Healthcare Predictive Analytics Market Synopsis:
Healthcare Predictive Analytics Market Size Was Valued at USD 14.65 Billion in 2023, and is Projected to Reach USD 109.15 Billion by 2032, Growing at a CAGR of 25.00% From 2024-2032.
Healthcare Predictive Analytics can be defined as the application of statistical models, visualization and machine learning techniques to current and historical data to forecast future outcomes of health-care activities. It allows health care providers and other stakeholders to foresee occurrence of clinical, operating, and incurred costs, which, in turn can increase efficiency and enhance clinical decision making and patient care.
This market has been under a steady growth mainly due to the growing concentrations by the healthcare industry on the use of information. The use of big data and predictive analytics in healthcare enhances the ability of treatment facilities to make predictions on future trends and results in order to increase the quality of care, decrease the amount being spent, and increase the overall effectiveness of the organizations. Risk assessment models in healthcare has become more realistic on account of leveraging of big data, machine learning, and artificial intelligence solutions by the healthcare providers like hospitals, clinics, insurance companies and even government bodies. Amassing huge streams of patient data make it possible for healthcare institutions to forecast disease outbreaks, patients readmissions, and predisposition to certain diseases.
It is also applied for clinical, operational, and financial prediction purposes Other than clinical predictions. It assists healthcare organisations to make decisions with regard to distribution of capital, manpower and supplies. For the customers, the healthcare payers, the potential uses include predictive claims, fraud detection, cost optimization, insurance risk management. As healthcare to moves more in the direction of personalized medicine, along with value based care, the market for predictive analytics is likewise expected to continue to grow exponentially. However, some of the major issues identified include data privacy concerns, high costs of implementing and massive project sizes for some organizations.
Healthcare Predictive Analytics Market Trend Analysis:
Growing Focus on Personalized Medicine
- Among all the trends in the Healthcare Predictive Analytics Market, it is crucial to highlight personalized medicine as being one of the main driving forces of development. Another important niche in which predictive analytics is being used more and more is to individualize treatment based on genetic, environmental, and lifestyle characteristics. Taking into account the data on the patient, including genetic information and the history of diseases, the predictive models to suggest how a particular patient would behave in response to a particular set of treatments, providing more accurate approach to the therapies required.
- It is revolutionalising healthcare delivery since patients gain treatments that are highly likely to succeed and which have minimal side effects as opposed to the one-size-fits-all approach. As a result, the practical application of medicine minimizes the trial learning that is characteristic of the traditional approach to healing hence improved health and reduced cost. Predictive analytics will follow the advancement of precision medicine and will be responsible for choosing the right treatment regimen at the right time from the patient.
Reducing Healthcare Costs
- Another important segment that defines the Healthcare Predictive Analytics Market is the existence of a great opportunity to cut healthcare costs. In this approach, risk management personnel are able to predict which clinical clients or patients are likely to attract hospital readm, major medical errors and thus prevent such occurrences. For instance, on the basis of the data collected, the probability of a specific patient developing a certain sickness or an ailment is estimated, which can help avert a flair-up of a chronic ailment which costs a lot of money to treat once it has had time to develop fully.
- Furthermore, predictive analytics can enhance resource management so healthcare providers can have the right human resources, tools and material at the right time, cutting costs and excess. Even insurance companies, payers in the healthcare industry, can employ predictive models in order to predict healthcare costs and hence set their premiums more appropriately to the risk involved. Applying predictive analytics not only brings benefits by improving supply chain management, but it has the potential in saving considerable funds especially when the quality of care is being expected to enhance at a personalized level and reduced costs.
Healthcare Predictive Analytics Market Segment Analysis:
Healthcare Predictive Analytics Market is Segmented on the basis of Component, Deployment Mode, Application, End user, and Region
By Component, Software segment is expected to dominate the market during the forecast period
- Software is the most basic component of the Healthcare Predictive Analytics Market since this is the aspect that does most of the calculations to create predictions for the organizations. These solutions are to operate in parallel with current healthcare frameworks like the EHR to compile and process data in real-time. With time, AI and machine learning, are enhancing predictive analytics software where by generating more accurate and viable prediction. A service component is critical in the deployment and sustenance of predictive analytics solutions. Selecting, procuring, integrating and deploying predictive analytics applications is usually done with the help of a third party solution provider since most of the health care organizations may lack the necessary expertise to do it by themselves. It includes consulting services and training, as well as technical support to make sure healthcare institution will be able to utilize those models.
