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醫療保健市場的預測分析 - 全球行業規模、佔有率、趨勢、機會和預測，按應用程式、組件、最終用戶、部署模式、地區、競爭細分，2019-2029F

Predictive Analytics in Healthcare Market - Global Industry Size, Share, Trends, Opportunity and Forecast, Segmented By Application, By Component, By End User, By Deployment Mode, By Region, By Competition, 2019-2029F
出版日期: | 出版商: TechSci Research | 英文 183 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2023 年全球醫療保健市場預測分析價值為130.1 億美元，預計到2029 年預測期內將實現強勁成長，複合年成長率為17.32%。了醫療保健領域擴大採用先進技術。預測分析涉及使用統計演算法和機器學習技術來分析歷史和當前資料，從而預測未來的結果。

在醫療保健領域，預測分析在增強病患照護、簡化營運和提高成本效率方面具有巨大潛力。該市場的成長受到幾個關鍵因素的推動，包括對個人化醫療的需求不斷增加、慢性病發病率上升以及有效的醫療管理解決方案的必要性。預測分析使醫療保健提供者能夠預測患者的健康風險、識別潛在的併發症並相應地客製化治療計劃，從而改善結果並提高患者滿意度。此外，預測分析與電子健康記錄 (EHR) 和其他醫療保健 IT 系統的無縫整合簡化了資料分析和決策流程。

此外，市場受益於穿戴式裝置、基因組學和健康社會決定因素等不同來源的醫療保健資料的不斷增加。然而，資料安全問題、互通性問題和熟練專業人員短缺等挑戰可能會在一定程度上阻礙市場成長。儘管如此，人工智慧 (AI)、巨量資料分析和雲端運算的不斷進步預計將推動醫療保健預測分析解決方案的持續創新。因此，醫療保健市場的全球預測分析預計將在可預見的未來大幅擴展，為供應商提供開發客製化解決方案的機會，以滿足全球醫療保健組織不斷變化的需求。

主要市場促進因素

慢性病盛行率上升

醫療保健 IT 解決方案的採用率不斷提高

人工智慧和巨量資料分析的技術進步

主要市場挑戰

資料安全問題

互通性挑戰

缺乏熟練的專業人員

主要市場趨勢

精準醫學的出現

轉向基於價值的護理

細分市場洞察

應用洞察

組件洞察

區域洞察

目錄

第 1 章：產品概述

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：醫療保健市場前景的全球預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按應用（臨床決策診斷支援 (CDS)、風險預測評分、需求預測、藥物發現、疾病癌症檢測、詐欺檢測等）
    • 按組件（硬體、軟體、服務）
    • 按最終用戶（醫療保健提供者、醫療保健付款人、其他）
    • 依部署模式（本地、雲端）
    • 按公司分類 (2023)
    • 按地區
  • 市場地圖

第 6 章：北美醫療保健市場前景預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按申請
    • 按組件
    • 按最終用戶
    • 按部署模式
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 墨西哥
    • 加拿大

第 7 章：歐洲醫療保健市場前景預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按申請
    • 按組件
    • 按最終用戶
    • 按部署模式
    • 按國家/地區
  • 歐洲：國家分析
    • 法國
    • 德國
    • 英國
    • 義大利
    • 西班牙

第 8 章：亞太地區醫療保健市場前景預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按申請
    • 按組件
    • 按最終用戶
    • 按部署模式
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 韓國
    • 日本
    • 澳洲

第 9 章：南美洲醫療保健市場前景預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按申請
    • 按組件
    • 按最終用戶
    • 按部署模式
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲醫療保健市場前景預測分析

  • 市場規模預測
    • 按價值
  • 市佔率預測
    • 按申請
    • 按組件
    • 按最終用戶
    • 按部署模式
    • 按國家/地區
  • MEA：國家分析
    • 南非
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 埃及
    • 土耳其

第 11 章：市場動態

  • 促進要素
  • 挑戰

第 12 章：市場趨勢發展

  • 併購（如有）
  • 產品發布（如有）
  • 最近的發展

第 13 章：波特五力分析

  • 產業競爭
  • 新進入者的潛力
  • 供應商的力量
  • 客戶的力量
  • 替代產品的威脅

第14章：競爭格局

  • International Business Machines Corporation
  • Unitedhealth Group.
  • Oracle Cerner
  • Microsoft Corporation
  • Veradigm LLC
  • Verisk Analytics, Inc
  • MedeAnalytics, Inc.
  • Cloud Software Group, Inc.
  • SAS Institute, Inc.
  • Health Catalyst

