The Healthcare Cloud Based analytics market is projected to register a CAGR of 10.2% during the forecast period (2022-2027).
Data analytics in healthcare has been a buzzword among industry leaders for years. The ability to use AI, machine learning and other technologies to extract actionable insights from massive amounts of data is a fascinating prospect, but it has proven notoriously difficult to achieve. With the outbreak of the COVID-19 pandemic, healthcare organizations were forced to confront their many data challenges, from collection to accuracy and analysis. As a result, the industry has increased its efforts to improve data analytics capabilities, with tech firms playing a key role.
Market players are rapidly developing artificial intelligence and data analytics in healthcare in order to contain COVID-19, paving the way for the technologies' future. Predictive analytics tools are assisting healthcare organizations in avoiding negative outcomes, resource shortages, and other COVID-19-related consequences. Researchers and providers have been able to analyze trends, monitor patient populations, and begin to address long-standing issues in the healthcare industry due to the massive amount of data generated by the pandemic. In the midst of a healthcare crisis, the ability to predict future events is critical, and predictive analytics tools can help healthcare organizations do just that.
To better understand which patients are at risk, where resources are most needed, and where the disease is likely to spike next, organizations are increasingly turning to predictive models. The most important use case for predictive analytics during the pandemic is determining which patients are most at risk of contracting the virus - as well as which individuals are most likely to have poor outcomes from COVID-19.
As per the article published in September 2020, a team from Mount Sinai recently developed a predictive analytics model based on three clinical features: age, minimum oxygen saturation, and type of patient encounter. These three characteristics can accurately classify COVID-19 patients as likely to live or die, according to the findings. Cleveland Clinic researchers set out to accomplish a similar goal in June 2020. The researchers created a predictive analytics model to predict a patient's likelihood of testing positive for COVID-19 and the disease's potential outcomes. A team from Johns Hopkins University School of Medicine attempted to predict COVID-19 outcomes using analytics tools. Researchers recently developed a predictive analytics model that can predict the likelihood of a patient's condition deteriorating while in the hospital. The patient's age, BMI, lung health and chronic disease, and vital signs were all considered risk factors.
As the pandemic progresses, predictive analytics will play an increasingly important role in tracking the virus's impact, from patient outcomes to hotspots of disease spread. The global health crisis has only served to emphasize the importance of predictive analytics in healthcare, potentially speeding up the adoption of these tools into standard care in the future.
Over the past decade, electronic health records have been widely adopted in hospitals and clinics worldwide. It helps to improve patient care and improves overall efficiency. Big Data in healthcare is being used to predict epidemics, cure diseases, improve the quality of life and avoid preventable deaths.
With the world’s increasing population and everyone’s wish to live longer, models of treatment delivery are rapidly changing, and many of the decisions behind those changes are being driven by data. The drive helps to understand a patient as much as possible, as early in their life as possible, hopefully picking up warning signs of serious illness at an early stage that treatment is far simpler (and less expensive) than if it had not been spotted until later.
Besides, the use of connected devices that generate huge amounts of data is expected to increase significantly; this raw data can be analyzed to make better enterprise decisions. According to European Telecommunications Network Operators' Association (ETNO), the number of IoT active connections in healthcare is expected to reach 10.34 million by 2025. Such growth further strengthens the demand for data analytics and tools to gain data-driven insights into the industry.
Descriptive analytics is the quantitative collection and transformation of data information, including summaries of hospitalizations, the number of disease cases, and length of stay in the hospital. These are reporting tools that focus on data visualization, standardized reporting, and historic trend analysis. Most of the healthcare organizations are extensive users of descriptive analytics that provide a clear understanding of the past medical history and its visualization through key performance metrics or other data in reports or dashboards. This is carried out by describing the current situation and past performance.
Descriptive analytics helps in categorizing the customers by product preference and life stage. Descriptive studies emphasize features of new diseases or accessing the health status of communities. It basically describes reported data but fails in predicting the results or the future. Epidemiologists and clinicians use descriptive analytics to search for clues to the cause of new diseases.
However, descriptive analytics has limitations, like temporal associations between putative cause and effects that may be unclear. Misinterpretation of data may affect public health. Providers engaging in descriptive analytics have the ability to generate reports that illuminate events that have already occurred, resources that have already been consumed, or patients who have a new diagnosis on their charts. Owing to these growing benefits of technology, the market is expected to grow in the future.
With the rapid spread of COVID-19, many hospitals and health systems were faced with the prospect of unexpected spikes in patient volume, putting a strain on resources and putting additional strain on staff. Organizations have implemented predictive tools that can help allocate resources to better plan for these potential surges. Cleveland Clinic researchers developed a predictive model that can predict patient volume, bed capacity, ventilator availability, and other metrics as of late April 2020. The model provides timely, accurate data to help COVID-19 and other patients get the best care possible.