- While software comes out as more vital in healthcare predictive analytics, hardware does offer a level of relevance as well. This range from servers, storage devices and other requirements that required for processing and analyzing big data. With time, the accumulation of large datasets adds pressure on the overall hardware systems of different healthcare organizations to support the needs of predictive analytics.
By End User, Healthcare Providers segment expected to held the largest share
- The main adopters of the predictive analytics include the healthcare facilities like the hospitals as well as the clinics. They employ these tools to predict the patient conditions so as to enhance treatment and organization planning. For example, stochastic models can predict patients’ admission, which will assist in deployment of required resources in the right measures. Providers also utilize predictive analytics to capture possibilities of patient risk, achieve early management of chronic diseases.
- Insurance companies and all other related healthcare players in the chain use predictive analytics in the evaluation of risks besides fighting frauds and keeping costs under control. Claims data is useful to payers in the following ways; A payer would be able to estimate the future health expenses as they relate to clients and adjust the premiums charged in the process come up with better ways of handling risks. Another advantage of the use of Predictive analytics is that it helps insurers to identify fraudsters from the data collected for the prediction models. Examples of other users of healthcare predictive analytics include governments, pharma majors, and research organizations. Health departments employ predictive scores in monitoring population health data and allocation of funds and resources while using analytics supported drug discovery and development by Drug development firms.
Healthcare Predictive Analytics Market Regional Insights:
North America is Expected to Dominate the Market Over the Forecast period
- North America has emerged as the largest healthcare predictive analytics market because of well-developed healthcare services, the increased use of the digital health system, and favourable regulations. The last few years have witnessed a rising usage of predictive analytics , especially in the United States where the tools have been adopted by many healthcare organizations .
- This has been backed by huge investments in the healthcare information and technology sector and large companies in technology and analytics. Moreover, examining changes in the market of the United States has been witnessing the increasing interest in value-based care, which has led many providers and payers to seek to improve the use of predictive analytics.
Active Key Players in the Healthcare Predictive Analytics Market:
- IBM Watson Health (U.S.)
- Cerner Corporation (U.S.)
- Optum, Inc. (U.S.)
- SAS Institute (U.S.)
- Allscripts Healthcare Solutions (U.S.)
- Health Catalyst (U.S.)
- MedeAnalytics (U.S.)
- Oracle Corporation (U.S.)
- McKesson Corporation (U.S.)
- CitiusTech (U.S.)
- GE Healthcare (U.S.)
- Epic Systems Corporation (U.S.)
- Other Active Players
Key Industry Developments in the Healthcare Predictive Analytics Market:
- In August 2024, Innovacer launched a Government Health AI Data and Analytics Platform (GHAAP) focusing on Public Health and Medicaid Modernization. This platform improves the unifying clinical and non-clinical data while leveraging built-in AI to drive progressive health IT outcomes and transformation.
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Global Healthcare Predictive Analytics Market |
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Base Year: |
2023 |
Forecast Period: |
2024-2032 |
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Historical Data: |
2017 to 2023 |
Market Size in 2023: |
USD 14.65 Billion |
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Forecast Period 2024-32 CAGR: |
25.00% |
Market Size in 2032: |
USD 109.15Billion |
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Segments Covered: |
By Component |
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By Application |
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By End User |
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By Deployment Mode |
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By Region |
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Key Market Drivers: |
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Key Market Restraints: |
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Key Opportunities: |
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Companies Covered in the report: |