第 15 章：策略建議

第 16 章：關於我們免責聲明

簡介目錄
Product Code: 7856

Global Predictive Analytics in Healthcare Market was valued at USD 13.01 Billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 17.32% through 2029. The global predictive analytics in healthcare market has witnessed remarkable growth in recent years, propelled by the increasing adoption of advanced technologies in the healthcare sector. Predictive analytics involves the use of statistical algorithms and machine learning techniques to analyze historical and current data, thereby predicting future outcomes.

In the healthcare sector, predictive analytics presents significant potential for enhancing patient care, streamlining operations, and driving cost efficiencies. This market's growth is spurred by several key factors, including the increasing demand for personalized medicine, the rising incidence of chronic diseases, and the necessity for effective healthcare management solutions. Predictive analytics empowers healthcare providers to anticipate patient health risks, identify potential complications, and customize treatment plans accordingly, resulting in improved outcomes and heightened patient satisfaction. Additionally, the seamless integration of predictive analytics with electronic health records (EHRs) and other healthcare IT systems has streamlined data analysis and decision-making processes.

Furthermore, the market benefits from the growing availability of healthcare data from diverse sources such as wearable devices, genomics, and social determinants of health. However, challenges like data security issues, interoperability concerns, and a shortage of skilled professionals may somewhat hinder market growth. Nonetheless, ongoing advancements in artificial intelligence (AI), big data analytics, and cloud computing are expected to fuel continued innovation in predictive analytics solutions for healthcare. Consequently, the global predictive analytics in healthcare market is poised for substantial expansion in the foreseeable future, presenting vendors with opportunities to develop tailored solutions that meet the evolving needs of healthcare organizations worldwide.

Key Market Drivers

Rising Prevalence of Chronic Diseases

The increasing global prevalence of chronic diseases serves as a significant catalyst driving the expansion of predictive analytics within the healthcare market. Conditions like diabetes, cardiovascular diseases, cancer, and respiratory disorders present formidable challenges to healthcare systems worldwide, contributing to rising healthcare expenditures and straining healthcare resources. With factors such as aging populations, sedentary lifestyles, and poor dietary habits fueling the surge in these conditions, there is a growing urgency to implement effective strategies for their management and prevention.

Predictive analytics emerges as a potent solution in this pursuit, empowering healthcare providers to anticipate disease progression, pinpoint high-risk individuals, and tailor interventions to mitigate risks and complications. Through the analysis of extensive patient data encompassing demographics, medical history, and lifestyle elements, predictive analytics generates actionable insights that inform preventive care strategies and personalized treatment protocols. For instance, predictive models can flag individuals at risk of developing diabetes based on factors like body mass index, blood glucose levels, and familial medical history, enabling healthcare providers to implement targeted interventions such as lifestyle adjustments, dietary modifications, and preemptive screenings to curb disease incidence.

By facilitating early detection and intervention, predictive analytics empowers healthcare providers to intervene during the initial stages of disease development, when interventions are most impactful and cost-effective. Leveraging predictive analytics, healthcare organizations can adopt proactive approaches to chronic disease management, including remote patient monitoring, telehealth interventions, and personalized health coaching. These initiatives not only enhance patient outcomes and quality of life but also optimize resource allocation and healthcare expenditures.

Predictive analytics equips healthcare providers with the tools to refine population health management strategies by discerning trends, patterns, and risk factors across patient demographics. Through the analysis of population-level data, predictive analytics informs the development of public health initiatives, disease prevention programs, and health promotion campaigns aimed at mitigating the impact of chronic diseases on society.

The increasing prevalence of chronic diseases underscores the urgent necessity for innovative solutions to enhance disease management and prevention efforts. Predictive analytics emerges as a valuable asset in this pursuit, harnessing data-driven insights to shape proactive strategies for chronic disease management, personalized interventions, and initiatives in population health management. With the persistent rise in chronic disease burdens, the demand for predictive analytics in healthcare is poised to escalate, propelling further innovation and adoption within the global healthcare market.

