한글목차

세계 헬스케어 예측 분석 시장의 미래는 유망하며, 보험사 및 의료 프로바이더 시장에 기회가 있습니다. 세계 헬스케어 예측 분석 시장은 2025-2031년 20.4%의 연평균 복합 성장률(CAGR)로 2031년까지 약 411억 달러에 달할 것으로 예측됩니다. 이 시장의 주요 촉진요인은 비용 절감과 환자 결과 개선을 위한 고급 분석 툴에 대한 업계 수요 증가, 개인화된 헬스케어의 인기 증가, 가치 기반 헬스케어에 대한 관심 증가, 전자건강기록의 보급 증가 등입니다.

• Lucintel의 예측에 따르면 용도별로는 예측 기간 중 재무 분야가 가장 큰 시장으로 남을 것으로 보입니다. 이는 헬스케어 분야의 부정행위로 인해 연간 수십억 달러의 비용이 발생하기 때문이며, 예측 분석은 보험사가 의심스러운 패턴과 행동을 감지하고 부정 청구를 방지하여 막대한 비용을 절감하는 데 도움이 될 수 있기 때문입니다.
• 지역별로는 전자의무기록, 데이터 인프라 등 첨단 기술 자원을 쉽게 이용할 수 있는 의료시설이 잘 갖추어진 북미가 예측 기간 중 가장 규모가 큰 지역으로 남을 것으로 보입니다.

헬스케어 예측 분석 시장의 전략적 성장 기회

전략적 성장 기회를 모색함으로써 헬스케어 예측 분석 시장의 확대와 혁신을 촉진할 수 있습니다.

• 신흥 시장으로의 확장: 헬스케어 인프라가 성장하고 있는 신흥 시장을 공략함으로써 시장 도달 범위와 영향력을 확대할 수 있습니다.
• 전문 솔루션 개발: 종양학, 순환기학 등 특정 헬스케어 니즈에 맞는 예측 분석 솔루션 구축이 요구되고 있습니다.
• IoT 기기와의 통합: 사물인터넷(IoT) 기기의 데이터를 활용하여 예측 모델과 실시간 모니터링을 강화할 수 있습니다.
• 연구개발 투자: 혁신을 촉진하고 최첨단 예측 분석 기술을 개발하기 위해서는 연구개발에 대한 투자가 필수적입니다.
• 전략적 파트너십: 의료 서비스 프로바이더 및 기술 회사와의 파트너십을 통해 제품 제공 및 기능을 확장할 수 있습니다.
• 예방의료에 집중: 예방의료에 초점을 맞춘 예측 툴의 개발은 의료비 절감과 환자 예후 개선으로 이어집니다.

이러한 전략적 성장 기회에 집중함으로써 헬스케어 분야에서 예측 분석의 영향력을 높이고, 혁신을 촉진하며, 시장 입지를 확대할 수 있습니다.

헬스케어 예측 분석 시장 성장 촉진요인 및 과제

헬스케어 예측 분석 시장 성장 촉진요인과 과제를 이해하는 것은 성장을 유도하고 장애물을 해결하는 데 매우 중요합니다.

헬스케어 예측 분석 시장 성장 촉진요인은 다음과 같습니다.

• 기술 발전: AI와 머신러닝의 급속한 발전으로 예측 능력과 정확도가 향상되고 있습니다.
• 데이터 가용성 증가: EHR, 웨어러블, 기타 소스로부터의 빅데이터 가용성 증가는 예측 분석의 채택을 촉진하고 있습니다.
• 맞춤형 의료에 대한 수요: 개인 맞춤형 치료 계획에 대한 수요가 증가함에 따라 고급 예측 분석 솔루션의 필요성이 대두되고 있습니다.
• 업무 효율화: 예측 분석은 헬스케어 조직이 업무를 최적화하고 비용을 절감하는 데 도움이 됩니다.
• 정부 지원: 정부의 지원책과 자금 지원은 헬스케어 분야에서 예측 분석의 활용을 촉진하고 있습니다.

헬스케어 예측 분석 시장이 해결해야 할 과제는 다음과 같습니다.