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Chapter 1: Introduction
1.1 Scope and Coverage
Chapter 2:Executive Summary
Chapter 3: Market Landscape
3.1 Market Dynamics
3.1.1 Drivers
3.1.2 Restraints
3.1.3 Opportunities
3.1.4 Challenges
3.2 Market Trend Analysis
3.3 PESTLE Analysis
3.4 Porter's Five Forces Analysis
3.5 Industry Value Chain Analysis
3.6 Ecosystem
3.7 Regulatory Landscape
3.8 Price Trend Analysis
3.9 Patent Analysis
3.10 Technology Evolution
3.11 Investment Pockets
3.12 Import-Export Analysis
Chapter 4: Healthcare Predictive Analytics Market by Component
4.1 Healthcare Predictive Analytics Market Snapshot and Growth Engine
4.2 Healthcare Predictive Analytics Market Overview
4.3 Software
4.3.1 Introduction and Market Overview
4.3.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
4.3.3 Key Market Trends, Growth Factors and Opportunities
4.3.4 Software: Geographic Segmentation Analysis
4.4 Services
4.4.1 Introduction and Market Overview
4.4.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
4.4.3 Key Market Trends, Growth Factors and Opportunities
4.4.4 Services: Geographic Segmentation Analysis
4.5 Hardware
4.5.1 Introduction and Market Overview
4.5.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
4.5.3 Key Market Trends, Growth Factors and Opportunities
4.5.4 Hardware: Geographic Segmentation Analysis
Chapter 5: Healthcare Predictive Analytics Market by Application
5.1 Healthcare Predictive Analytics Market Snapshot and Growth Engine
5.2 Healthcare Predictive Analytics Market Overview
5.3 Operations Management
5.3.1 Introduction and Market Overview
5.3.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
5.3.3 Key Market Trends, Growth Factors and Opportunities
5.3.4 Operations Management: Geographic Segmentation Analysis
5.4 Financial Data Analytics
5.4.1 Introduction and Market Overview
5.4.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
5.4.3 Key Market Trends, Growth Factors and Opportunities
5.4.4 Financial Data Analytics: Geographic Segmentation Analysis
5.5 Population Health Management
5.5.1 Introduction and Market Overview
5.5.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
5.5.3 Key Market Trends, Growth Factors and Opportunities
5.5.4 Population Health Management: Geographic Segmentation Analysis
5.6 Clinical Data Analytics
5.6.1 Introduction and Market Overview
5.6.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
5.6.3 Key Market Trends, Growth Factors and Opportunities
5.6.4 Clinical Data Analytics: Geographic Segmentation Analysis
5.7 Others
5.7.1 Introduction and Market Overview
5.7.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
5.7.3 Key Market Trends, Growth Factors and Opportunities
5.7.4 Others: Geographic Segmentation Analysis
Chapter 6: Healthcare Predictive Analytics Market by End User
6.1 Healthcare Predictive Analytics Market Snapshot and Growth Engine
6.2 Healthcare Predictive Analytics Market Overview
6.3 Healthcare Providers
6.3.1 Introduction and Market Overview
6.3.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
6.3.3 Key Market Trends, Growth Factors and Opportunities
6.3.4 Healthcare Providers: Geographic Segmentation Analysis
6.4 Healthcare Payers
6.4.1 Introduction and Market Overview
6.4.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
6.4.3 Key Market Trends, Growth Factors and Opportunities
6.4.4 Healthcare Payers: Geographic Segmentation Analysis
6.5 Other
6.5.1 Introduction and Market Overview
6.5.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
6.5.3 Key Market Trends, Growth Factors and Opportunities
6.5.4 Other: Geographic Segmentation Analysis
Chapter 7: Healthcare Predictive Analytics Market by Deployment Mode
7.1 Healthcare Predictive Analytics Market Snapshot and Growth Engine
7.2 Healthcare Predictive Analytics Market Overview
7.3 On-premise
7.3.1 Introduction and Market Overview
7.3.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
7.3.3 Key Market Trends, Growth Factors and Opportunities