Increasing Adoption of Healthcare IT Solutions

The growing adoption of healthcare IT solutions is a driving force behind the expansion of predictive analytics within the healthcare market. Across the globe, healthcare organizations are embracing digital transformation initiatives to elevate patient care, enhance operational efficiency, and streamline clinical workflows. This shift towards digitalization places a significant emphasis on harnessing cutting-edge technologies, such as electronic health records (EHRs), telemedicine platforms, and digital health applications, to gather, store, and analyze extensive volumes of patient data.

Predictive analytics seamlessly integrates with healthcare IT solutions, empowering healthcare providers to extract actionable insights from the abundance of data generated across various touchpoints within the healthcare ecosystem. Leveraging predictive analytics capabilities embedded within EHR systems, healthcare providers can tap into historical patient data, clinical notes, diagnostic tests, and treatment outcomes to uncover patterns, trends, and risk factors associated with specific diseases and patient demographics. This enables healthcare organizations to anticipate patient health risks, forecast disease progression, and tailor personalized treatment plans to meet individual patient needs.

The adoption of telemedicine platforms and remote monitoring technologies further drives the demand for predictive analytics in healthcare. These solutions enable healthcare providers to gather real-time patient data from remote locations, including home-based monitoring devices and wearable sensors, facilitating continuous monitoring and early detection of health issues. Predictive analytics algorithms analyze streaming data from these sources to identify deviations from baseline health parameters, trigger alerts for potential health risks, and enable timely interventions to prevent adverse outcomes.

Healthcare IT solutions facilitate interoperability and data exchange among disparate systems and stakeholders, enabling the seamless integration of predictive analytics into existing healthcare workflows. Through standardized data formats and interoperability standards, healthcare organizations can aggregate data from multiple sources, including EHRs, laboratory systems, imaging systems, and wearable devices, to construct comprehensive patient profiles for predictive modeling and analysis.

Technological Advancements in AI and Big Data Analytics

Technological advancements in artificial intelligence (AI) and big data analytics are catalyzing the growth of the global predictive analytics market in healthcare, revolutionizing how patient care is delivered, managed, and optimized. AI algorithms and big data analytics techniques empower healthcare organizations to unlock insights from vast and diverse datasets, facilitating more accurate predictions, personalized interventions, and improved patient outcomes.

AI-driven predictive analytics solutions leverage machine learning algorithms to analyze complex healthcare data, including electronic health records (EHRs), medical imaging, genomics, and real-time patient monitoring data. These algorithms can identify patterns, correlations, and hidden insights within large datasets, enabling healthcare providers to predict disease onset, progression, and treatment response with unprecedented accuracy. For example, AI-powered predictive analytics can analyze medical imaging data to detect early signs of diseases such as cancer, enabling timely interventions and improving patient survival rate.

The integration of big data analytics into predictive analytics solutions enhances scalability, performance, and data processing capabilities. Big data technologies enable healthcare organizations to store, manage, and analyze massive volumes of structured and unstructured data generated from diverse sources, including medical devices, wearables, social media, and population health databases. By harnessing big data analytics platforms, healthcare providers can gain deeper insights into population health trends, epidemiological patterns, and disease outbreaks, facilitating proactive interventions and public health initiatives.

Advancements in AI and big data analytics are driving innovation in predictive modeling techniques, enabling the development of more sophisticated predictive analytics algorithms. Deep learning algorithms, a subset of AI, mimic the human brain's neural networks and can process complex data structures, such as images, text, and time-series data, with remarkable accuracy. In healthcare, deep learning-based predictive analytics models are used for tasks such as medical image analysis, drug discovery, and personalized treatment recommendations, enhancing clinical decision-making and patient care.

Key Market Challenges

Data Security Concerns

One of the primary challenges hindering the global predictive analytics in healthcare market is data security concerns. Healthcare organizations handle sensitive patient data, including medical records, diagnostic tests, and treatment histories, which are subject to strict privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Protecting patient privacy and ensuring data security are paramount concerns for healthcare providers, as any breach or unauthorized access to patient information can have severe consequences, including legal and financial penalties, reputational damage, and loss of patient trust. The integration of predictive analytics requires robust data security measures, including encryption, access controls, and data anonymization techniques, to safeguard patient confidentiality and comply with regulatory requirements.

