Healthcare Artificial Intelligence Market Insight by 2023: Top Players Like Stryker, General Vision …

Healthcare Artificial Intelligence market top players including are IBM Watson Health, DEEP GENOMICS, Koninklijke Philips N.V., Stryker, CloudMedx …

Worldwide Healthcare Artificial Intelligence 2019 Research Report presents a professional and complete analysis of Global Healthcare Artificial Intelligence Market on the current situation.

This press release was orginally distributed by SBWire

New York, NY — (SBWIRE) — 01/23/2019 — Study papers on Healthcare Artificial Intelligence market and regional forecast. Healthcare Artificial Intelligence market top players including are IBM Watson Health, DEEP GENOMICS, Koninklijke Philips N.V., Stryker, CloudMedx Inc., General Vision, Intel Corporation, Google, General Electric, Next IT Corp., Microsoft Corporation, NVIDIA Corporation.

The recent report, Healthcare Artificial Intelligence market fundamentally discovers insights that enable stakeholders, business owners and field marketing executives to make effective investment decisions driven by facts – rather than guesswork. The study aims at listening, analyzing and delivering actionable data on the competitive landscape to meet the unique requirements of the companies and individuals operating in the Healthcare Artificial Intelligence market for the forecast period, 2019 to 2023. To enable firms to understand the Healthcare Artificial Intelligence industry in various ways the report thoroughly assesses the share, size and growth rate of the business worldwide.

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The study explores what the future Healthcare Artificial Intelligence market will look like. Most importantly, the research familiarizes product owners with whom the immediate competitors are and what buyers expect and what are the effective business strategies adopted by prominent leaders. To help both established companies and new entrants not only see the disruption but also see opportunities. In-depth exploration of how the industry behaves, including assessment of government bodies, financial organization and other regulatory bodies. Beginning with a macroeconomic outlook, the study drills deep into the sub-categories of the industry and evaluation of the trends influencing the business.

Global Healthcare Artificial Intelligence Market Analysis by Application

1 Global Healthcare Artificial Intelligence Consumption and Market Share by Application (2012-2018)

2 Global Healthcare Artificial Intelligence Consumption Growth Rate by Application (2012-2018)

3 Market Drivers and Opportunities

3.1 Potential Applications

3.2 Emerging Markets/Countries

The extensive documentation of the Healthcare Artificial Intelligence industry gives access to all the factors expected to influence the growth prospect of the business worldwide. Nobel effort to capture the factors that impede the growth of the market is clearly visible in the report. These factors result in an effective and reliable branding and promotion and marketing plan. In addition, comprehensive coverage of recent advancements, product nearing development stage, project pipeline, and major industrial players offer all the confidence a business owner needs to design a business strategy that will drive company’s success.

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Understanding what the audience is looking for in a report the researchers behind this attunes deliverables according to their needs such as product price, demand and supply status, end-use, profit and others. By operating in close alignment with the major vendors, the researchers have customized the literature – based on universal perspective as well as comprehensive knowledge of the local business owners. The document further aims at addressing the different challenges and opportunities of carrying out business operations in North America and beyond.

The Research Provides Answers to the Following Key Questions:

– What is the size of occupied by the prominent leaders for the forecast period, 2019 to 2023? What will be the share and the growth rate of the Healthcare Artificial Intelligence Market during the forecast period?

– Which companies are dominating the competitive landscape across different region and what strategies have they applied to gain a competitive edge?

– What are the major factors responsible for the growth of the Healthcare Artificial Intelligence market across the different regions?

– What are the challenges faced by the companies operating in the Healthcare Artificial Intelligence Market?

– What are the future prospects for the Healthcare Artificial Intelligence Market industry in the coming years?

– Which trends are likely to contribute to the development rate of the Healthcare Artificial Intelligence industry during the forecast period, 2019 to 2023?

– What are the future prospects of the Healthcare Artificial Intelligence industry for the forecast period, 2019 to 2023?

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IT managers look to deploy BI, analytics in the cloud

IT managers are looking to position BI and analytics in the cloud. … Big data platform/enterprise data warehouse/data lake (26.5%) rounded out the top …

IT managers are looking to position BI and analytics in the cloud.

That’s according to TechTarget’s 2019 IT Priorities Survey, which asked a total of 624 IT professionals from a wide range of industries in North America about what’s on their to-do lists.

