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Healthcare Predictive Analytics Market - Global Outlook & Forecast 2023-2028

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  • 309 Pages
  • July 2023
  • Region: Global
  • Arizton
  • ID: 5820218
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The global healthcare predictive analytics market is expected to grow at a CAGR of 22.23% from 2022-2028.


Predictive AI Enhancing Patient Care and Health Outcomes

Predictive AI enhancing patient care and health outcomes is an emerging area of healthcare technology that uses artificial intelligence (AI) to help healthcare providers make informed decisions about a patient’s care. Predictive AI can analyze vast amounts of data from multiple sources to provide insights about the patient’s condition, treatment history, and other data points that can help health professionals predict health problems and develop targeted treatment plans. By integrating AI into patient care, healthcare providers can more accurately diagnose and treat conditions, improve patient outcomes, and reduce costs. In addition, predictive AI can identify potential risk factors for disease and provide early warning signs of health problems. In the healthcare predictive analytics market, AI can examine patient records to predict the likelihood of certain diseases and disorders. Recent studies have shown that AI can detect previously difficult conditions to identify and diagnose, such as rare genetic and neurodegenerative diseases.

Predictive Analytics in Home Health

Predictive analytics in home health uses data-driven models and algorithms to help predict and anticipate future health outcomes or risks. These analytical methods are typically used by clinicians, administrators, providers, and health plans to identify and reduce risks in the home health care setting. Through predictive analysis, healthcare providers can make better-informed decisions regarding patient care to improve the quality and outcomes of care. Predictive analytics can help determine which patients are likely to require higher levels of care or may need additional services; identify patients who may benefit from personalized treatments; assess the impact of various interventions on treatment outcomes; and improve communication between providers and patients. Predictive analytics can also assess mortality, readmission rates, and healthcare costs.

Increasing Importance of Predictive Analytics in Healthcare

The increasing importance of the healthcare predictive analytics market is projected to support the healthcare analytics industry. Predictive analytics helps healthcare practitioners identify potential health issues and take preventive action before it is too late, thus providing cost and time efficiency to healthcare providers. Data from medical sensors, clinical laboratories, pharmacy information systems, and other sources help healthcare providers deliver tailored and personalized patient care. Predictive analytics helps organizations to identify patterns and trends in large amounts of data and utilize the insights to improve operational efficiency, reduce costs and improve patient outcomes. Predictive analytics enables the healthcare provider to predict future health trends, identify at-risk patients, and formulate relevant intervention programs. The increasing use of healthcare analytics is leading to enhanced accuracy in diagnosis, improved patient outcomes, and reduced cost.

Growing Need to Reduce Healthcare Costs Predictive Analytics

Predictive analytics has become a valuable tool in healthcare as it helps to reduce healthcare costs by enabling improved decision-making, operational improvements, and targeted interventions. Predictive analytics helps healthcare organizations anticipate costs, identify trends, and reduce costs to focus on high-value patient care. Predictive analytics helps to forecast the future with better precision and accuracy, which provides a more robust basis for decisions and actions. Predictive analytics are also used to support decisions related to population health, such as allocating resources to patients, assessing the financial impact of different treatments, and predicting when changes should be made to maximize cost savings. The growing need to reduce healthcare costs is leading to an increased demand for predictive analytics and is driving the healthcare predictive analytics market.

Availability of Abundant Patient Data Encouraging Usage of Predictive Analytics

The availability of abundant patient data is encouraging the usage of predictive analytics in healthcare and boosts the growth of the healthcare predictive analytics market. Predictive analytics uses data mining, machine learning, and statistical techniques to identify patterns and trends that can help diagnose illnesses, identify risk factors, and predict health outcomes. This data can also identify high-risk patients, suggest interventions, and provide insights for improving patient care. By leveraging this data, healthcare professionals can make more informed decisions, leading to better outcomes and improved patient safety. Additionally, predictive analytics can help health organizations improve efficiency and decrease costs by reducing unnecessary testing and treatment. The availability of abundant patient data pushes healthcare organizations to adopt predictive analytics technology and use it to its fullest potential.