• 데이터 프라이버시 우려: 예측 분석을 활용하면서 데이터 프라이버시를 보장하고 규제를 준수하는 것은 어려운 일입니다.
• 높은 도입 비용: 고급 예측 분석 솔루션의 도입 비용은 일부 헬스케어 조직에 장벽이 될 수 있습니다.
• 데이터 통합 문제: 다양한 소스의 데이터를 통합하여 정확한 예측 모델을 만드는 것은 복잡할 수 있습니다.
• 기술적 복잡성: 예측 분석 기술은 복잡하므로 전문적인 지식과 훈련이 필요합니다.
• 규제 준수: 규제 요건과 표준을 이해하는 것은 시간이 많이 걸리고 어려운 과제입니다.
• 제한된 상호운용성: 서로 다른 헬스케어 시스템 간의 상호운용성 부족은 예측 분석의 효과를 저해할 수 있습니다.

헬스케어 예측 분석 시장은 기술 발전과 개인 맞춤형 치료에 대한 수요 증가에 힘입어 성장하고 있지만, 지속적인 성장과 효과를 달성하기 위해서는 데이터 프라이버시, 비용, 통합과 관련된 문제를 해결하는 것이 필수적입니다.

목차

제1장 개요

제2장 세계의 헬스케어 예측 분석 시장 : 시장 역학

• 서론, 배경, 분류
• 공급망
• 업계의 촉진요인과 과제

제3장 2019-2031년 시장 동향과 예측 분석

• 거시경제 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 헬스케어 예측 분석 시장 동향(2019-2024년)과 예측(2025-2031년)
• 용도별
  • 업무 관리
  • 재무
  • 집단건강관리
  • 임상
• 최종 용도별
  • 보험자
  • 의료 제공자
  • 기타

제4장 2019-2031년 지역별 시장 동향과 예측 분석

• 지역별
• 북미
• 유럽
• 아시아태평양
• 기타 지역

제5장 경쟁 분석

• 제품 포트폴리오 분석
• 운영 통합
• Porter's Five Forces 분석

제6장 성장 기회와 전략 분석

• 성장 기회 분석
  • 용도별
  • 최종 용도별
  • 지역별
• 세계의 헬스케어 예측 분석 시장의 새로운 동향
• 전략 분석
  • 신제품 개발
  • 세계의 헬스케어 예측 분석 시장의 능력 확대
  • 세계의 헬스케어 예측 분석 시장에서의 합병, 인수, 합병사업
  • 인증과 라이선싱

제7장 주요 기업의 기업 개요

• IBM
• Cerner
• Verisk Analytics
• McKesson
• SAS
• Oracle
• Allscripts
• Optum
• MedeAnalytics
• OSP

영문목차

The future of the global healthcare predictive analytics market looks promising, with opportunities in the payers and provider markets. The global healthcare predictive analytics market is expected to reach an estimated $41.1 billion by 2031, with a CAGR of 20.4% from 2025 to 2031. The major drivers for this market are the industry's growing need for advanced analytics tools to save costs and enhance patient outcomes, the growing popularity of individualized healthcare, the emphasis on value-based healthcare, and the rising adoption of electronic health records.

• Lucintel forecasts that, within the application category, financial will remain the largest segment over the forecast period due to healthcare fraud costs billions annually, and predictive analytics helps insurers detect suspicious patterns and behaviors, preventing fraudulent claims and saving significant amounts of money.
• In terms of regions, North America will remain the largest region over the forecast period due to well-equipped healthcare facilities with readily available advanced technological resources like electronic health records and data infrastructure.

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Emerging Trends in the Healthcare Predictive Analytics Market

The healthcare predictive analytics market is experiencing several emerging trends that are shaping its future.

• AI and Machine Learning Integration: There is an increasing use of AI and ML algorithms to enhance predictive accuracy and decision-making in healthcare.
• Personalized Medicine: There is a growing focus on using predictive analytics for personalized treatment plans based on individual patient data.
• Real-Time Analytics: The development of real-time analytics tools provides immediate insights and interventions, improving patient outcomes.
• Big Data Utilization: There is an expanding use of big data from various sources, such as wearables and EHRs, to drive predictive models.
• Predictive Maintenance: The implementation of predictive analytics for equipment maintenance and management in healthcare facilities is becoming more prevalent.

These trends indicate a shift towards more advanced, real-time, and personalized predictive analytics solutions in healthcare, promising improved patient care and operational efficiency.

Recent Developments in the Healthcare Predictive Analytics Market

Recent developments in the healthcare predictive analytics market reflect advancements in technology and application.

