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The CIO Point of ViewHEALTHCAREThe New Agenda for Transformative Leadership: Reimagine Business for Machine Learning

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Global Overview

Machine learning has arrived in the enterprise, and companies are eager to reap the competitive benefits the technology can provide. From automating processes that enable faster business operations to applying algorithms that improve accuracy, CIOs are adapting the technology for a wide variety of uses—and transforming the way we work.

To investigate the rise of machine learning, we worked with Oxford Economics to conduct a survey of 500 CIOs in 11 countries on three continents and across 25 industries, and conducted in-depth interviews with leaders in the field. We found that CIOs are increasing their investment in machine learning but must overcome several barriers to achieve their productivity, revenue, and innovation goals. Unless CIOs turn their attention to updating talent and business process strategies alongside technology, the full value of machine learning cannot be realized.

CIOs in the healthcare industry lead other sectors in preparing their organizations for the adoption of machine learning by optimizing business processes, updating IT structures, and enacting risk-management procedures (e.g., accounting for machine-made errors). These are critical first steps for an industry poised for digital transformation.

However, healthcare CIOs are less likely to actually use machine learning across the organization and are less likely to believe the technology will reduce risk.

As part of our global study, we surveyed 50 CIOs in the healthcare sector to better understand how the state of machine learning differs in this industry. This paper highlights their key responses.

Key Insights

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CIOs from the healthcare industry lead other

sectors in optimizing business processes,

updating IT structures, and enacting risk

management procedures for machine learning.

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Machine learning maturity in healthcare

Q: Which of the following do you see as most important to the success of your organization over the next three years?

Q: Which best describes your organization’s use of machine learning?

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We are using machine learning in parts of the business

Automating routine processes is important to my organization’s success

Healthcare Others

Despite compelling use cases, adoption of new and emerging technologies has been traditionally slower in healthcare compared to other verticals. This is in large part due to funding issues and increased complexity. Since healthcare deals with human lives, safeguards and testing are needed to ensure the appropriateness of machine learning interpretations and recommendations, especially since medical context is complex and the impact on patients could be significant. Based on our survey results, it is clear that healthcare CIOs see the value of machine learning and are actively preparing their IT organizations to adopt the technology, even as their investment lags behind other industries.

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CIOs Are Adopting Machine Learning to Advance Digital TransformationMachine learning and automated decision-making will be critical as organizations take on digital transformation—that is, the strategic use of technology to improve enterprise performance, customer relationships, processes, products, and services. More than half (56%) of healthcare CIOs say machine learning is a strategic focus for them as their role changes to lead organizationwide digitization efforts compared with 53% of CIOs from other fields.

As machine learning becomes more important to CIO strategy, budgets for the technology are increasing. CIOs from healthcare say spending on machine learning will grow considerably over the next three years—34% of healthcare companies are investing in machine learning today, and 66% will be in three years. This matches cross-industry averages of 35% and 64%, respectively.

So far, this group of CIOs has been focused on investing in the foundational technologies for their industry, such as electronic medical records, analytics, and mobile. The need for this catch-up strategy is clear from the relatively low maturity for a range of supporting technologies, including analytics (50% in healthcare say their capabilities are “substantially” or “highly” developed vs. 66% of others), mobile (32% vs. 54%), social (20% vs. 37%), IoT (24% vs. 36%), and process automation software (12% vs. 32%).

of healthcare CIOs say machine learning is a focus for them vs. 53% of others.

56%

Investment in machine learning will increase sharply in three years

Q: To what extent are you investing in machine learning, today and in three years? No investment, minimal investment, some investment, and substantial investment responses are shown here.

Some and substantial investmentNo and minimal investment

2017

2020

34%66%

34% 66%

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CIOs Expect Machine Learning to Drive Business ValueHealthcare CIOs expect investments in machine learning to pay off in multiple ways. They expect significant improvements in the accuracy of decisions, speed of decisions, and employee focus on strategic work—all of which will have an impact on care delivery and patient outcomes. For example, a HealthITAnalytics article reports that researchers at Stanford University found machine learning tools performed better than human pathologists at distinguishing between types of lung cancers and predicting patient prognosis. In addition, machine learning tools applied by researchers at Google were able to more accurately examine metastasized breast cancer tissue than humans, resulting in a significant reduction in false negative diagnoses.