The global healthcare predictive analytics market by delivery mode is segmented into on-premises and cloud-based. The on-premises segment accounted for a major market share and is anticipated to retain its dominance during the forecast period. The on-premises delivery model allows companies to verify customers and store data on their servers. No third party can access the customers’ data, the service provider, or vendors. Several on-premises benefits also significantly contribute to why healthcare organizations are still hesitant to embrace the cloud. The biggest benefit to on-premises applications is that the IT department has full control over the data stored on them. For healthcare organizations, control over the data is significant because many entities do not trust cloud service providers to provide a fully Healthcare Portability and Accountability Act (HIPAA) compliant Protected Health Information (PHI) environment.

Segmentation by Delivery Mode

  • On-premises
  • Cloud-based


The finance & operations application segment dominated the global healthcare predictive analytics market, accounting for over 45% share in 2022. Healthcare insurance companies today enjoy an extensive database of customers belonging to different demographics. Applying data analytics to this large dataset can help agents identify patterns, clusters, and typical human behavior. With insurance models and schemes moving online, consumers expect transparency and an accurate breakdown of their health insurance spending. Thus, the insurers cannot add hidden costs to make additional profits. Competition among payers is stiff, and hence, attracting customers with lucrative benefits is how these business agencies function.

Segmentation by Application

  • Finance & Operations
  • Clinical Care
  • Population Health Management
  • R&D


The global healthcare predictive analytics market by end-user is segmented into payers, healthcare settings, and others. The payers’ segment accounted for a major share in 2022. Payers in the health industry are organizations, such as healthcare plan providers, Medicare, and Medicaid, which establish service rates, collection of payments, process healthcare plans, and suppliers. Payers are investing in adopting technological advancements, mainly focusing on AI and machine learning. AI can simplify many paper-based processes at medical offices and insurance companies using natural language processing, which allows health professionals to enter information.

Segmentation by End-user

  • Payers
  • Healthcare Settings
  • Others


North America emerged as the leading region in the global healthcare predictive analytics market, accounting for a share of over 40% in 2022. Factors such as the increasing elderly population, prevalence of chronic illnesses, increasing medication errors, adoption of healthcare IT solutions, rise in prescriptions and hospitalization rates, and growing awareness of technological developments contribute to the market growth. Advancements and increased use of digital health technologies with the growing number of emerging and leading players entering the healthcare predictive analytics field contribute to this region's market growth.

Segmentation by Geography

  • North America
  • The U.S.
  • Canada
  • Europe
  • Germany
  • The U.K.
  • France
  • Italy
  • Spain
  • APAC
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Latin America
  • Brazil
  • Mexico
  • Argentina
  • Middle East & Africa
  • Turkey
  • Saudi Arabia
  • South Africa


The global healthcare predictive analytics market is highly competitive, with global, regional, and local players recommending a broad range of conventional and latest-generation AI technologies for end-users. The key vendors in the market include Health Catalyst, IBM, Koninklijke Philips, Microsoft, Oracle, and UNITEDHEALTH GROUP, based on digital healthcare platforms, patient management, and clinical advancements. These companies have a wide geographic presence, diverse product portfolios, and a strong focus on product innovation, R&D, and business expansion activities.

Key Company Profiles

  • Health Catalyst
  • IBM
  • Koninklijke Philips
  • Microsoft
  • Oracle

Other Prominent Vendors

  • Allscripts Healthcare
  • Apixio
  • CGI
  • Change Healthcare
  • Conduent
  • Epic Systems
  • Flatiron
  • Forian
  • FraudLens
  • Healthcare Fraud Shield
  • LexisNexis Risk Solutions
  • MedeAnalytics
  • Net Health
  • OSP
  • PotentiaMetrics
  • SAS Institute
  • Verisk Analytics
  • Wipro


1. How big is the global healthcare predictive analytics market?
2. What is the growth rate of the healthcare predictive analytics market?
3. What are the growing trends in the healthcare predictive analytics industry?
4. Which region holds the most significant global healthcare predictive analytics market share?
5. Who are the key players in the global healthcare predictive analytics market?