7.3.4 On-premise: Geographic Segmentation Analysis
7.4 Cloud-based
7.4.1 Introduction and Market Overview
7.4.2 Historic and Forecasted Market Size in Value USD and Volume Units (2017-2032F)
7.4.3 Key Market Trends, Growth Factors and Opportunities
7.4.4 Cloud-based: Geographic Segmentation Analysis
Chapter 8: Company Profiles and Competitive Analysis
8.1 Competitive Landscape
8.1.1 Competitive Benchmarking
8.1.2 Healthcare Predictive Analytics Market Share by Manufacturer (2023)
8.1.3 Industry BCG Matrix
8.1.4 Heat Map Analysis
8.1.5 Mergers and Acquisitions
8.2 IBM WATSON HEALTH (U.S.)
8.2.1 Company Overview
8.2.2 Key Executives
8.2.3 Company Snapshot
8.2.4 Role of the Company in the Market
8.2.5 Sustainability and Social Responsibility
8.2.6 Operating Business Segments
8.2.7 Product Portfolio
8.2.8 Business Performance
8.2.9 Key Strategic Moves and Recent Developments
8.2.10 SWOT Analysis
8.3 CERNER CORPORATION (U.S.)
8.4 OPTUM INC. (U.S.)
8.5 SAS INSTITUTE (U.S.)
8.6 ALLSCRIPTS HEALTHCARE SOLUTIONS (U.S.)
8.7 HEALTH CATALYST (U.S.)
8.8 MEDEANALYTICS (U.S.)
8.9 ORACLE CORPORATION (U.S.)
8.10 MCKESSON CORPORATION (U.S.)
8.11 CITIUSTECH (U.S.)
8.12 GE HEALTHCARE (U.S.)
8.13 EPIC SYSTEMS CORPORATION (U.S.)
8.14 OTHER ACTIVE PLAYERS
Chapter 9: Global Healthcare Predictive Analytics Market By Region
9.1 Overview
9.2. North America Healthcare Predictive Analytics Market
9.2.1 Key Market Trends, Growth Factors and Opportunities
9.2.2 Top Key Companies
9.2.3 Historic and Forecasted Market Size by Segments
9.2.4 Historic and Forecasted Market Size By Component
9.2.4.1 Software
9.2.4.2 Services
9.2.4.3 Hardware
9.2.5 Historic and Forecasted Market Size By Application
9.2.5.1 Operations Management
9.2.5.2 Financial Data Analytics
9.2.5.3 Population Health Management
9.2.5.4 Clinical Data Analytics
9.2.5.5 Others
9.2.6 Historic and Forecasted Market Size By End User
9.2.6.1 Healthcare Providers
9.2.6.2 Healthcare Payers
9.2.6.3 Other
9.2.7 Historic and Forecasted Market Size By Deployment Mode
9.2.7.1 On-premise
9.2.7.2 Cloud-based
9.2.8 Historic and Forecast Market Size by Country
9.2.8.1 US
9.2.8.2 Canada
9.2.8.3 Mexico
9.3. Eastern Europe Healthcare Predictive Analytics Market
9.3.1 Key Market Trends, Growth Factors and Opportunities
9.3.2 Top Key Companies
9.3.3 Historic and Forecasted Market Size by Segments
9.3.4 Historic and Forecasted Market Size By Component
9.3.4.1 Software
9.3.4.2 Services
9.3.4.3 Hardware
9.3.5 Historic and Forecasted Market Size By Application
9.3.5.1 Operations Management
9.3.5.2 Financial Data Analytics
9.3.5.3 Population Health Management
9.3.5.4 Clinical Data Analytics
9.3.5.5 Others
9.3.6 Historic and Forecasted Market Size By End User
9.3.6.1 Healthcare Providers
9.3.6.2 Healthcare Payers
9.3.6.3 Other
9.3.7 Historic and Forecasted Market Size By Deployment Mode
9.3.7.1 On-premise
9.3.7.2 Cloud-based
9.3.8 Historic and Forecast Market Size by Country
9.3.8.1 Russia
9.3.8.2 Bulgaria
9.3.8.3 The Czech Republic
9.3.8.4 Hungary
9.3.8.5 Poland
9.3.8.6 Romania
9.3.8.7 Rest of Eastern Europe
9.4. Western Europe Healthcare Predictive Analytics Market
9.4.1 Key Market Trends, Growth Factors and Opportunities
9.4.2 Top Key Companies
9.4.3 Historic and Forecasted Market Size by Segments
9.4.4 Historic and Forecasted Market Size By Component
9.4.4.1 Software
9.4.4.2 Services
9.4.4.3 Hardware
9.4.5 Historic and Forecasted Market Size By Application
9.4.5.1 Operations Management
9.4.5.2 Financial Data Analytics
9.4.5.3 Population Health Management
9.4.5.4 Clinical Data Analytics
9.4.5.5 Others
9.4.6 Historic and Forecasted Market Size By End User
9.4.6.1 Healthcare Providers
9.4.6.2 Healthcare Payers
9.4.6.3 Other
9.4.7 Historic and Forecasted Market Size By Deployment Mode
9.4.7.1 On-premise
9.4.7.2 Cloud-based
9.4.8 Historic and Forecast Market Size by Country
9.4.8.1 Germany
9.4.8.2 UK
9.4.8.3 France
9.4.8.4 The Netherlands
9.4.8.5 Italy
9.4.8.6 Spain
9.4.8.7 Rest of Western Europe