Interoperability Challenges

Interoperability challenges pose significant barriers to the adoption and implementation of predictive analytics in healthcare. Healthcare data is often fragmented across disparate systems, including electronic health records (EHRs), laboratory information systems, imaging systems, and wearable devices, making it difficult to aggregate, integrate, and analyze data from multiple sources. Lack of interoperability hampers data sharing and collaboration among healthcare stakeholders, limiting the effectiveness of predictive analytics in generating actionable insights. Addressing interoperability challenges requires investment in interoperability standards, data exchange protocols, and interoperable IT infrastructure to enable seamless integration of predictive analytics into existing healthcare workflows.

Shortage of Skilled Professionals

A shortage of skilled professionals, including data scientists, statisticians, and healthcare informaticians, poses a significant challenge to the global predictive analytics in healthcare market. Developing and deploying predictive analytics solutions require interdisciplinary expertise in data science, healthcare domain knowledge, and statistical modeling techniques. However, there is a growing demand for these specialized skills in the healthcare industry, outpacing the supply of qualified professionals. Moreover, healthcare organizations face challenges in recruiting and retaining talent with the necessary skills and experience to develop and implement predictive analytics solutions effectively. Addressing the shortage of skilled professionals requires investment in workforce training and education programs, collaboration with academic institutions, and fostering a culture of data-driven decision-making within healthcare organizations.

Key Market Trends

Emergence of Precision Medicine

The emergence of precision medicine is revolutionizing healthcare delivery and significantly boosting the global predictive analytics market in healthcare. Precision medicine represents a paradigm shift in healthcare, focusing on personalized treatments tailored to individual patient characteristics, including genetic makeup, biomarkers, and lifestyle factors. This approach recognizes that patients with the same diagnosis may respond differently to treatments based on their unique genetic profiles and environmental influences.

Predictive analytics plays a crucial role in precision medicine by leveraging advanced algorithms and machine learning techniques to analyze vast amounts of patient data and predict treatment responses with unprecedented accuracy. By analyzing genomic data, electronic health records (EHRs), medical imaging, and other patient data sources, predictive analytics can identify patterns, correlations, and predictive insights to inform personalized treatment plans. One of the key advantages of predictive analytics in precision medicine is its ability to identify biomarkers and genetic mutations associated with disease susceptibility, treatment efficacy, and adverse drug reactions. By analyzing genomic profiles, predictive analytics can predict disease risk, recommend targeted therapies, and optimize treatment regimens tailored to individual patient needs. This enables healthcare providers to deliver more effective treatments, minimize adverse effects, and improve patient outcomes.

Predictive analytics facilitates proactive risk assessment and early intervention, enabling healthcare providers to identify high-risk individuals and intervene before diseases progress to advanced stages. By analyzing patient data in real-time, predictive analytics can identify subtle changes in health parameters and trigger alerts for potential health risks, facilitating timely interventions and preventive measures.

Shift Towards Value-Based Care

The global healthcare landscape is undergoing a significant transformation with a shift towards value-based care models, and this trend is notably boosting the adoption of predictive analytics in healthcare. Value-based care models prioritize the quality of patient outcomes over the volume of services provided, incentivizing healthcare providers to deliver efficient, cost-effective care that focuses on prevention, early intervention, and coordinated management of chronic conditions. Predictive analytics plays a crucial role in enabling value-based care by providing actionable insights derived from vast datasets, including electronic health records (EHRs), claims data, and patient-generated data. By leveraging advanced algorithms and machine learning techniques, predictive analytics can identify high-risk patients, predict adverse events, and recommend personalized interventions to improve patient outcomes while reducing healthcare costs.

One of the key advantages of predictive analytics in value-based care is its ability to support population health management initiatives. By analyzing patient data at the population level, predictive analytics can identify trends, patterns, and risk factors that contribute to poor health outcomes. Healthcare providers can use this information to target interventions, allocate resources effectively, and implement preventive strategies to improve the health of their patient populations.

Predictive analytics enables healthcare organizations to optimize care coordination and resource utilization, two essential components of value-based care delivery. By identifying patients who are at risk of hospital readmissions or complications, predictive analytics can help healthcare providers intervene proactively, ensuring that patients receive the appropriate level of care at the right time and place. This proactive approach not only improves patient outcomes but also reduces unnecessary healthcare expenditures associated with preventable hospitalizations and emergency room visits.