Table of Contents

1 Research Methodology2 Research Objectives3 Research Process
4 Scope & Coverage
4.1 Market Definition
4.1.1 Inclusions
4.1.2 Exclusions
4.1.3 Market Estimation Caveats
4.2 Base Year
4.3 Scope of the Study
4.3.1 Market Segmentation by Delivery Mode
4.3.2 Market Segmentation by Application
4.3.3 Market Segmentation by End-User
4.3.4 Market Segmentation by Geography
5 Report Assumptions & Caveats
5.1 Key Caveats
5.2 Currency Conversion
5.3 Market Derivation
6 Premium Insights
6.1 Overview
6.1.1 Geography Insights
6.1.2 Delivery Mode Segmentation Insights
6.1.3 Application Segmentation Insights
6.1.4 End-User Segmentation Insights
7 Market at a Glance
8 Introduction
8.1 Overview
9 Market Opportunities & Trends
9.1 Predictive Ai Enhancing Patient Care and Health Outcomes
9.2 Predictive Analytics in Home Health
9.3 Emerging Use of Predictive Analytics in Disease Management
10 Market Growth Enablers
10.1 Increasing Importance of Predictive Analytics in Healthcare
10.2 Digital Transformation in Healthcare
10.3 Growing Need to Reduce Healthcare Costs Using Predictive Analytics
10.4 Availability of Abundant Patients Data Encouraging Use of Predictive Analytics
11 Market Restraints
11.1 Ethics and Moral Hazards in Healthcare Predictive Analytics
11.2 Serious Privacy Considerations
11.3 Impact of Technology on Care and Lack of Regulations
12 Market Landscape
12.1 Market Overview
12.2 Market Size & Forecast
12.3 Market Opportunities
12.3.1 Market by Geography
12.3.2 Market by Delivery Mode
12.3.3 Market by Application
12.3.4 Market by End-User
12.4 Five Forces Analysis
12.4.1 Threat of New Entrants
12.4.2 Bargaining Power of Suppliers
12.4.3 Bargaining Power of Buyers
12.4.4 Threat of Substitutes
12.4.5 Competitive Rivalry
13 Delivery Mode
13.1 Market Snapshot & Growth Engine
13.2 Market Overview
13.3 On-Premises
13.3.1 Market Overview
13.3.2 Market Size & Forecast
13.3.3 On-Premise by Geography
13.4 Cloud-Based
13.4.1 Market Overview
13.4.2 Market Size & Forecast
13.4.3 Cloud-Based by Geography
14 Application
14.1 Market Snapshot & Growth Engine
14.2 Market Overview
14.3 Finance & Operations
14.3.1 Market Overview
14.3.2 Market Size & Forecast
14.3.3 Finance & Operations by Geography
14.4 Clinical Care
14.4.1 Market Overview
14.4.2 Market Size & Forecast
14.4.3 Clinical Care by Geography
14.5 Population Health Management
14.5.1 Market Overview
14.5.2 Market Size & Forecast
14.5.3 Population Health Management by Geography
14.6 Research & Development (R&D)
14.6.1 Market Overview
14.6.2 Market Size & Forecast
14.6.3 R&D by Geography
15 End-Users
15.1 Market Snapshot & Growth Engine
15.2 Market Overview
15.3 Payers
15.3.1 Market Overview
15.3.2 Market Size & Forecast
15.3.3 Payers by Geography
15.4 Healthcare Settings
15.4.1 Market Overview
15.4.2 Market Size & Forecast
15.4.3 Healthcare Settings by Geography
15.5 Others
15.5.1 Market Overview
15.5.2 Market Size & Forecast
15.5.3 Others by Geography
16 Geography
16.1 Market Snapshot & Growth Engine
16.2 Geographic Overview
17 North America
17.1 Market Overview
17.2 Market Size & Forecast
17.2.1 North America by Delivery Mode
17.2.2 North America by Application
