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Healthcare Predictive Analytics Market To Reach USD 28.77 Billion By 2027 With CAGR of 28.9 % | Reports and Data

Exponentially increasing healthcare database volume, growing investments in digital technology to effectively manage available information, rising adoption of electronic health records to effectively manage patients’ health, increasing adoption of advanced analytics, and growing demand for cost-cutting tools such as healthcare predictive analytics software are all factors that will contribute to the market’s high CAGR during the forecast period.

As reported by Reports and Data, the global Healthcare Predictive Analytics market was valued at USD 3.74 billion in 2019 and is predicted to reach USD 28.77 billion by 2027, growing at a compound annual growth rate (CAGR) of 289 percent. The research is focused on healthcare predictive analytics, which is an analytical methodology that analyses and predicts outcomes for individual patients using statistical methods and technology, operating on huge volumes of relevant data for each patient. Healthcare Predictive Analytics is widely employed in the healthcare business all around the world and is becoming increasingly popular. Among the most significant recent developments in the healthcare business is the recent advent of Healthcare Predictive Analytics as a time-saving and cost-reducing tool. By this, a growing number of businesses and hospitals are implementing Healthcare Predictive Analytics to save time and money. Using Cerner’s Continuous Advancement Services, West Tennessee Healthcare, for example, was able to save more than 8,000 hours per year through an optimization effort that reduced the number of discrete task tests that nurses were required to complete on a timely basis.

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Techniques such as knowledge discovery, data mining, and machine learning have recently gained a great deal of attention, owing to the increasing amount of data available and the increasing need to base reasoning on evidence derived from physical measurements. Following this development, data-driven approaches to knowledge extraction were developed as a complementary approach to more traditional human-centered approaches. These approaches enable systems to generate new knowledge, update previously stored information, and optimize performance without the need for human intervention or reprogramming. An extremely active research field, the organization (or representation) of medical knowledge is distinguished by an extensive range of tools, models, and languages. Combined with the availability of increasing computer capabilities, this allows one to specify and emulate systems that are becoming increasingly complex. A few widely used basic representational systems in the business today are frame representations, semantic networks, and conceptual graph representations, to name a few examples. A rise in the volume of healthcare databases, rising investments in digital technology to effectively manage available information, rising adoption of electronic health records to effectively manage patients’ health, increasing adoption of advanced analytics, and growing demand for cost-cutting tools such as healthcare predictive analytics are some of the key factors driving the growth of the Healthcare Predictive Analytics market in the industry.

However, the high cost of analytics solutions, a scarcity of trained employees, and operational gaps between payers and providers will be the most significant impediments to market growth throughout the period 2019-2027, according to the report. Individual health and safety are jeopardized by issues such as erroneous diagnoses and poor medication adherence. Healthcare Predictive Analytics, which employs individualized treatment regimens, follow-up notifications, and real-time diagnosis monitoring, is now able to ease, if not eliminate, these problems in the healthcare setting. In addition to increasing the quality of life for patients with chronic diseases and their families, pervasive and context-aware monitoring technologies are helping to save healthcare expenditures in the long run while also improving the overall quality of care. Currently, the acquisition and representation of knowledge in clinical decision support systems is a rapidly expanding research field, characterized by more difficult modeling and software engineering issues. A broadening of the scope of creating knowledge-based systems in medicine has occurred in recent years, moving beyond simple diagnostic tasks to encompass the broader issue of patient management, leading to improved integration in hospital information systems. These advantages are projected to have a beneficial impact on the market for Healthcare Predictive Analytics.