The survey posed the following question to respondents: Which of the following applications are you most likely to deploy in the cloud this year? Among the 231 IT professionals who responded to this question, the top response was BI/analytics (27%), followed by customer relationship management (23%) and big data platform/enterprise data warehouse/data lake (21%). In last year’s survey, respondents said they were most likely to deploy CRM (34%), ERP (29%) and business process management (27%) in the cloud.

Respondents who plan on deploying data warehouses, data lakes, BI and analytics in the cloud this year are in alignment with a growing enterprise trend, according to experts.

Jen Underwood, senior director at machine learning software vendor DataRobot, said the results are “not at all surprising.”

“Analytics in the cloud is usually ahead of on-premises offerings,” Underwood said. “With rapid weekly updates, on-demand scale, speed and ease of simply getting things done, cloud is a no-brainer for many organizations outside heavily regulated industries.”

Application deployments planned for 2019

Isaac Sacolick, president of StarCIO and author of Driving Digital, said, to drive efficiencies, improve customer experience and target optimal markets, organizations are going “from centralized BI functions to more distributed analytics teams supported by citizen data scientists using self-service BI tools.” Deploying BI and analytics in the cloud is a sensible next step in that transition.

With rapid weekly updates, on-demand scale, speed and ease of simply getting things done, cloud is a no-brainer for many organizations outside heavily regulated industries.
Jen UnderwoodSenior director, DataRobot

“Cloud offerings enable organizations to quickly and more easily ramp up BI tool usage, provide access to more data sets and scale usage of produced analytics with less effort by IT to enable and support infrastructure,” Sacolick said. “IT is then better poised to partner with the business on data governance, integration and modeling initiatives that fuel ongoing analytics needs.”

Underwood and Sacolick aren’t alone in their thinking. Feyzi Bagirov, data science advisor at B2B data insight vendor, said he also is seeing more organizations deploying BI and analytics in the cloud, but that the trend is still in the early stages. He cited 2018 Gartner research that found on-premises deployments still dominate globally, ranging from 43% to 51% of deployments.

Data governance, predictive analytics are priorities

The 2019 IT Priorities Survey also asked respondents what information management initiatives their companies will deploy in 2019. Among the 215 IT professionals who responded to this question, the top response was data governance (28.8%), followed closely by predictive analytics (27.9%) and data integration (27.9%). Big data platform/enterprise data warehouse/data lake (26.5%) rounded out the top four.

Bagirov said he thinks these results more or less align with enterprise trends. He said that priorities may vary by industry — companies in the financial sector might be more inclined to push data lake initiatives, for example.

Data governance and integration will top IT professionals’ objectives this year, Bagirov said. “Those are the steps that are essential before predictive analytics can be scaled up,” he said.

Management initiatives planned for 2019

As for Underwood, she said the European Union’s recent rollout of GDPR likely influenced data governance’s top placement in the survey. Governance probably won’t be as prominent next year, though, she said.

“In my machine learning and artificial intelligence work … I am seeing early adopters achieve astounding results that I have never seen happen throughout my entire 20-plus-year analytics career,” Underwood said. “The artificial intelligence gap is already being exploited as a game-changing competency for competitive advantage in the algorithm economy. As a result, I forecast predictive analytics to be No. 1 on your ranking next year. Artificial intelligence and automation is changing analytics as we know it today.”

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AI, cross-industry collaboration will continue to reshape healthcare in 2019, Optum says

At HIMSS19, cross-industry collaboration and adoption of emerging technologies are two health IT trends that will be the focus of Optum, a health IT …

At HIMSS19, cross-industry collaboration and adoption of emerging technologies are two health IT trends that will be the focus of Optum, a health IT vendor whose products include analytics, population health and pharmacy care.

Health systems and physician practices have a long history of information-sharing to support population health goals and improve the patient experience, said Mark Morsh, vice president of technology at Optum, and that trend will only accelerate in 2019.

Practical results of emerging tech

“In the past year, we have seen a concerted effort to convene stakeholders on emerging technology capabilities like blockchain and AI/advanced analytics, which can improve the effectiveness and efficiency of care,” said Morsh.