Segmental Insights

Application Insights

Based on the application, Clinical Decision Diagnosis Support (CDS) segment emerged as the dominant segment in the global Predictive Analytics in Healthcare market in 2023.The dominance of the Clinical Decision Diagnosis Support (CDS) segment in the global predictive analytics in healthcare market in 2023 can be attributed to several key factors. Firstly, healthcare providers are increasingly recognizing the value of predictive analytics in improving clinical workflows, enhancing diagnostic accuracy, and optimizing treatment outcomes. The integration of predictive analytics into CDS systems enables healthcare providers to leverage data-driven insights to support clinical decision-making, streamline care delivery processes, and improve patient outcomes. Advancements in artificial intelligence (AI) and machine learning have significantly enhanced the capabilities of predictive analytics in clinical decision support. AI-driven CDS systems can analyze complex datasets, including medical imaging, genomic data, and real-time patient monitoring data, to generate personalized treatment recommendations tailored to individual patient characteristics and preferences.

Component Insights

Based on the component, software segment emerged as the dominant segment in the global Predictive Analytics in Healthcare market in 2023.The dominance of the Software segment in the global predictive analytics in healthcare market in 2023 is primarily due to the growing demand for advanced analytics software solutions capable of leveraging artificial intelligence (AI) and machine learning techniques to extract actionable insights from vast and complex healthcare datasets. Healthcare organizations are increasingly investing in predictive analytics software to enhance clinical decision-making, improve patient outcomes, and optimize operational efficiency. The Software segment benefits from ongoing technological advancements in AI, big data analytics, and cloud computing, which have significantly enhanced the capabilities and functionalities of predictive analytics software solutions. These advancements enable healthcare providers to leverage predictive analytics software to address a wide range of use cases, including clinical decision support, risk prediction, population health management, and personalized medicine.

Regional Insights

North America emerged as the dominant player in the Global Predictive Analytics in Healthcare Market in 2023, holding the largest market share. North America is home to a thriving ecosystem of technology companies, research institutions, and healthcare organizations at the forefront of innovation in predictive analytics and artificial intelligence (AI). Leading technology hubs such as Silicon Valley in the United States and Toronto in Canada serve as epicenters of research and development in healthcare analytics, driving the development of cutting-edge predictive analytics solutions tailored to the needs of healthcare providers and patients. North America benefits from strong government support and investment in healthcare innovation and digital health initiatives. Government agencies, such as the U.S. Food and Drug Administration (FDA) and Health Canada, provide regulatory oversight and guidance to ensure the safety, efficacy, and interoperability of predictive analytics solutions in healthcare settings.

Key Market Players

International Business Machines Corporation

Unitedhealth Group.

Oracle Cerner

Microsoft Corporation

Veradigm LLC

Verisk Analytics, Inc

MedeAnalytics, Inc.

Cloud Software Group, Inc.

SAS Institute, Inc.

Health Catalyst

Report Scope:

In this report, the Global Predictive Analytics in Healthcare Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Predictive Analytics in Healthcare Market,By Application:

  • Clinical Decision Diagnosis Support (CDS)
  • Risk Prediction Scoring
  • Demand Forecast
  • Drug Discovery
  • Disease Cancer Detection
  • Fraud Detection
  • Others

Predictive Analytics in Healthcare Market,By Component:

  • Hardware
  • Software
  • Services

Predictive Analytics in Healthcare Market,End User:

  • Healthcare Providers
  • Healthcare Payers
  • Others

Predictive Analytics in Healthcare Market,Deployment Mode:

  • On premises
  • Cloud

Predictive Analytics in Healthcare Market, By Region:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East Africa
    • South Africa
    • Saudi Arabia
    • UAE
    • Egypt
    • Turkey

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Predictive Analytics in Healthcare Market.

Available Customizations:

Global Predictive Analytics in Healthcare Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1.Product Overview

  • 1.1.Market Definition
  • 1.2.Scope of the Market
    • 1.2.1.Markets Covered
    • 1.2.2.Years Considered for Study
    • 1.2.3.Key Market Segmentations

2.Research Methodology

  • 2.1.Objective of the Study
  • 2.2.Baseline Methodology
  • 2.3.Key Industry Partners
  • 2.4.Major Association and Secondary Sources
  • 2.5.Forecasting Methodology
  • 2.6.Data Triangulation Validation
  • 2.7.Assumptions and Limitations