• Advanced AI Algorithms: There is an adoption of sophisticated AI and ML algorithms to improve predictive accuracy and patient outcomes.
• Integration with EHR Systems: Predictive analytics tools are being integrated with EHR systems to enhance data utilization and decision-making.
• Predictive Models for Chronic Diseases: The development of predictive models is aimed at better managing chronic diseases and reducing hospital readmissions.
• Enhanced Data Security: There is an implementation of robust data security measures to protect patient information while using predictive analytics.
• Collaborations and Partnerships: There is increased collaboration between healthcare providers and technology firms to advance predictive analytics solutions.
• Government Support: Government initiatives and funding promote the use of predictive analytics in improving healthcare delivery.

These developments highlight the rapid evolution of the healthcare predictive analytics market, driven by technological advancements and an increased focus on improving patient care and operational efficiency.

Strategic Growth Opportunities for Healthcare Predictive Analytics Market

Exploring strategic growth opportunities can drive expansion and innovation in the healthcare predictive analytics market.

• Expansion into Emerging Markets: Targeting emerging markets with growing healthcare infrastructure can increase market reach and impact.
• Development of Specialized Solutions: There is a need for creating predictive analytics solutions tailored to specific healthcare needs, such as oncology or cardiology.
• Integration with IoT Devices: Leveraging data from Internet of Things (IoT) devices can enhance predictive models and real-time monitoring.
• Investment in R&D: Investing in research and development is essential to drive innovation and develop cutting-edge predictive analytics technologies.
• Strategic Partnerships: Forming partnerships with healthcare providers and technology companies can expand product offerings and capabilities.
• Focus on Preventive Care: Developing predictive tools focused on preventive care can reduce healthcare costs and improve patient outcomes.

Focusing on these strategic growth opportunities can enhance the impact of predictive analytics in healthcare, driving innovation and expanding market presence.

Healthcare Predictive Analytics Market Driver and Challenges

Understanding the drivers and challenges in the healthcare predictive analytics market is crucial for navigating growth and addressing obstacles.

The factors responsible for driving the healthcare predictive analytics market include:

• Technological Advancements: Rapid advancements in AI and machine learning are enhancing predictive capabilities and accuracy.
• Increasing Data Availability: The growing availability of big data from EHRs, wearables, and other sources is driving predictive analytics adoption.
• Demand for Personalized Medicine: The rising demand for personalized treatment plans is fueling the need for advanced predictive analytics solutions.
• Operational Efficiency: Predictive analytics helps healthcare organizations optimize operations and reduce costs.
• Government Support: Supportive government initiatives and funding are promoting the use of predictive analytics in healthcare.

Challenges in the healthcare predictive analytics market include:

• Data Privacy Concerns: Ensuring data privacy and compliance with regulations while utilizing predictive analytics can be challenging.
• High Implementation Costs: The cost of implementing advanced predictive analytics solutions can be a barrier for some healthcare organizations.
• Data Integration Issues: Integrating data from various sources to create accurate predictive models can be complex.
• Technical Complexity: The complexity of predictive analytics technologies requires specialized expertise and training.
• Regulatory Compliance: Navigating regulatory requirements and standards can be time-consuming and challenging.
• Limited Interoperability: The lack of interoperability between different healthcare systems can hinder the effectiveness of predictive analytics.

While the healthcare predictive analytics market is driven by technological advancements and increasing demand for personalized care, addressing challenges related to data privacy, cost, and integration is essential for achieving sustainable growth and effectiveness.

List of Healthcare Predictive Analytics Companies

Companies in the market compete based on product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies, healthcare predictive analytics companies cater to increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the healthcare predictive analytics companies profiled in this report include-

• IBM
• Cerner
• Verisk Analytics
• McKesson
• SAS
• Oracle
• Allscripts
• Optum
• MedeAnalytics
• OSP

Healthcare Predictive Analytics by Segment

The study includes a forecast for the global healthcare predictive analytics market by application, end use, and region.

Healthcare Predictive Analytics Market by Application [Analysis by Value from 2019 to 2031]:

• Operations Management
• Financial
• Population Health
• Clinical

Healthcare Predictive Analytics Market by End Use [Analysis by Value from 2019 to 2031]:

• Payers
• Providers
• Others

Healthcare Predictive Analytics Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Healthcare Predictive Analytics Market

Major players in the market are expanding their operations and forming strategic partnerships to strengthen their positions. The content below highlights recent developments by major healthcare predictive analytics players in key regions: the USA, China, India, and Japan.