“This year, our presence at HIMSS shows how emerging technology can have practical results, taking a closer look at personalized medicine, AI and machine learning, and IoT, and how these advancements can improve financial performance and population health, enable risk-based reimbursement programs, and modernize the military and veterans’ health systems,” he added.

Those might appear to be lofty goals, but technology has improved how stakeholders in healthcare safeguard, interpret and share a trove of healthcare data. The industry often focuses on the power of technology, he said, but it’s really about clinicians’ ability to apply it in ways that allow them to practice at the top of their license.

A standout 2019 health IT trend for Morsh is the fast pace at which healthcare organizations are adopting emerging technologies, such as artificial intelligence and advanced analytics.

“We recently surveyed 500 healthcare executives, and three out of four indicated they’re in the process of, or are going to, implement an AI strategy,” he said. “More than 91 percent of respondents also expressed confidence that their organization will see a full return on investment in about five years. We have not seen that kind of momentum since the wave of electronic health record adoption earlier this decade.”

The proliferation of data, including social determinants paired with more traditional claims and clinical data, and the ability to mine that information with new tools, is yielding new insights about cost, quality and access, and opportunities to improve, he added.

“What I’m most excited about is the way this can move us closer to a denial-free future where care is personalized and where we can redeploy resources to improve the experience of patients and families,” he said.

Let technology do it

Morsh believes people in general have reached a point in time where humans are more comfortable allowing technology to augment their day-to-day experience and enhance their decision making.

“Although there are some generational differences, think of how quickly healthcare organizations have moved to adopt devices like tablets and smartphones,” he noted. “The reality now is that technology is central to the care delivery experience for providers and patients and the proliferation of devices, data and connectivity is changing how individuals work and receive care.”

“To be successful in a business setting, investments in advanced analytics and AI must have a defined objective and align with your overall technology strategy.”

Mark Morsh, Optum

On a related front, healthcare faces a talent shortage, especially when it comes to data science, he said.

“It’s difficult for health systems and physician practices that are worried about margins and changing reimbursement to compete for talent in an increasingly competitive and expensive global marketplace,” Morsh explained. “That’s one reason we are seeing a renewed emphasis on industry collaboratives and vendor partnerships to solve large-scale technology strategy and delivery needs.”

It’s also a reason that organizations are looking for practical applications of data and analytics – to make them more efficient and increase overall performance, he said.

Advice for HIMSS19 attendees

Asked what he would advise HIMSS19 attendees, Morsh stuck with his big theme: collaboration and AI.

“Collaborate for a shared vision: Healthcare is a very specialized industry and evidence-based decision making is at the core of our DNA,” he said. “For that reason, it’s really important to build the right team of multidisciplinary professionals. Sometimes you’ll be able to find the right skill set within your organization, but more often than not, success requires bringing in individuals from the outside, who can share a fresh perspective and keep you current with market movements.”

That said, Morsh recommends that healthcare organizations make sure the organizations they partner with have enough grounding in healthcare to understand their business models and operating environment so they don’t end up with technology investments that have little practical value.

“And focus on AI with ROI,” he added. “To be successful in a business setting, investments in advanced analytics and AI must have a defined objective and align with your overall technology strategy. One of the most interesting developments in healthcare is that more applied uses of artificial intelligence, like natural language processing for revenue cycle management or deep learning models that support disease prediction.”

These use-cases must be backed by a business case and defined problem where technology can augment the human – patient, provider or administrator – experience, he said.

Twitter: @SiwickiHealthIT

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HIMSS19 Preview

An inside look at the innovation, education, technology, networking and key events at the HIMSS19 global conference in Orlando.

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Elected Leaders Need Operations Research and Analytics to Deliver Better Results from …

What could be more important for our elected leaders than using the best data and insights to make a positive difference for the American people and …

With midterm elections behind us and the 116th United States Congress ahead of us, Washington has a unique opportunity to advance operations research (O.R.) and analytics as a priority to enhance how the federal government implements public policies, makes critical decisions, and conducts its day-to-day operations. What could be more important for our elected leaders than using the best data and insights to make a positive difference for the American people and to strengthen our position vis a vis our nation’s allies and enemies?

In an era where we have vast amounts of data readily available, the challenge for government is not to access that information, but rather to understand what the data is revealing and how best to act on it. Misunderstanding or misapplying data has serious repercussions. It is imperative that government decision-makers at all levels have the advanced scientific tools necessary to make the right decisions using the right data.