A critical aspect of the pandemic is predicting the needs of hospital staff. A Cedars-Sinai team created a machine learning tool that can forecast data points related to the COVID-19 pandemic and predict staffing requirements. With an accuracy of 85 percent to 95 percent, the platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for rising COVID-19 patient volumes.
The United States healthcare analytics market holds the largest market share in the North American region, as the healthcare infrastructure in the United States is experiencing a positive trend in the healthcare analytics domain. The substantial growth avenues in the region can be attributed to the presence of several prominent players in the region. Besides, the players are also coming up with new products which are further supporting the market growth. For instance, in July 2021, UnitedHealthcare introduced the use of predictive analytics to help improve well-being, lower costs and drive engagement in clinical-intervention programs by addressing social determinants of health for people in some employer-sponsored benefit plans. Building on similar initiatives for UnitedHealthcare Medicare and Medicaid beneficiaries, the new capability adds the use of predictive analytics to help efficiently identify people in need of support related to social determinants of health. Also, in October 2020, Prognos Health, a leading clinically-focused healthcare analytics firm, launched a new integrated platform to transform healthcare analytics. Pharmaceutical companies, providers, and payers can pinpoint insights at decision-making speeds and deliver a clear picture of opportunities to improve outcomes along the patient journey with prognosFACTOR, which unifies data across the healthcare landscape.
The market is fragmented and competitive and consists of several major players. The major players operating in the market are IBM, Allscripts Healthcare Solutions, Cerner Corporation, Mckesson Corporation, and Koninklijke Philips N.V. Majority of the healthcare cloud-based analytics services are being provided by the global key players. Market leaders with more funds for research and a better distribution system have established their position in the market. Moreover, Asia-pacific is witnessing an emergence of some small players due to the rise of awareness. This has also helped the market grow.
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Data analytics in healthcare has been a buzzword among industry leaders for years. The ability to use AI, machine learning and other technologies to extract actionable insights from massive amounts of data is a fascinating prospect, but it has proven notoriously difficult to achieve. With the outbreak of the COVID-19 pandemic, healthcare organizations were forced to confront their many data challenges, from collection to accuracy and analysis. As a result, the industry has increased its efforts to improve data analytics capabilities, with tech firms playing a key role.
Market players are rapidly developing artificial intelligence and data analytics in healthcare in order to contain COVID-19, paving the way for the technologies' future. Predictive analytics tools are assisting healthcare organizations in avoiding negative outcomes, resource shortages, and other COVID-19-related consequences. Researchers and providers have been able to analyze trends, monitor patient populations, and begin to address long-standing issues in the healthcare industry due to the massive amount of data generated by the pandemic. In the midst of a healthcare crisis, the ability to predict future events is critical, and predictive analytics tools can help healthcare organizations do just that.
To better understand which patients are at risk, where resources are most needed, and where the disease is likely to spike next, organizations are increasingly turning to predictive models. The most important use case for predictive analytics during the pandemic is determining which patients are most at risk of contracting the virus - as well as which individuals are most likely to have poor outcomes from COVID-19.
As per the article published in September 2020, a team from Mount Sinai recently developed a predictive analytics model based on three clinical features: age, minimum oxygen saturation, and type of patient encounter. These three characteristics can accurately classify COVID-19 patients as likely to live or die, according to the findings. Cleveland Clinic researchers set out to accomplish a similar goal in June 2020. The researchers created a predictive analytics model to predict a patient's likelihood of testing positive for COVID-19 and the disease's potential outcomes. A team from Johns Hopkins University School of Medicine attempted to predict COVID-19 outcomes using analytics tools. Researchers recently developed a predictive analytics model that can predict the likelihood of a patient's condition deteriorating while in the hospital. The patient's age, BMI, lung health and chronic disease, and vital signs were all considered risk factors.
As the pandemic progresses, predictive analytics will play an increasingly important role in tracking the virus's impact, from patient outcomes to hotspots of disease spread. The global health crisis has only served to emphasize the importance of predictive analytics in healthcare, potentially speeding up the adoption of these tools into standard care in the future.
Over the past decade, electronic health records have been widely adopted in hospitals and clinics worldwide. It helps to improve patient care and improves overall efficiency. Big Data in healthcare is being used to predict epidemics, cure diseases, improve the quality of life and avoid preventable deaths.
With the world’s increasing population and everyone’s wish to live longer, models of treatment delivery are rapidly changing, and many of the decisions behind those changes are being driven by data. The drive helps to understand a patient as much as possible, as early in their life as possible, hopefully picking up warning signs of serious illness at an early stage that treatment is far simpler (and less expensive) than if it had not been spotted until later.