3.Executive Summary

  • 3.1.Overview of the Market
  • 3.2.Overview of Key Market Segmentations
  • 3.3.Overview of Key Market Players
  • 3.4.Overview of Key Regions/Countries
  • 3.5.Overview of Market Drivers, Challenges, and Trends

4.Voice of Customer

5.Global Predictive Analytics in Healthcare Market Outlook

  • 5.1.Market Size Forecast
    • 5.1.1.By Value
  • 5.2.Market Share Forecast
    • 5.2.1.By Application (Clinical Decision Diagnosis Support (CDS), Risk Prediction Scoring, Demand Forecast, Drug Discovery, Disease Cancer Detection, Fraud Detection, Others)
    • 5.2.2.By Component (Hardware, Software, Services)
    • 5.2.3.By End User (Healthcare Providers, Healthcare Payers, Others)
    • 5.2.4.By Deployment Mode(On premises, Cloud)
    • 5.2.5.By Company (2023)
    • 5.2.6.By Region
  • 5.3.Market Map

6.North America Predictive Analytics in Healthcare Market Outlook

  • 6.1.Market Size Forecast
    • 6.1.1.By Value
  • 6.2.Market Share Forecast
    • 6.2.1.By Application
    • 6.2.2.By Component
    • 6.2.3.By End User
    • 6.2.4.By Deployment Mode
    • 6.2.5.By Country
  • 6.3.North America: Country Analysis
    • 6.3.1.United States Predictive Analytics in Healthcare Market Outlook
      • 6.3.1.1.Market Size Forecast
        • 6.3.1.1.1.By Value
      • 6.3.1.2.Market Share Forecast
        • 6.3.1.2.1.By Application
        • 6.3.1.2.2.By Component
        • 6.3.1.2.3.By End User
        • 6.3.1.2.4.By Deployment Mode
    • 6.3.2.Mexico Predictive Analytics in Healthcare Market Outlook
      • 6.3.2.1.Market Size Forecast
        • 6.3.2.1.1.By Value
      • 6.3.2.2.Market Share Forecast
        • 6.3.2.2.1.By Application
        • 6.3.2.2.2.By Component
        • 6.3.2.2.3.By End User
        • 6.3.2.2.4.By Deployment Mode
    • 6.3.3.Canada Predictive Analytics in Healthcare Market Outlook
      • 6.3.3.1.Market Size Forecast
        • 6.3.3.1.1.By Value
      • 6.3.3.2.Market Share Forecast
        • 6.3.3.2.1.By Application
        • 6.3.3.2.2.By Component
        • 6.3.3.2.3.By End User
        • 6.3.3.2.4.By Deployment Mode

7.Europe Predictive Analytics in Healthcare Market Outlook

  • 7.1.Market Size Forecast
    • 7.1.1.By Value
  • 7.2.Market Share Forecast
    • 7.2.1.By Application
    • 7.2.2.By Component
    • 7.2.3.By End User
    • 7.2.4.By Deployment Mode
    • 7.2.5.By Country
  • 7.3.Europe: Country Analysis
    • 7.3.1.France Predictive Analytics in Healthcare Market Outlook
      • 7.3.1.1.Market Size Forecast
        • 7.3.1.1.1.By Value
      • 7.3.1.2.Market Share Forecast
        • 7.3.1.2.1.By Application
        • 7.3.1.2.2.By Component
        • 7.3.1.2.3.By End User
        • 7.3.1.2.4.By Deployment Mode
    • 7.3.2.Germany Predictive Analytics in Healthcare Market Outlook
      • 7.3.2.1.Market Size Forecast
        • 7.3.2.1.1.By Value
      • 7.3.2.2.Market Share Forecast
        • 7.3.2.2.1.By Application
        • 7.3.2.2.2.By Component
        • 7.3.2.2.3.By End User
        • 7.3.2.2.4.By Deployment Mode
    • 7.3.3.United Kingdom Predictive Analytics in Healthcare Market Outlook
      • 7.3.3.1.Market Size Forecast
        • 7.3.3.1.1.By Value
      • 7.3.3.2.Market Share Forecast
        • 7.3.3.2.1.By Application
        • 7.3.3.2.2.By Component
        • 7.3.3.2.3.By End User
        • 7.3.3.2.4.By Deployment Mode
    • 7.3.4.Italy Predictive Analytics in Healthcare Market Outlook
      • 7.3.4.1.Market Size Forecast
        • 7.3.4.1.1.By Value
      • 7.3.4.2.Market Share Forecast
        • 7.3.4.2.1.By Application
        • 7.3.4.2.2.By Component
        • 7.3.4.2.3.By End User
        • 7.3.4.2.4.By Deployment Mode
    • 7.3.5.Spain Predictive Analytics in Healthcare Market Outlook
      • 7.3.5.1.Market Size Forecast
        • 7.3.5.1.1.By Value
      • 7.3.5.2.Market Share Forecast
        • 7.3.5.2.1.By Application
        • 7.3.5.2.2.By Component
        • 7.3.5.2.3.By End User
        • 7.3.5.2.4.By Deployment Mode