One of the best proven ways to do this is through the use of O.R. and analytics. O.R. and analytics are the application of advanced mathematical tools that enable organizations to turn complex challenges into substantial opportunities. These powerful tools do not merely evaluate existing solutions to problems. More usefully, they relentlessly seek solutions that provide the best possible outcome, and thereby deliver prescriptive value to decision-makers. They do so by structuring data into solutions and insights for making better decisions which offer improved results.

In summary, O.R. and analytics offer policymakers proven scientific and mathematical processes that save lives, save money and solve problems. Some recent examples include:

  • Lieutenant Colonel Christopher E. Marks of the U.S. Army and colleagues, Tauhid Zaman of the Massachusetts Institute of Technology and Jytte Klausen of Brandeis University, found a way to identify extremists—such as those associated with the terrorist group, ISIS—by monitoring their social media accounts and identifying them even before they post threatening content;
  • The Centers for Disease Control and Prevention eradicated the last pockets of the Wild Polio virus around the world;
  • The Federal Communications Commission completed the world’s first two-sided auction of valuable low-band electromagnetic spectrum, contributing more than $7 billion to reduce the federal deficit; and
  • The Federal Aviation Administration deployed the Airspace Flow Program to improve air traffic management and reduce flight delays, saving hundreds of millions of dollars.
  • The Transportation Security Administration (TSA) created the PreCheck passenger screening program—a risk-based aviation security policy that affects more than one million passengers in the U.S. every day—enabling low risk passengers to utilize expedited airport security screening, saving the federal government one-third of a billion dollars every year.

The power of O.R. and analytics also extends to state and local governments, including:

  • The City of Philadelphia redesigned districts for its council members based upon the 2010 census to increase public engagement and minimizing gerrymandering;
  • The Pennsylvania Department of Corrections transformed the complex process of assigning inmates to one of the Pennsylvania Department of Correction’s 25 facilities, reducing a week of seven employees’ time to less than 10 minutes; and
  • The New York City Police Department created the Domain Awareness System, a robust network of sensors, databases, devices, software and infrastructure that informs a variety of tactical and strategic decisions which officers make every day, saving at least $50 million per year.

Despite these outstanding achievements, more can and must be done to expand the use of O.R. and analytics at all levels of government. As a university professor and an operations research professional who has spent his entire career working to improve and expand this field, I am certain that expanding the use of O.R. and analytics would make significant contributions to solving our nation’s problems.

There is broad, bipartisan agreement that various federal government programs and missions continue to operate under antiquated methods, accrue excessive costs and perform inefficiently. I propose O.R. and analytics as an original, powerful and proven approach to fix these problems. The correct application of O.R. and analytic principles to compelling public policy issues will provide robust insights that inform sound, reasonable and bipartisan policy making and program implementation.

Applying these practices to a wide swath of policy areas—such as predicting future outbreaks and pandemics, influencing the design and construction of tomorrow’s communities, building new, cutting-edge transportation and power generation systems, and preventing gun violence through advanced social media monitoring and interventions—will fundamentally improve the government’s fulfillment of its public service mission.

For seven decades, O.R. and analytics have delivered proven and profound value in the private sector with documented savings amounting to many billions of dollars. Now, it is time for O.R. and analytics to become the driver of a higher level of efficiency in Washington, DC.

About the Author

Nicholas G. Hall was the 2018 president of INFORMS. With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research and analytics professionals. He is also the Fisher College of Business Distinguished Professor at The Ohio State University. He holds a Ph.D. (Management Science, University of California, Berkeley, 1986), and B.A., M.A. (Economics, University of Cambridge). His research and teaching interests include project management, scheduling and pricing. He has published 82 articles in Operations Research, Management Science, Mathematics of Operations Research, Mathematical Programming, Games and Economic Behavior, Interfaces and other journals. He has served as Associate Editor of Operations Research (1991–) and Management Science (1993–2008). His 335 presentations include 11 keynote addresses, 8 INFORMS tutorials and 98 invited talks in 23 countries.

