Market Overview:

Healthcare predictive analytics is generally used in the analysis of current data in order to predict future by using statistics, data mining, modeling, machine learning, and artificial intelligence. Increasing efficiency in the healthcare sector, the rising demand to curtail healthcare cost, and the advent of evidence-based and personalized medicine are some of the primary growth stimulants of the healthcare predictive analytics market. Rising importance of healthcare, especially in emerging nations is likely to generate new growth opportunities for the market in the foreseeable future.

Developing economies across the world are facing issues owing to the increasing medication cost. Governments are thus taking efforts to offer cost-effective services to the consumers at better quality. These efforts are likely to stimulate the predictive analytics market in the healthcare industry. The soaring demand for personalization for patient care, advancing technology, and rising awareness created by the top players are considered to encourage the market growth to a large extent.The global Healthcare Predictive Analytics Market is expected to exhibit a strong 29.3% CAGR over the forecast period (2018 to 2023), according to the latest research report from Market Research Future (MRFR).

Growing use of data-driven clinical decision support systems in the healthcare sector is likely to be a major driver for the global healthcare predictive analytics market over the forecast period. Clinical decision support systems are being welcomed in the healthcare sector around the world, as they provide the physician with a second layer of security, allowing more accurate diagnoses and better rehabilitation. Increasing applications of healthcare predictive analytics in population health management is also likely to be a major driver for the global healthcare predictive analytics market over the forecast period.

Competitive Analysis:

• IBM
• Verisk Analytics Inc.
• Cerner Corporation
• McKesson Corporation
• Oracle
• SAS
• Allscripts
• MedeAnalytics Inc
• Optum Inc
• Inovalon
• SCIO Health Analytics
• Health Catalyst
• Verscend Technologies Inc.
• Wipro Limited
• CitiusTech Inc

Segmentation:

The global healthcare predictive analytics market report is segmented on the basis of application, end use, and component.

By application, the global healthcare predictive analytics market is segmented into operations management, financial, population health management, and clinical. The operations management segment is further sub-segmented into demand forecasting, workforce planning and scheduling, inpatient scheduling, and outpatient scheduling. The financial segment is further classified as revenue cycle management, fraud detection, and others. The population health segment is further sub-segmented as population risk management, patient engagement, population therapy management, and other applications. The clinical segment includes quality benchmarking, patient care enhancement, and clinical outcome analysis and management.

By end use, the global healthcare predictive analytics market is segmented into payers, providers, and others.

By component, the global healthcare predictive analytics market is segmented into services, software, and hardware.

Regional Analysis:

Healthcare predictive analytics is generally used in the analysis of current data in order to predict future by using statistics, data mining, modeling, machine learning, and artificial intelligence. Increasing efficiency in the healthcare sector, the rising demand to curtail healthcare cost, and the advent of evidence-based and personalized medicine are some of the primary growth stimulants of the healthcare predictive analytics market. Rising importance of healthcare, especially in emerging nations is likely to generate new growth opportunities for the market in the foreseeable future.

Developing economies across the world are facing issues owing to the increasing medication cost. Governments are thus taking efforts to offer cost-effective services to the consumers at better quality. These efforts are likely to stimulate the predictive analytics market in the healthcare industry. The soaring demand for personalization for patient care, advancing technology, and rising awareness created by the top players are considered to encourage the market growth to a large extent.

Long before big data became cool in technology conversations, a baseball team manager and a Harvard Economics graduate applied Big Data analytics to baseball to create sporting history. The concept was made popular by the movie, Moneyball, which recounts the compelling tale of the resurgence of the cash-strapped Oakland A’s which had just lost its star players to free agency. Billy Beane, the then General Manager for the A’s, and Paul DePodesta, the Assistant General Manager, began analyzing baseball statistics to value and purchase players, which led to the team winning 20 consecutive games between August and September 2002. 

The Moneyball theory witnessed rapid adoption across the professional sporting community and laid the foundation of data-driven decision-making. In the world of business, the Moneyball theory has gone on to influence how organizations go about gathering business intelligence. Data-driven decision-making is now commonplace across key business functions including marketing, information technology, finance, and supply chain. Ironically as the one business function that has historically been the custodian of most organizational data, HR has been slow to adopt data-driven, objective decision-making. 

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However, over the past few years, factors such as digital disruption, increased competition for talent, and changing workforce models have compelled HR leaders to embrace evidence-based decisions that can be linked to key business objectives. As HR’s role evolves to become a strategic business partner to the organization, the value of data and analytics cannot be understated. 

The Rise of Predictive Analytics in HR

While data analysis in HR is not exactly a new concept, too often, HR focuses on what has already happened, and in doing so, loses out visibility into what will happen next. Specific insights on what to expect in the future is a tremendous competitive advantage for organizations, and HR can leverage this technology to transform the business impact of the function drastically. Before we go any further, let’s look at what predictive analytics means within the HR context and explore some of its important use cases. 

What is Predictive Analytics in HR? 

Predictive analytics in HR is defined as the application of data, statistical modeling, and machine learning methods to historical data to identify the likelihood of future outcomes. In other words, predictive analytics helps organizations predict future outcomes of an event. For example, if an HR team wants to determine the rate of attrition for the next two fiscal years, it can leverage predictive analytics to identify the future turnover rate based on historical patterns within existing data. Using these insights, HR can then proactively engage and retain employees and reduce turnover. 

With affordable, easy-to-use software becoming available, HR teams now can turn historic workforce data into a competitive advantage. As the predictive analytics vendor ecosystem matures, organizations no longer need to rely on statisticians and mathematicians to use and understand how predictive HR analytics software functions. Predictive analytics software enables HR professionals to gather real-time insights into the efficiency of current HR processes and policies, how employees interact with their work and its business impact, future recruitment needs and the best course of action, and ultimately deliver an exceptional, personalized employee experience. 

3 Examples of Predictive Analytics in HR 

In response to the developments in predictive analytics technology, HR teams have begun leveraging it to drive continuous improvement and build a predictable talent pipeline. Here are a few innovative ways that organizations have successfully deployed predictive analytics in HR: 

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