Prescriptive analytics is one of the big 3s of Big Data analytics along with descriptive analytics and predictive analytics. These are interrelated solutions that can be used by companies to efficiently utilize the big data that they are currently equipped with. One of the primary tasks is to know when and where to implement which kind of analytics. While descriptive analytics talks about what has happened' and predictive analytics emphasizes on what can happen in the future from current patterns,' the prescriptive analysis gives a solution to what should a business do to maximize its key metrics.' Thus prescriptive analytics is the stage which provides action plans on how to proceed in future based on descriptive and predictive datasets. Aurora Health Care systems saved $6million annually by applying prescriptive analytics to reduce re-admission rate by 10%. Since prescriptive analytics is comparatively complex, many organizations are yet to utilize the potential. However, large organizations are managing their day-to-day operations like scheduling inventory and optimizing production using prescriptive analytics.
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Since healthcare prescriptive analytics is the next step of predictive analytics, many drivers and inhibitors are similar for both markets. The primary market driver is the need to implement efficient process regarding cost cutting and reduction of unnecessary efforts. This will ensure utilization of resources in a proper manner. Furthermore, because of integration of Big Data in healthcare, the requirement of prescriptive analysis to create action plans based on past trends will expand. Additionally, with increasing advancements in technology, need to develop prescriptive analytics will form the competitive edge and thus, because of movement towards prescriptive analytics, growth will be further enhanced. However, due to high costs associated with the initial investment required to establish prescriptive analytics system, the increase will get negatively affected. The issue of initial investment is further escalated by the costs associated with training labor to understand the nuances of technology and therefore, lack of availability of skilled IT labors acts as another inhibitor of the market growth. Apart from these factors, growing concerns about data privacy and security of the same impacts the procurement of necessary data required to generate data based solutions.
The major forms of segmentation of Healthcare Predictive Analytics Market Include segmentation by component, by application, by deployment, by end-user and by geography. By component, software, hardware and services cover the entire market. By application, a market is categorized as Clinical Analysis, Financial Analysis, Operational Analysis and Research Analysis. Segmentation through deployment includes On-Premise, Web-based and Cloud-based. By end-user, segmentation comprises Physicians, Private Organizations, Hospitals, Clinics and, Government and Research Institutions. By geography, North America, Europe, Asia-Pacific and, Middle East and Africa form the entire market. Currently, Clinical Analytics segment is forecasted to have the maximum growth in application segmentation. Although Cloud-based services are gaining traction massively, yet, On-Premise solutions have captured the highest market share in deployment category.
North America is leading the market because of the presence of giants like IBM and Oracle. Europe is following North America with second most significant market share. Asia-Pacific region is expected to grow at highest growth rate.
The critical players in the market include IBM, Cerner, Oracle, MedeAnalytics Inc., McKesson Corporation, Optum Inc. and AllScripts Healthcare Solutions Inc.
Report ContentsRegional AnalysisReport Highlights
Market Drivers, Restraints and Opportunities
Market Size & Forecast 2016 to 2022
Supply & Demand Value Chain
Market - Current Trends
Competition & Major Companies
Technology and R&D Status
Porters Five Force Analysis
Strategic and Critical Success Factor Analysis of Key Players
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