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Snowflake, DataRobot Partner To Bring AI to Data Warehouse-as-a-Service

Snowflake Computing is partnering with DataRobot and its advanced enterprise automated machine learning platform. The goal is to bring AI to huge …

Snowflake Computing is partnering with DataRobot and its advanced enterprise automated machine learning platform. The goal is to bring AI to huge caches of enterprise data held in Snowflake cloud-based data warehouse installations

by Craig Gehrig

Tags: AI/ML,analytics,cloud,DataRobot,data warehouse,DWaaS,Snowflake,

Snowflake Computing is partnering with DataRobot and its enterprise automated machine learning platform to bring AI to huge caches of data held in Snowflake cloud-based data warehouse installations.

The deep product-level integration brings together Snowflake’s scalable and manageable cloud-based data warehouse as a service functions with DataRobot’s AI and analytics capabilities.

As a result, Snowflake’s data warehouse services can amass a vast amount of an organization’s data and then make it simple for DataRobot’s AI/ML capabilities to access it – all without disrupting the performance or operations of the data. Users are free to obtain deeper data-driven insights for enterprise level users, according to Snowflake vice president of alliances, Walter Aldana.

“We’re committed to providing our customers with the tools they need to empower data-driven organizations — an imperative for every organization today. The partnership with DataRobot extends our ability to provide customers with access to automated machine learning technology that will uncover business-driving predictive insights,” Aldana added in a statement.

Seann Gardiner, DataRobot’s senior vice president of business development added, “Businesses today know they need to embrace AI to stay ahead. Our partnership with Snowflake makes it easier than ever for users of all skill levels to access data and build, tweak, and deploy machine learning models to make business decisions.”

Snowflake’s cloud-based data-warehouse-as-a-service (DWaaS) architecture offers a single, logically integrated solution to supply full relational database support for both structured data (e.g. CSV files and tables) and semi-structured data (e.g. JSON, Avro, Parquet, etc.).

Snowflake also offers discrete metadata processing. Notably, Snowflake’s architecture separates compute and storage services, allowing them to run independently of one another. This means Snowflake can scale near-linearly — as your compute needs and resources scale out. As a result, Snowflake enables data warehouse managers to support enterprise-wide data warehouse requirements with virtually unlimited concurrency.

For developers, Snowflake also a powerful query processing back-end platform to help them create modern data-driven applications.

Data is also kept secure. The Snowflake services layer is built for security. It authenticates user sessions, enforces security functions, and performs query compilation. Queries are compiled and metadata is used to decide the micro-partition columns that need to be scanned. It also provides all security and encryption key management.

Snowflake is also compatible with popular ETL and BI tools.

All these features set the stage for DataRobot’s enterprise automated machine learning platform to safely, securely and at high-performance access vast amounts of Snowflake-resident data – and deliver insights in compelling ways. In specific, the DataRobot platform delivers top levels of automation and simplicity for machine learning initiatives.

It allows companies to create and deploy powerful machine learning models, without the time and expense of a traditional data science process. Each data model is unique to your dataset and prediction target. They automatically search through millions of combinations of algorithms to determine the best learning model for your data.

Internal to the platform, DataRobot sports a massively parallel modeling engine that can scale to hundreds or even thousands of powerful servers to explore, build and tune machine learning models.

Developers can use DataRobot to easily build and analyze machine learning models, and embed it using a variety of options. Just a few lines of code executes the power of DataRobot to test hundreds of solutions that can combine data preparation with a range of open source algorithms from R, Python, Spark, H20, and many more.

DataRobot’s APIs for Python and R show complete transparency into the model building process, which allows developers to iterate their models, and even compare solutions for speed and accuracy. Another benefit in DataRobot is that any model is production-ready. With the APIs, users can operationalize machine learning models for real-time predictions, batch deployments, or scoring on Hadoop.

Beyond its AI/ML capabilities, DataRobot also sports other important features for data-driven insights. Among them:

  • High-availability
  • Distributed and self-healing architecture
  • Integrates with existing enterprise security
  • Hadoop cluster compatibility
  • Multiple database certifications

Elsewhere, Snowflake also is partnering with global solutions provider Saggezza to reduce costs for data-related projects and expanding ways in which the data can be interpreted. With Snowflake, Saggezza can aggregate all sources of client data into a single source for easy viewability, providing a 360-degree view of data and making it easier to attain actionable insights from the data, according to execs from both firms.


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