Competing on the Rate of Learning
New technologies operate at superhuman speed. Social, political, and economic forces move much more slowly. To use learning as a competitive advantage, companies must be able to learn on both timescales.
Related Expertise: Biopharma, Medical Devices and Technology, Health Care Payers, Providers, Systems & Services
What Will It Take to Win the ’20s?
Business leaders no longer think about artificial intelligence in terms of future impact—they’re seeing the impact today. AI is appearing in all corners of business, transforming the way companies operate. Health care is no exception.
Health care players are using AI to address significant inefficiencies and open up powerful new opportunities. These include everything from the delivery of remote health care services to the early diagnosis of disease and the hunt for new life-saving medicines. Today, the technology is incorporated into heart monitors, smart glucose pumps, and other recently FDA-approved diagnostic devices. Biopharma companies are already using AI to improve the efficiency of R&D; one notable example is through identification of better drug targets.
The ongoing rapid development of AI will trigger a major shift in the value pools across health care. This has serious implications not only for the industry’s four major traditional sectors—biopharma, providers, payers, and medtech—but also for consumers and technology companies. Boston Consulting Group has conducted an in-depth analysis of the potential impact of AI on health care, identifying two prospective scenarios for how value will shift among stakeholders. Under one scenario, much of the value unlocked by AI is retained by players in the four health care sectors and technology companies—while the second scenario sees much of the value flowing directly to consumers.
AI is an amalgam of novel methods for gathering data (including machine vision, speech recognition, and natural language processing), new processing techniques (such as machine learning), and innovative interfaces with the real world (including speech generation and 3D navigation). While the term AI is often used to encompass a broad array of technologies, it should not be confused with traditional business intelligence or business analytics, both of which typically rely on structured data—applying classical statistics such as variances, correlations, and regressions to produce insights for business. AI does more. It harnesses diverse and unstructured data sets and employs novel methods such as neural networks to adapt and learn.
AI is taking off in health care today for three reasons. First, in developed markets there is mounting pressure to contain or reduce health care costs and improve outcomes. Second, there has been an explosion in the availability of health care data, including genomics data, electronic medical records, and information from monitoring devices, such as pacemakers and wearables. Third, advances in software and hardware make it possible to harness that data in new, powerful ways.
As AI-driven innovations take off, they will allow providers to diagnose disease earlier with greater accuracy—and ultimately manage it more effectively. Such advances will be critical drivers that help deliver the best patient outcomes at the lowest possible cost—what is known as Competing on Outcomes: Winning Strategies for Value-Based Health Care (VBHC). (See Competing on Outcomes: Winning Strategies for Value-Based Health Care.)
There are major opportunities to increase efficiency in seven areas across the health care value chain. Players in the four traditional health care sectors, as well as technology companies, are already deploying AI tools and approaches in order to seize those opportunities. By 2022 spending on AI-related tools will top $8 billion annually across the following seven areas:
AI will cause shifts in health care value pools—reflected in revenues and profits—by exposing inefficiencies, improving medical decision making, and increasing the quality of care. (See Exhibit 1.) Value will shift not only among the health care sectors but also to players traditionally outside the industry, and to consumers.
Impact on Health Care Players. There are three primary categories of value pool shifts. The first category includes changes created by applications that will reduce costs within a sector and therefore unlock additional value within that sector. These are net positives for the sector. The second are AI shifts yielded by applications within one health care sector that will threaten revenue or profits within other sectors. In such cases, value will flow from one health care sector to the other. The third includes shifts driven by AI applications within one of the four health care sectors that cause value to flow from that sector to either technology companies or consumers.
Two Possible Outcomes. The directional flow of the various shifts in value is clear—but the magnitude is not. It is difficult to predict the extent to which the four traditional health care sectors will retain value instead of passing it on to consumers or the technology industry. Multiple scenarios are possible. Let’s consider two:
The journey to integrate AI into strategies and operations must be a sustained one. But even companies that have yet to invest in AI decisively can make some smart, low-risk moves to either enhance the positive value shifts or minimize the negative impacts.
To make the most of the value shifts in these areas, health care players must ensure they have the right talent and the right data.
The talent challenge has a number of different—but interconnected—layers. Health care players will need to lure data scientists and engineers away from the likes of Alphabet, Apple, and Tesla. At the same time, they will need leaders who understand the AI opportunity, are conversant with the technical issues involved, and can communicate to the wider organization. Companies will also need to figure out where to house and how to organize AI talent so that they build a group that is both cohesive and dynamic—but is also accessible to, and integrated within, the overall organization.
The data issues associated with AI are similarly daunting. AI requires large amounts of data—but information in health care is often irregular or poorly structured, and dispersed among players that have different standards and regulatory restrictions. As a result, while individual players have valuable data sets, they often have difficulty pulling information together from across the entire industry. Payers, for example, have claims databases that can yield powerful insights—but they don’t always have access to other information, such as electronic health records, that would give them a system-level view. The bottom line: companies must either invest in generating the data they need internally or strike partnerships with external players to gain access to it.
Having the right talent and data will be table stakes in a transformed industry. The organizations with an edge in both areas will have enormous advantages. Health care players must act now to develop and implement strategies to prepare themselves for this future.