8.Asia-Pacific Predictive Analytics in Healthcare Market Outlook

  • 8.1.Market Size Forecast
    • 8.1.1.By Value
  • 8.2.Market Share Forecast
    • 8.2.1.By Application
    • 8.2.2.By Component
    • 8.2.3.By End User
    • 8.2.4.By Deployment Mode
    • 8.2.5.By Country
  • 8.3.Asia-Pacific: Country Analysis
    • 8.3.1.China Predictive Analytics in Healthcare Market Outlook
      • 8.3.1.1.Market Size Forecast
        • 8.3.1.1.1.By Value
      • 8.3.1.2.Market Share Forecast
        • 8.3.1.2.1.By Application
        • 8.3.1.2.2.By Component
        • 8.3.1.2.3.By End User
        • 8.3.1.2.4.By Deployment Mode
    • 8.3.2.India Predictive Analytics in Healthcare Market Outlook
      • 8.3.2.1.Market Size Forecast
        • 8.3.2.1.1.By Value
      • 8.3.2.2.Market Share Forecast
        • 8.3.2.2.1.By Application
        • 8.3.2.2.2.By Component
        • 8.3.2.2.3.By End User
        • 8.3.2.2.4.By Deployment Mode
    • 8.3.3.South Korea Predictive Analytics in Healthcare Market Outlook
      • 8.3.3.1.Market Size Forecast
        • 8.3.3.1.1.By Value
      • 8.3.3.2.Market Share Forecast
        • 8.3.3.2.1.By Application
        • 8.3.3.2.2.By Component
        • 8.3.3.2.3.By End User
        • 8.3.3.2.4.By Deployment Mode
    • 8.3.4.Japan Predictive Analytics in Healthcare Market Outlook
      • 8.3.4.1.Market Size Forecast
        • 8.3.4.1.1.By Value
      • 8.3.4.2.Market Share Forecast
        • 8.3.4.2.1.By Application
        • 8.3.4.2.2.By Component
        • 8.3.4.2.3.By End User
        • 8.3.4.2.4.By Deployment Mode
    • 8.3.5.Australia Predictive Analytics in Healthcare Market Outlook
      • 8.3.5.1.Market Size Forecast
        • 8.3.5.1.1.By Value
      • 8.3.5.2.Market Share Forecast
        • 8.3.5.2.1.By Application
        • 8.3.5.2.2.By Component
        • 8.3.5.2.3.By End User
        • 8.3.5.2.4.By Deployment Mode

9.South America Predictive Analytics in Healthcare Market Outlook

  • 9.1.Market Size Forecast
    • 9.1.1.By Value
  • 9.2.Market Share Forecast
    • 9.2.1.By Application
    • 9.2.2.By Component
    • 9.2.3.By End User
    • 9.2.4.By Deployment Mode
    • 9.2.5.By Country
  • 9.3.South America: Country Analysis
    • 9.3.1.Brazil Predictive Analytics in Healthcare Market Outlook
      • 9.3.1.1.Market Size Forecast
        • 9.3.1.1.1.By Value
      • 9.3.1.2.Market Share Forecast
        • 9.3.1.2.1.By Application
        • 9.3.1.2.2.By Component
        • 9.3.1.2.3.By End User
        • 9.3.1.2.4.By Deployment Mode
    • 9.3.2.Argentina Predictive Analytics in Healthcare Market Outlook
      • 9.3.2.1.Market Size Forecast
        • 9.3.2.1.1.By Value
      • 9.3.2.2.Market Share Forecast
        • 9.3.2.2.1.By Application
        • 9.3.2.2.2.By Component
        • 9.3.2.2.3.By End User
        • 9.3.2.2.4.By Deployment Mode
    • 9.3.3.Colombia Predictive Analytics in Healthcare Market Outlook
      • 9.3.3.1.Market Size Forecast
        • 9.3.3.1.1.By Value
      • 9.3.3.2.Market Share Forecast
        • 9.3.3.2.1.By Application
        • 9.3.3.2.2.By Component
        • 9.3.3.2.3.By End-User
        • 9.3.3.2.4.By Deployment Mode

