The global predictive disease analytics market size accounted for USD 3.1 billion in 2022 and is projected to reach around USD 14.09 billion by 2032, growing at a CAGR of 16.40% from 2023 to 2032.
Predictive disease analytics involves the use of data analysis, statistical modeling, and machine learning techniques to predict the occurrence, progressions, and outcomes of diseases. This field leverages various types of data, including clinical records, genetic information, environmental factors, and lifestyle data, to generate insights that can aid in the early detection, prevention, and management of diseases.
The predictive disease analytics market is driven by several factors including the growing prevalence of chronic disease, advancement in technology, growing health data availability, emphasis on personalized medicine, rising healthcare costs and the need for efficiency, and increasing collaboration and partnerships.
Furthermore, the growing product launch is expected to drive market growth during the forecast period. For instance, in October 2022, researchers from the University of Florida Health and the University of Pennsylvania Perelman School of Medicine (Penn Medicine) revealed their plans to create a set of predictive analytics algorithms that would determine which individuals are most likely to suffer from specific rare health conditions. Owing to their hidden nature, confusing symptoms, and low prevalence, rare diseases can be challenging to identify. Patient outcomes might be adversely affected when individuals with rare conditions go years without a diagnosis or treatment.
The researchers' goal was to create artificial intelligence (AI)-based models that might identify people at risk of developing one of these diseases to prevent this. They are contributing to this activity with their technology, called predictive analytics via Networked Distributed Algorithms for multi-system diseases (PANDA). The National Institutes of Health (NIH) provided funding for the experiment for $4.7 million.
The research team will use the award to create a set of machine-learning (ML) algorithms that will determine which patients are susceptible to two types of spondylarthritis, such as ankylosing spondylitis and psoriatic arthritis, and five types of vasculitis, which are all regarded as rare diseases in the US.
North America is expected to dominate the market. The market growth in the region is attributed to the growing geriatric population along with the rising prevalence of chronic disease. According to the study provided by the National Institute of Health, between 2020 and 2050, there will be a 61.11% increase in the number of Americans 50 years of age and older, from 137.25 million to 221.13 million. The number of people 50 years of age and older who have at least one chronic health condition is predicted to rise from 71.522 million in 2020 to 142.66 million in 2050, a 99.5% increase.
Simultaneously, it is anticipated that the number of people with multimorbidity will rise from 7.8304 million in 2020 to 14.968 million in 2050, a 91.16% increase. According to race, 64.6% of non-Hispanic white people 61.47% of non-Hispanic black people and 64.5% of Hispanic and other people will probably have a chronic medical condition by 2050. Moreover, the increasing government initiatives and incentives are expected to drive market growth over the forecast period.
In the region, the US captured a maximum market share over the projected period. The market expansion in the country is owing to the presence of major manufacturers such as EVERSANA. The company actively launched their novel product related to predictive disease analytics which will drive the market growth in the country. For instance, the power of real-world data is being brought to chronic disease research, evidence development, and patient assistance with the announcement of EVERSANATM, the first next-generation commercial services provider to the global life sciences sector.
To improve the chronic, comorbid patient experience, life sciences stakeholders can conduct regulatory-grade research studies, generate evidence, and offer data-driven insight and proactive support through EVERSANA's Chronic Disease Real-World Data (RWD) solution. This is made possible by the ACTICS by EVERSANA technology platform in conjunction with strong outsourced service capabilities in value and evidence research and patient hub services.
| Report Coverage | Details |
| Market Revenue in 2023 | USD 3.59 Billion |
| Projected Forecast Revenue by 2032 | USD 14.09 Billion |
| Growth Rate from 2023 to 2032 | CAGR of 16.40% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Drivers
Growing prevalence of chronic disease
The rising incidence of chronic diseases, such as diabetes, cardiovascular diseases, and cancer, has fueled the demand for predictive analytics to enable early detection and proactive management. For instance, as per the data published by the Australian Institute of Health and Welfare, all kinds of cardiovascular disease (CVD) cost Australians 670,000 years of healthy life (DALY) in 2022, or 19.9 per 1,000 people.
Technological advancements
Continuous advancements in technology, especially in areas like machine learning, Artificial Intelligence, and data analytics, have empowered more sophisticated and accurate predictive modeling for disease diagnosis and prognosis.
Restraints
Data privacy and security concerns
Healthcare data, especially patient information, is highly sensitive. Concerns about data privacy and security can hinder the widespread adoption of predictive disease analytics. Ensuring compliance with regulations such as HIPAA is crucial. For instance, the HHS Office for Civil Rights has received reports of 5,150 healthcare data breaches involving 500 or more records between 2009 and 2022. As a result of the breaches, 382,262,109 healthcare records were exposed or improperly disclosed. That is equivalent to almost 1.2 times the population of the US. Approximately one healthcare data breach involving 500 or more records was reported every day in 2018. Thus, the aforementioned stats negatively impacted the market growth.
Cost and resource constraints
Implementing predictive disease analytics solutions may require significant investments in technology, staff training, and infrastructure. Smaller healthcare organizations, in particular, may face budgetary constraints that hinder their ability to adapt these technologies.
Opportunities
Growing partnerships
The increasing partnership among the market players is expected to offer a lucrative opportunity for market growth during the forecast period. For instance, in February 2023, with strategic partnerships with Tangent Works, a leading provider of predictive modeling and advanced forecasting, Tech Data, a TD SYNNEX company, announced the expansion of its AI (artificial intelligence) and predictive analytics solutions portfolio. By providing Tangent Works' cloud-native technologies, Tech Data will further strengthen its AI and predictive analytics solutions portfolio and accelerate the adoption of AI projects in the industry.
Remote patient monitoring
Advances in wearable devices and remote monitoring technologies provide opportunities for continuous data collection. Predictive analytics can leverage this real-time data to monitor patient health remotely, enabling early detection of changes in health status and reducing the need for hospital admissions. Thus, this is expected to offer a potential opportunity for market growth over the forecast period.
Market Segmentation
By Component
By Deployment
By End Users
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