9.5. Asia Pacific Healthcare Predictive Analytics Market
9.5.1 Key Market Trends, Growth Factors and Opportunities
9.5.2 Top Key Companies
9.5.3 Historic and Forecasted Market Size by Segments
9.5.4 Historic and Forecasted Market Size By Component
9.5.4.1 Software
9.5.4.2 Services
9.5.4.3 Hardware
9.5.5 Historic and Forecasted Market Size By Application
9.5.5.1 Operations Management
9.5.5.2 Financial Data Analytics
9.5.5.3 Population Health Management
9.5.5.4 Clinical Data Analytics
9.5.5.5 Others
9.5.6 Historic and Forecasted Market Size By End User
9.5.6.1 Healthcare Providers
9.5.6.2 Healthcare Payers
9.5.6.3 Other
9.5.7 Historic and Forecasted Market Size By Deployment Mode
9.5.7.1 On-premise
9.5.7.2 Cloud-based
9.5.8 Historic and Forecast Market Size by Country
9.5.8.1 China
9.5.8.2 India
9.5.8.3 Japan
9.5.8.4 South Korea
9.5.8.5 Malaysia
9.5.8.6 Thailand
9.5.8.7 Vietnam
9.5.8.8 The Philippines
9.5.8.9 Australia
9.5.8.10 New Zealand
9.5.8.11 Rest of APAC
9.6. Middle East & Africa Healthcare Predictive Analytics Market
9.6.1 Key Market Trends, Growth Factors and Opportunities
9.6.2 Top Key Companies
9.6.3 Historic and Forecasted Market Size by Segments
9.6.4 Historic and Forecasted Market Size By Component
9.6.4.1 Software
9.6.4.2 Services
9.6.4.3 Hardware
9.6.5 Historic and Forecasted Market Size By Application
9.6.5.1 Operations Management
9.6.5.2 Financial Data Analytics
9.6.5.3 Population Health Management
9.6.5.4 Clinical Data Analytics
9.6.5.5 Others
9.6.6 Historic and Forecasted Market Size By End User
9.6.6.1 Healthcare Providers
9.6.6.2 Healthcare Payers
9.6.6.3 Other
9.6.7 Historic and Forecasted Market Size By Deployment Mode
9.6.7.1 On-premise
9.6.7.2 Cloud-based
9.6.8 Historic and Forecast Market Size by Country
9.6.8.1 Turkiye
9.6.8.2 Bahrain
9.6.8.3 Kuwait
9.6.8.4 Saudi Arabia
9.6.8.5 Qatar
9.6.8.6 UAE
9.6.8.7 Israel
9.6.8.8 South Africa
9.7. South America Healthcare Predictive Analytics Market
9.7.1 Key Market Trends, Growth Factors and Opportunities
9.7.2 Top Key Companies
9.7.3 Historic and Forecasted Market Size by Segments
9.7.4 Historic and Forecasted Market Size By Component
9.7.4.1 Software
9.7.4.2 Services
9.7.4.3 Hardware
9.7.5 Historic and Forecasted Market Size By Application
9.7.5.1 Operations Management
9.7.5.2 Financial Data Analytics
9.7.5.3 Population Health Management
9.7.5.4 Clinical Data Analytics
9.7.5.5 Others
9.7.6 Historic and Forecasted Market Size By End User
9.7.6.1 Healthcare Providers
9.7.6.2 Healthcare Payers
9.7.6.3 Other
9.7.7 Historic and Forecasted Market Size By Deployment Mode
9.7.7.1 On-premise
9.7.7.2 Cloud-based
9.7.8 Historic and Forecast Market Size by Country
9.7.8.1 Brazil
9.7.8.2 Argentina
9.7.8.3 Rest of SA
Chapter 10 Analyst Viewpoint and Conclusion
10.1 Recommendations and Concluding Analysis
10.2 Potential Market Strategies
Chapter 11 Research Methodology
11.1 Research Process
11.2 Primary Research
11.3 Secondary Research
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Global Healthcare Predictive Analytics Market |
|||
|
Base Year: |
2023 |
Forecast Period: |
2024-2032 |
|
Historical Data: |
2017 to 2023 |
Market Size in 2023: |
USD 14.65 Billion |
|
Forecast Period 2024-32 CAGR: |
25.00% |
Market Size in 2032: |
USD 109.15Billion |
|
Segments Covered: |
By Component |
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|
|
By Application |
|
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|
By End User |
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By Deployment Mode |
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|
By Region |
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Key Market Drivers: |
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Key Market Restraints: |
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Key Opportunities: |
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Companies Covered in the report: |
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