• USA: In the United States, the healthcare predictive analytics market is witnessing substantial growth driven by advancements in artificial intelligence (AI) and machine learning (ML). Recent developments include the integration of AI-driven predictive models into Electronic Health Records (EHR) systems, enhancing the ability to forecast patient outcomes and optimize treatment plans. There is also an increasing investment in predictive analytics for reducing hospital readmissions and managing chronic diseases. Furthermore, major healthcare organizations and technology firms are forming partnerships to develop innovative solutions that leverage big data for predictive insights, supporting value-based care models.
• China: China is rapidly advancing its healthcare predictive analytics capabilities, driven by significant investments in health IT infrastructure and AI technologies. Recent developments include the implementation of predictive analytics in public health initiatives, such as epidemic forecasting and disease prevention. Chinese technology companies are developing advanced analytics platforms that integrate big data from various sources, including wearable devices and health records, to improve disease management and patient outcomes. The government is supporting these advancements through initiatives aimed at modernizing the healthcare system and enhancing predictive analytics applications for better public health management.
• India: In India, the healthcare predictive analytics market is growing with a focus on enhancing healthcare delivery and management. Recent developments include the adoption of predictive analytics for improving patient care and operational efficiency in hospitals. Indian startups and technology firms are developing affordable analytics solutions tailored to local healthcare challenges, such as managing chronic diseases and optimizing resource allocation. There is also increasing collaboration between healthcare providers and tech companies to integrate predictive analytics into health management systems, supported by government initiatives to boost digital health infrastructure and data utilization.
• Japan: Japan's healthcare predictive analytics market is evolving with advancements in data integration and AI technologies. Recent developments include the use of predictive analytics to support personalized medicine and improve patient outcomes through advanced modeling techniques. Japanese healthcare institutions are increasingly adopting predictive tools for early disease detection and treatment optimization. The government's support for digital health innovation and research is driving the development of new predictive analytics solutions. Additionally, Japan is focusing on integrating predictive analytics with existing health information systems to enhance overall healthcare efficiency and patient management.

Features of the Global Healthcare Predictive Analytics Market

Market Size Estimates: Healthcare predictive analytics market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Healthcare predictive analytics market size by application, end use, and region in terms of value ($B).

Regional Analysis: Healthcare predictive analytics market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different applications, end uses, and regions for the healthcare predictive analytics market.

Strategic Analysis: This includes M&A, new product development, and the competitive landscape of the healthcare predictive analytics market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers the following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the healthcare predictive analytics market by application (operations management, financial, population health, and clinical), end use (payers, providers, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market, and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years, and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Healthcare Predictive Analytics Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Healthcare Predictive Analytics Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Healthcare Predictive Analytics Market by Application
  • 3.3.1: Operations Management
  • 3.3.2: Financial
  • 3.3.3: Population Health
  • 3.3.4: Clinical
• 3.4: Global Healthcare Predictive Analytics Market by End Use
  • 3.4.1: Payers
  • 3.4.2: Providers
  • 3.4.3: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Healthcare Predictive Analytics Market by Region
• 4.2: North American Healthcare Predictive Analytics Market
  • 4.2.1: North American Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.2.2: North American Market by End Use: Payers, Providers, and Others
• 4.3: European Healthcare Predictive Analytics Market
  • 4.3.1: European Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.3.2: European Market by End Use: Payers, Providers, and Others
• 4.4: APAC Healthcare Predictive Analytics Market
  • 4.4.1: APAC Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.4.2: APAC Market by End Use: Payers, Providers, and Others
• 4.5: ROW Healthcare Predictive Analytics Market
  • 4.5.1: ROW Market by Application: Operations Management, Financial, Population Health, and Clinical
  • 4.5.2: ROW Market by End Use: Payers, Providers, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Healthcare Predictive Analytics Market by Application
  • 6.1.2: Growth Opportunities for the Global Healthcare Predictive Analytics Market by End Use
  • 6.1.3: Growth Opportunities for the Global Healthcare Predictive Analytics Market by Region
• 6.2: Emerging Trends in the Global Healthcare Predictive Analytics Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Healthcare Predictive Analytics Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Healthcare Predictive Analytics Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: IBM
• 7.2: Cerner
• 7.3: Verisk Analytics
• 7.4: McKesson
• 7.5: SAS
• 7.6: Oracle
• 7.7: Allscripts
• 7.8: Optum
• 7.9: MedeAnalytics
• 7.10: OSP