Moreover, a significant number of healthcare CIOs (54% vs. 52% of others) say their organization is advancing beyond the automation of routine tasks to the automation of complex decisions. For instance, machine learning is being used in conjunction with natural language

processing to parse medical records and inform decisions on diagnosis and treatment.

Healthcare CIOs are cognizant of the increased information security risks that their organizations face. In just the last two years, millions of patients’ private data was exposed by hackers. Ponemon Institute found that healthcare organizations experienced almost one cyber attack per month in 2015. Indeed, 92% of healthcare CIOs say automating decision-making will deliver value in security, compared with 96% of CIOs in other fields.

Healthcare CIOs are more restrained in their expectations for machine learning across their organizations. For example, they are less likely than CIOs from other fields to expect the technology to improve recruitment and retention (26% expect positive effects vs. 39% of others), competitiveness (56% vs. 65% of others), or risk and compliance (36% vs. 58% of others). They are also less focused on top-line growth than their peers.

CIOs expect substantial value from machine learning

Q: To what extent do you expect decision automation to deliver value in the following areas over the next three years? “Substantial value” and “transformative value” responses combined are shown here.

Healthcare Others

Accuracy of decisions

Speed of decisions

Employee focus on strategic work

Top-line growth CompetitivenessCustomer service

90%82%

68%64%

58% 56%

70%65%

86% 84%

58% 56%

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20%

Machine Learning Demands New Jobs and ProcessesSuccessfully automating tasks and decisions requires more than just the right technology. It requires talent who know how to program and manage machines as well as updated business processes that reflect a new way of work. Healthcare organizations lead their peers in executing on these changes.

They are more likely than others to say they are making changes to IT structure to accommodate machine learning (68% vs. 53%), that they are making organizational changes to accommodate machine-driven business practices (56% vs. 47%), and that their IT management system is maintained by an automated process (40% vs. 34%). They also are more likely to say they have developed a road map for future process changes (44% vs. 35%).

On the talent front, healthcare organizations still have work to do. The skill sets required in the machine learning enterprise are diverse and involve multiple disciplines from engineering, data science, and math to critical thinking and problem-solving. Although 42% have redefined job descriptions to focus on work with intelligent machines (vs. 38% of others), only 20% have recruited employees with new skill sets (vs. 28% of others).

of healthcare CIOs have developed a road map for future process changes.

44%

56%68%

Healthcare ahead in redesigning organizations

How Healthcare Organizations Perform

Healthcare CIOs must focus on talent

Making organizational changes

Making changes to IT structure to accommodate machine learning

Developed a road map for future process changes

Redefined job descriptions to focus on work with intelligent machines

Addressed the operational impact of mistakes made by machines

Recruited employees with new skill sets

Healthcare Others

44%

42%

38%28%

35%

52%

36%

53%47%

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ConclusionMachine learning promises to remake business models across sectors as algorithms identify and solve problems faster than human employees ever could. For healthcare organizations, the technology has the potential to not only reimagine the delivery of care but also the results felt by patients. When doctors and nurses can work in tandem with intelligent machines, studies are already showing more accurate diagnoses and care plans.

However, healthcare organizations are slow to adopt new technologies. So far, CIOs are focusing on foundational and industry-specific tools. While they continue to make these early investments, CIOs in healthcare will also need to focus on hiring talent with machine learning capabilities in order to advance the adoption of the technology.

By doing so, we could envision a healthcare system that is more accurate and faster, with countless benefits to the quality of human life.

First Mover Advantage We isolated a group of respondents—we call them first movers—who are ahead of their peers in spending on machine learning, automating business processes, making organizational changes to support digital work, and updating business processes and talent strategy (see our Global Point of View for a full breakdown of the criteria). Their plans and actions provide a useful guide for CIOs to advance machine learning in their companies.

All companies are just beginning to adopt this technology, but some are further ahead than others: 10% of global CIOs and 9% of CIOs in the healthcare industry qualify as leaders in our analysis.

First movers demonstrate leadership in a number of areas: They are concentrating on innovation (70% vs. 54% of others), and say automating routine processes (50% vs. 33%) and digitizing business processes (46% vs. 27%) will be important to their organization’s success over the next three years.

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