ヘルスケアにおける予測分析市場- 世界の産業規模、シェア、動向、機会、予測、用途別、競合別、エンドユーザー別、展開モード別、地域別、競合別、2019年～2029年

ヘルスケアにおける予測分析の世界市場は、2023年に130億1,000万米ドルと評価され、2029年までのCAGRは17.32%で、予測期間中に堅調な成長が予測されています。

ヘルスケアにおける予測分析の世界市場は、ヘルスケア分野での先端技術の採用増加に後押しされ、近年目覚ましい成長を遂げています。予測分析には、統計アルゴリズムと機械学習技術を使用して、過去と現在のデータを分析し、将来の結果を予測することが含まれます。

ヘルスケア分野では、予測分析は患者ケアの強化、業務の合理化、コスト効率の向上に大きな可能性を示しています。この市場の成長には、個別化医療に対する需要の高まり、慢性疾患の罹患率の上昇、効果的なヘルスケア管理ソリューションの必要性など、いくつかの重要な要因が拍車をかけています。予測分析は、ヘルスケア提供者に患者の健康リスクを予測し、潜在的な合併症を特定し、それに応じて治療計画をカスタマイズする力を与え、転帰の改善と患者満足度の向上をもたらします。さらに、予測分析を電子カルテ（EHR）やその他のヘルスケアITシステムとシームレスに統合することで、データ分析と意思決定プロセスが合理化されています。

さらに同市場は、ウェアラブルデバイス、ゲノム、健康の社会的決定要因など、多様な情報源から入手可能なヘルスケアデータの増加からも恩恵を受けています。しかし、データセキュリティの問題、相互運用性の懸念、熟練した専門家の不足といった課題は、市場の成長をやや妨げる可能性があります。とはいえ、人工知能（AI）、ビッグデータ分析、クラウドコンピューティングの継続的な進歩により、ヘルスケア向け予測分析ソリューションの継続的な革新が促進されると予想されます。その結果、ヘルスケアにおける予測分析の世界市場は、当面大幅な拡大が見込まれ、ベンダーは世界中の医療機関の進化するニーズに対応したソリューションを開発する機会を得ることになります。

主な市場促進要因

慢性疾患の有病率の上昇

ヘルスケアITソリューションの採用増加

AIとビッグデータ分析の技術的進歩

主な市場課題

データセキュリティの懸念

相互運用性の課題

熟練専門家の不足

主な市場動向

精密医療の出現

バリューベース医療へのシフト

セグメント別の洞察

用途別の洞察

コンポーネント別の洞察

地域別の洞察

目次

第1章 概要

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 顧客の声

第5章 ヘルスケアにおける予測分析の世界市場展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別（臨床判断診断支援（CDS）、リスク予測スコアリング、需要予測、創薬、疾患がん検出、不正検出、その他）
  • コンポーネント別（ハードウェア、ソフトウェア、サービス）
  • エンドユーザー別（ヘルスケアプロバイダー、ヘルスケアペイヤー、その他）
  • 展開モード別（オンプレミス、クラウド）
  • 企業別（2023年）
  • 地域別
• 市場マップ

第6章 北米のヘルスケアにおける予測分析市場の展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別
  • コンポーネント別
  • エンドユーザー別
  • 展開モード別
  • 国別
• 北米国別分析
  • 米国
  • メキシコ
  • カナダ

第7章 欧州のヘルスケアにおける予測分析市場の展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別
  • コンポーネント別
  • エンドユーザー別
  • 展開モード別
  • 国別
• 欧州国別分析
  • フランス
  • ドイツ
  • 英国
  • イタリア
  • スペイン

第8章 アジア太平洋地域のヘルスケアにおける予測分析市場の展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別
  • コンポーネント別
  • エンドユーザー別
  • 展開モード別
  • 国別
• アジア太平洋地域国別分析
  • 中国
  • インド
  • 韓国
  • 日本
  • オーストラリア

第9章 南米のヘルスケアにおける予測分析市場の展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別
  • コンポーネント別
  • エンドユーザー別
  • 展開モード別
  • 国別
• 南米：国別分析
  • ブラジル
  • アルゼンチン
  • コロンビア

第10章 中東・アフリカのヘルスケアにおける予測分析市場の展望

• 市場規模予測
  • 金額別
• 市場シェア予測
  • 用途別
  • コンポーネント別
  • エンドユーザー別
  • 展開モード別
  • 国別
• MEA：国別分析
  • 南アフリカ
  • サウジアラビア
  • アラブ首長国連邦
  • エジプト
  • トルコ

第11章 市場力学

• 促進要因
• 課題

第12章 市場動向と発展

• 合併買収（もしあれば）
• 製品上市（もしあれば）
• 最近の動向

第13章 ポーターのファイブフォース分析

• 業界内の競合
• 新規参入の可能性
• サプライヤーの力
• 顧客の力
• 代替品の脅威

第14章 競合情勢

• 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 戦略的提言

第16章 免責事項

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