Healthcare Predictive Analytics Market Size to Reach USD 87.5 Billion by 2030 Driven By Integration of AI and Big Data – Exclusive Report By Acumen Research and Consulting

LOS ANGELES, July 06, 2022 (GLOBE NEWSWIRE) -- The Global Healthcare Predictive Analytics Market Size was valued at USD 9.5 Billion in 2021 and is estimated to reach the market value of USD 87.5 Billion by 2030, growing at a CAGR of 28.2% from 2022 to 2030.

Predictive analytics in healthcare can help identify early symptoms of weakening patients in the ICU and general ward, recognize at-risk patients at home to avoid hospital readmissions and inhibit avoidable medical equipment downtime. The growing prevalence of chronic disorders and the increasing expenditures on these diseases has been troublesome for people with low and middle income. This factor has exploded the healthcare predictive analytics market revenue throughout the forecast timeframe from 2022 to 2030. According to the Centers for Disease Control and Prevention (CDC), people with chronic and mental health conditions account for 90% of the United States' USD 3.8 trillion in yearly healthcare expenditure. As a result, the potential cost savings from lowering chronic disorder treatment are substantial. For instance, more than 877,500 Americans die each year from heart disease or stroke, accounting for one-third of all deaths. These diseases also have an economic impact, costing the US healthcare system USD 216 billion per year. Furthermore, in the United States each year, more than 1.7 million people are diagnosed with cancer, and nearly 600,000 die from it, making it the second-largest cause of death. Cancer care costs continue to rise and are expected to reach nearly USD 240 billion by 2030. Furthermore, over 34.2 million Americans have diabetes, and another 88 million adults in the U.S. have prediabetes, a condition that puts them at risk of developing type 2 diabetes. Diabetes was estimated to cost USD 327 billion in medical costs and lost productivity in 2017.

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Report Coverage:

Impact of COVID-19 Pandemic on Global Healthcare Predictive Analytics Market

Hospitals and health systems are reaching alarming rates on resources and capacity as COVID-19 spreads rapidly among patient populations. Real-time analytics tools are increasingly being used by businesses to track and predict healthcare demand. Predictive analytics was used by several institutions, including hospitals, to determine the likelihood of a patient developing severe symptoms, their infection/disease trajectory, and a number of other factors. For instance, in April 2020 - Cleveland Clinic and SAS developed innovative models to help hospitals forecast patient volume, ventilator availability, bed capacity, and other factors in the wake of the COVID-19 pandemic. The models provide hospitals and health departments with timely, reliable information to help them optimize healthcare delivery for SARS-CoV-2 and other patients, as well as forecasting impacts on supply chain, finance, and other critical areas. As a result, the rise of the COVID-19 outbreak was considered one of the recent trends in the healthcare predictive analytics market that fueled the demand rapidly.

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Global Healthcare Predictive Analytics Market Dynamics

The communication gap between caregivers and patients has narrowed with the emergence of data-rich tools like electronic health records (EHR), eHealth, mHealth, and mobile applications. These tools generate incredible data that can be used to develop personalized treatments. Government initiatives to increase EHR adoption, the advent of big data in healthcare, rising venture capital investments, increasing pressure to cut healthcare spending, improving patient outcomes, and the surging prominence of real-world evidence are all factors that are expected to boost the worldwide predictive healthcare analytics market growth. However, a scarcity of qualified IT professionals, as well as concerns about data integrity and security, will limit the market growth. Furthermore, the advent of big data, AI, and IoT coupled with the rising technological improvements in the healthcare infrastructure are likely to create significant impetus in the coming future.

Worldwide Healthcare Predictive Analytics Market Segmentation

The global healthcare predictive analytics market has been segmented into component, application, end-user, and geography. Based on the component, the market is divided into software, hardware, and services. Among them, the services segment achieved the maximum share in 2021.

By application, the market is categorized as financial analytics, clinical, population health, and operations management. Based on the application, the financial analytics segment accounted for a significant market share in 2021. Furthermore, the end-user segment is split into payer, provider, and others. The payer will dominate the entire end-user segment during the forecasted years 2022 to 2030. Furthermore, the provider is likely to attain a substantial growth rate in the coming years.

Healthcare Predictive Analytics Market Regional Overview

The global healthcare predictive analytics market is divided into five regions: North America, Europe, Latin America, Asia-Pacific, and the Middle East and Africa. North America accounted for a sizable portion of the market in 2021. The enormous shares in the region are attributed to the factors such as growing pressure to reduce healthcare costs, the surge in chronic disorders, technological advancements, a significant amount of VC investments, and high product and service availability. On the other hand, the Asia-Pacific region is likely to register the highest CAGR during the projected timeframe. This is because of improved healthcare infrastructure and the widespread adoption of healthcare predictive analytics to minimize rising healthcare costs.

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Healthcare Predictive Analytics Market Players

Some healthcare predictive analytics companies covered globally include All scripts, Cerner Corp., IBM, McKesson Corp., Mede Analytics, Inc., Optum, Inc., Oracle, SAS, Verisk Analytics, Inc., and Wipro Limited.

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