17.2.3 North America by End-User
17.3 Key Countries
17.3.1 Us: Market Size & Forecast
17.3.2 Canada: Market Size & Forecast
18 Europe
18.1 Market Overview
18.2 Market Size & Forecast
18.2.1 Europe by Delivery Mode
18.2.2 Europe by Application
18.2.3 Europe by End-User
18.3 Key Countries
18.3.1 Germany: Market Size & Forecast
18.3.2 Uk: Market Size & Forecast
18.3.3 France: Market Size & Forecast
18.3.4 Italy: Market Size & Forecast
18.3.5 Spain: Market Size & Forecast
19 Apac
19.1 Market Overview
19.2 Market Size & Forecast
19.2.1 APAC by Delivery Mode
19.2.2 APAC by Application
19.2.3 APAC by End-User
19.3 Key Countries
19.3.1 China: Market Size & Forecast
19.3.2 Japan: Market Size & Forecast
19.3.3 India: Market Size & Forecast
19.3.4 South Korea: Market Size & Forecast
19.3.5 Australia: Market Size & Forecast
20 Latin America
20.1 Market Overview
20.2 Market Size & Forecast
20.2.1 Latin America by Delivery Mode
20.2.2 Latin America by Application
20.2.3 Latin America by End-User
20.3 Key Countries
20.3.1 Brazil: Market Size & Forecast
20.3.2 Mexico: Market Size & Forecast
20.3.3 Argentina: Market Size & Forecast
21 Middle East & Africa
21.1 Market Overview
21.2 Market Size & Forecast
21.2.1 Middle East & Africa by Delivery Mode
21.2.2 Middle East & Africa by Application
21.2.3 Middle East & Africa by End-User
21.3 Key Countries
21.3.1 Turkey: Market Size & Forecast
21.3.2 Saudi Arabia: Market Size & Forecast
21.3.3 South Africa: Market Size & Forecast
22 Competitive Landscape
22.1 Competition Overview
22.2 Market Share Analysis
22.2.1 Health Catalyst
22.2.2 Ibm
22.2.3 Koninklijke Philips
22.2.4 Microsoft
22.2.5 Oracle
22.2.6 Unitedhealth Group
23 Key Company Profiles
23.1 Health Catalyst
23.1.1 Business Overview
23.1.2 Product Offerings
23.1.3 Key Strategies
23.1.4 Key Strengths
23.1.5 Key Opportunities
23.2 Ibm
23.2.1 Business Overview
23.2.2 Product Offerings
23.2.3 Key Strategies
23.2.4 Key Strengths
23.2.5 Key Opportunities
23.3 Koninklijke Philips
23.3.1 Business Overview
23.3.2 Product Offerings
23.3.3 Key Strategies
23.3.4 Key Strengths
23.3.5 Key Opportunities
23.4 Microsoft
23.4.1 Business Overview
23.4.2 Product Offerings
23.4.3 Key Strategies
23.4.4 Key Strengths
23.4.5 Key Opportunities
23.5 Oracle
23.5.1 Business Overview
23.5.2 Product Offerings
23.5.3 Key Strategies
23.5.4 Key Strengths
23.5.5 Key Opportunities
23.6 Unitedhealth Group
23.6.1 Business Overview
23.6.2 Product Offerings
23.6.3 Key Strategies
23.6.4 Key Strengths
23.6.5 Key Opportunities
24 Other Prominent Vendors
24.1 Allscripts Healthcare
24.1.1 Business Overview
24.1.2 Product Offerings
24.2 Apixio
24.2.1 Business Overview
24.2.2 Product Offerings
24.3 Cgi
24.3.1 Business Overview
24.3.2 Product Offerings
24.4 Change Healthcare
24.4.1 Business Overview
24.4.2 Product Offerings
24.5 Conduent
24.5.1 Business Overview
24.5.2 Product Offerings
24.6 Cotiviti
24.6.1 Business Overview
24.6.2 Product Offerings
24.7 Epic Systems
24.7.1 Business Overview
24.7.2 Product Offerings
24.8 Flatiron
24.8.1 Business Overview
24.8.2 Product Offerings
24.9 Forian
24.9.1 Business Overview
24.9.2 Product Offerings
24.10 Fraudlens
24.10.1 Business Overview
24.10.2 Product Offerings