10.Middle East and Africa Predictive Analytics in Healthcare Market Outlook

  • 10.1.Market Size Forecast
    • 10.1.1.By Value
  • 10.2.Market Share Forecast
    • 10.2.1.By Application
    • 10.2.2.By Component
    • 10.2.3.By End User
    • 10.2.4.By Deployment Mode
    • 10.2.5.By Country
  • 10.3.MEA: Country Analysis
    • 10.3.1.South Africa Predictive Analytics in Healthcare Market Outlook
      • 10.3.1.1.Market Size Forecast
        • 10.3.1.1.1.By Value
      • 10.3.1.2.Market Share Forecast
        • 10.3.1.2.1.By Application
        • 10.3.1.2.2.By Component
        • 10.3.1.2.3.By End User
        • 10.3.1.2.4.By Deployment Mode
    • 10.3.2.Saudi Arabia Predictive Analytics in Healthcare Market Outlook
      • 10.3.2.1.Market Size Forecast
        • 10.3.2.1.1.By Value
      • 10.3.2.2.Market Share Forecast
        • 10.3.2.2.1.By Application
        • 10.3.2.2.2.By Component
        • 10.3.2.2.3.By End User
        • 10.3.2.2.4.By Deployment Mode
    • 10.3.3.UAE Predictive Analytics in Healthcare Market Outlook
      • 10.3.3.1.Market Size Forecast
        • 10.3.3.1.1.By Value
      • 10.3.3.2.Market Share Forecast
        • 10.3.3.2.1.By Application
        • 10.3.3.2.2.By Component
        • 10.3.3.2.3.By End User
        • 10.3.3.2.4.By Deployment Mode
    • 10.3.4.Egypt Predictive Analytics in Healthcare Market Outlook
      • 10.3.4.1.Market Size Forecast
        • 10.3.4.1.1.By Value
      • 10.3.4.2.Market Share Forecast
        • 10.3.4.2.1.By Application
        • 10.3.4.2.2.By Component
        • 10.3.4.2.3.By End User
        • 10.3.4.2.4.By Deployment Mode
    • 10.3.5.Turkey Predictive Analytics in Healthcare Market Outlook
      • 10.3.5.1.Market Size Forecast
        • 10.3.5.1.1.By Value
      • 10.3.5.2.Market Share Forecast
        • 10.3.5.2.1.By Application
        • 10.3.5.2.2.By Component
        • 10.3.5.2.3.By End User
        • 10.3.5.2.4.By Deployment Mode

11.Market Dynamics

  • 11.1.Drivers
  • 11.2.Challenges

12.Market Trends Developments

  • 12.1.Merger Acquisition (If Any)
  • 12.2.Product Launches (If Any)
  • 12.3.Recent Developments

13.Porters Five Forces Analysis

  • 13.1.Competition in the Industry
  • 13.2.Potential of New Entrants
  • 13.3.Power of Suppliers
  • 13.4.Power of Customers
  • 13.5.Threat of Substitute Products

14.Competitive Landscape

  • 14.1.International Business Machines Corporation
    • 14.1.1.Business Overview
    • 14.1.2.Company Snapshot
    • 14.1.3.Products Services
    • 14.1.4.Financials (As Reported)
    • 14.1.5.Recent Developments
    • 14.1.6.Key Personnel Details
    • 14.1.7.SWOT Analysis
  • 14.2.Unitedhealth Group.
  • 14.3.Oracle Cerner
  • 14.4.Microsoft Corporation
  • 14.5.Veradigm LLC
  • 14.6.Verisk Analytics, Inc
  • 14.7.MedeAnalytics, Inc.
  • 14.8.Cloud Software Group, Inc.
  • 14.9.SAS Institute, Inc.
  • 14.10.Health Catalyst

15.Strategic Recommendations

16.About Us Disclaimer