Healthcare Cognitive Computing Market Size & Share, by Technology (Natural Language Processing, Machine Learning, Automated Reasoning, Data Extraction, Interpretation, Language Processing & Training, Automated Planning, Computer Vision, Handwriting & OCR, Speech Recognition); Deployment Model (Cloud-based, On-premise); End-user (Hospitals, Pharmaceuticals, Medical Devices, Insurance) - Global Supply & Demand Analysis, Growth Forecasts, Statistics Report 2024-2036

Growth Drivers

• Infusion of Digital Health in Hospitals – In the recent period, a high focus is shifted toward the adoption of electronic health records (EHR) in hospitals and clinics. As a result, the integration of healthcare cognitive computing has been on the constant surge as it allows to improve the quality of healthcare by increasing the precision and clarity of medical records along with the reduction in the frequency of medical errors. Through recent data, it has been found that since 2011 only 28% of hospitals and 34% of physicians had implemented an EHR in the United States. Since then, there has been a significant 10-year advancement till 2021 with almost 96% of non-federal acute care hospitals and approximately four out of five office-based physicians approximately 78% had embraced certified EHRs.
• High Amount of Data Present in the Healthcare Sector – Every day in the healthcare sector, a huge amount of data is generated in the form of published articles, blogs, books, images, audio, and video. An enormous amount of data needs high-quality healthcare management services such as healthcare cognitive computing for analyzing and extracting the needed information for future reference.  For instance, Icahn School of Medicine at Mount Sinai students are researching breast and ovarian cancer by analyzing enormous volumes of data from more than 2,000 Genetic sequences. The research takes place on a safe cloud-based platform by Amazon Web Services, and the data set is of volume more than 100 terabytes.
• Favorable Initiatives to Improve the Healthcare System – In the past few years, governments of different nations are initiating programs for enhancing healthcare supply chain management through the adoption of the latest technology such as healthcare cognitive computing. Such initiatives are expected to increase the awareness level of healthcare cognitive computing across the world and help in increasing the adoption rate of it in global hospital settings.  Recently, the government of the United States has initiated the “Healthy People 2030” plan which has 355 specific goals for enhancing the standard of healthcare and guaranteeing that everyone has access to the services they require.
• Increasing Prevalence of Chronic Disease – Growing cases of chronic disease have increased hospital visits and admission.  As a result, the amount of data on patients’ health records has subsequently risen along with the demand for better patient care and personalized treatments. This factor is anticipated to bring favorable opportunities for the adoption of healthcare cognitive computing across various healthcare settings and ultimately aid the development of the global market in the near future. According to the World Health Organization before the age of 70, around 17 million people per year pass away from an NCD. Non-communicable diseases (NCDs) account for 41 million annual fatalities or 74% of all fatalities worldwide.
• Growing Adoption of Technology in Healthcare - Executive automation familiarity in healthcare increased worldwide from around 50% to 66% between 2019 and 2020, while the deployment of automation solutions rose from nearly 23% to 34%.
• Integration of the Internet of Things (IoT) in Healthcare - By integrating advanced technology, including IoT is projected to enable the global healthcare industry to save up a lot of investment, from USD 250 billion to nearly USD 420 billion by 2030. Moreover, it is expected to benefit inventory management by giving nearly 100% compliant, secure, and reliable management.

Challenges

• High cost of implementing the healthcare cognitive computing
• Risk to data security present in the automated system- The potential of data breaches and other data privacy and security issues also exists in the healthcare sector. In order to make the most of their interaction, patients and professionals exchange a lot of private information. With the integration of automation, inappropriate data disposal, data loss, hacking IT incidents, data theft, unauthorized access, and other issues are among the most frequent dangers to data privacy and security.
• Absence of analytics infrastructure and data systems.