24.11 Friss
24.11.1 Business Overview
24.11.2 Product Offerings
24.12 Healthcare Fraud Shield
24.12.1 Business Overview
24.12.2 Product Offerings
24.13 Lexisnexis Risk Solutions
24.13.1 Business Overview
24.13.2 Product Offerings
24.14 Medeanalytics
24.14.1 Business Overview
24.14.2 Product Offerings
24.15 Net Health
24.15.1 Business Overview
24.15.2 Product Offerings
24.16 Osp
24.16.1 Business Overview
24.16.2 Product Offerings
24.17 Potentiametrics
24.17.1 Business Overview
24.17.2 Product Offerings
24.18 Sas Institute
24.18.1 Business Overview
24.18.2 Product Offerings
24.19 Verisk Analytics
24.19.1 Business Overview
24.19.2 Product Offerings
24.20 Wipro
24.20.1 Business Overview
24.20.2 Product Offerings
25 Report Summary
25.1 Key Takeaways
25.2 Strategic Recommendations
26 Quantitative Summary
26.1 Market by Delivery Mode
26.1.1 North America by Delivery Mode
26.1.2 Europe by Delivery Mode
26.1.3 APAC by Delivery Mode
26.1.4 Latin America by Delivery Mode
26.1.5 Middle East & Africa by Delivery Mode
26.2 Market by Application
26.2.1 North America by Application
26.2.2 Europe by Application
26.2.3 APAC by Application
26.2.4 Latin America by Application
26.2.5 Middle East & Africa by Application
26.3 Market by End-User
26.3.1 North America by End-User
26.3.2 Europe by End-User
26.3.3 APAC by End-User
26.3.4 Latin America by End-User
26.3.5 Middle East & Africa by End-User Segmentation
26.4 Market by Geography
26.4.1 On-Premises by Geography
26.4.2 Cloud-Based by Geography
26.4.3 Finance & Operations by Geography
26.4.4 Clinical Care by Geography
26.4.5 Population Health Management by Geography
26.4.6 R&D by Geography
26.4.7 Payers by Geography
26.4.8 Healthcare Settings by Geography
26.4.9 Others by Geography
27 Appendix
27.1 Abbreviations

Companies Mentioned

  • Health Catalyst
  • IBM
  • Koninklijke Philips
  • Microsoft
  • Oracle
  • Allscripts Healthcare
  • Apixio
  • CGI
  • Change Healthcare
  • Conduent
  • Epic Systems
  • Flatiron
  • Forian
  • FraudLens
  • Healthcare Fraud Shield
  • LexisNexis Risk Solutions
  • MedeAnalytics
  • Net Health
  • OSP
  • PotentiaMetrics
  • SAS Institute
  • Verisk Analytics
  • Wipro


Our research comprises a mix of primary and secondary research. The secondary research sources that are typically referred to include, but are not limited to, company websites, annual reports, financial reports, company pipeline charts, broker reports, investor presentations and SEC filings, journals and conferences, internal proprietary databases, news articles, press releases, and webcasts specific to the companies operating in any given market.

Primary research involves email interactions with the industry participants across major geographies. The participants who typically take part in such a process include, but are not limited to, CEOs, VPs, business development managers, market intelligence managers, and national sales managers. We primarily rely on internal research work and internal databases that we have populated over the years. We cross-verify our secondary research findings with the primary respondents participating in the study.



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