023-2034) ($MN)
  • Table 31 Global Healthcare Predictive Analytics Market Outlook, By Pharmaceutical & Biotechnology Companies (2023-2034) ($MN)
  • Table 32 Global Healthcare Predictive Analytics Market Outlook, By Diagnostic Laboratories (2023-2034) ($MN)
  • Table 33 Global Healthcare Predictive Analytics Market Outlook, By Ambulatory Care Centers (2023-2034) ($MN)
  • Table 34 Global Healthcare Predictive Analytics Market Outlook, By Government & Public Health Agencies (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.