Besides, the use of connected devices that generate huge amounts of data is expected to increase significantly; this raw data can be analyzed to make better enterprise decisions. According to European Telecommunications Network Operators' Association (ETNO), the number of IoT active connections in healthcare is expected to reach 10.34 million by 2025. Such growth further strengthens the demand for data analytics and tools to gain data-driven insights into the industry.
Key Market Trends
Descriptive Analytics is Expected to Hold a Major Market Share in Healthcare Cloud Based Analytics Market
Descriptive analytics is the quantitative collection and transformation of data information, including summaries of hospitalizations, the number of disease cases, and length of stay in the hospital. These are reporting tools that focus on data visualization, standardized reporting, and historic trend analysis. Most of the healthcare organizations are extensive users of descriptive analytics that provide a clear understanding of the past medical history and its visualization through key performance metrics or other data in reports or dashboards. This is carried out by describing the current situation and past performance.
Descriptive analytics helps in categorizing the customers by product preference and life stage. Descriptive studies emphasize features of new diseases or accessing the health status of communities. It basically describes reported data but fails in predicting the results or the future. Epidemiologists and clinicians use descriptive analytics to search for clues to the cause of new diseases.
However, descriptive analytics has limitations, like temporal associations between putative cause and effects that may be unclear. Misinterpretation of data may affect public health. Providers engaging in descriptive analytics have the ability to generate reports that illuminate events that have already occurred, resources that have already been consumed, or patients who have a new diagnosis on their charts. Owing to these growing benefits of technology, the market is expected to grow in the future.
North America is Expected to Hold a significant share in the market and is expected to do the Same in the Forecast Period.
With the rapid spread of COVID-19, many hospitals and health systems were faced with the prospect of unexpected spikes in patient volume, putting a strain on resources and putting additional strain on staff. Organizations have implemented predictive tools that can help allocate resources to better plan for these potential surges. Cleveland Clinic researchers developed a predictive model that can predict patient volume, bed capacity, ventilator availability, and other metrics as of late April 2020. The model provides timely, accurate data to help COVID-19 and other patients get the best care possible.
A critical aspect of the pandemic is predicting the needs of hospital staff. A Cedars-Sinai team created a machine learning tool that can forecast data points related to the COVID-19 pandemic and predict staffing requirements. With an accuracy of 85 percent to 95 percent, the platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for rising COVID-19 patient volumes.
The United States healthcare analytics market holds the largest market share in the North American region, as the healthcare infrastructure in the United States is experiencing a positive trend in the healthcare analytics domain. The substantial growth avenues in the region can be attributed to the presence of several prominent players in the region. Besides, the players are also coming up with new products which are further supporting the market growth. For instance, in July 2021, UnitedHealthcare introduced the use of predictive analytics to help improve well-being, lower costs and drive engagement in clinical-intervention programs by addressing social determinants of health for people in some employer-sponsored benefit plans. Building on similar initiatives for UnitedHealthcare Medicare and Medicaid beneficiaries, the new capability adds the use of predictive analytics to help efficiently identify people in need of support related to social determinants of health. Also, in October 2020, Prognos Health, a leading clinically-focused healthcare analytics firm, launched a new integrated platform to transform healthcare analytics. Pharmaceutical companies, providers, and payers can pinpoint insights at decision-making speeds and deliver a clear picture of opportunities to improve outcomes along the patient journey with prognosFACTOR, which unifies data across the healthcare landscape.
Competitive Landscape
The market is fragmented and competitive and consists of several major players. The major players operating in the market are IBM, Allscripts Healthcare Solutions, Cerner Corporation, Mckesson Corporation, and Koninklijke Philips N.V. Majority of the healthcare cloud-based analytics services are being provided by the global key players. Market leaders with more funds for research and a better distribution system have established their position in the market. Moreover, Asia-pacific is witnessing an emergence of some small players due to the rise of awareness. This has also helped the market grow.
Additional Benefits:
- The market estimate (ME) sheet in Excel format
- 3 months of analyst support
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Table of Contents
1 INTRODUCTION
4 MARKET DYNAMICS
5 MARKET SEGMENTATION (Market Size by Value - USD million)
6 COMPETITIVE LANDSCAPE
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Allscripts Healthcare, LLC
- Cerner corporation
- CitiusTech
- HP
- IBM
- McKesson
- Optum Health
- Oracle
- Verisk Analytics
- UnitedHealth Group
- McKesson Corporation
- Microsoft
- MedeAnalytics, Inc.
- Health Catalyst
Methodology
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