If 2020 has taught us anything, it's that technology can change the way health care operates—often times for the better. Artificial intelligence (AI) hasn't quite hit the mainstream in health care, but it's commonly talked about technology with a lot of potential for the future.
For three years, Optum has surveyed approximately 500 health care executives to understand their attitudes, progress, and goals when it comes to AI. The survey's respondents span across sectors, including life sciences, employers, health plans, and hospitals and health systems. We reviewed the 2020 survey results and identified three of the most interesting findings that are likely to shape the future trajectory of AI in health care.
(Editor's note: Advisory Board is a subsidiary of Optum. All Advisory Board research, expert perspectives, and recommendations remain independent. Optum did not participate in selecting the topic for this post or in the analysis presented in this post.)
1. AI is more likely to create jobs than destroy them
Despite doomsday fears when it comes to AI, we strongly believe that AI is not going to replace clinicians or eliminate jobs. Survey respondents not only agreed with this sentiment, but they took it a step further: 56% of the leaders surveyed believe that AI will actually create new jobs. There were two interesting additional details embedded within this data point:
- The belief that AI will create jobs was especially true among senior executives who weren't C-level—61% of these senior executives said AI will create jobs, compared with 45% of their C-suite peers. This division suggests that those who are closer to the day-to-day work of staff are particularly confident that AI technologies will bring new development or employment opportunities for their teams.
- This optimism around job creation was generally higher at organizations further along in their AI journeys—76% of leaders at organizations in late-stages of AI deployment said AI would create jobs, compared with 55% of those in early- or middle-stage deployment. This shows that those who are closer to realizing and using AI are fairly confident that in practice, the technology will create rather than reduce work opportunities.
The conversation around AI is shifting from fear to opportunity; leaders are thinking less about job elimination and more about job creation. The obvious question that stems from this shift in thought is what exactly are these new jobs? This is still an open question the industry needs to answer, but the survey did specify what factors may influence hiring decisions in a world where AI is more commonplace.
2. Organizations will need to upskill their workforce, but technical talent isn't top-of-mind
For years, hospitals and health systems interested in pursuing AI have struggled to hire data scientists who can build and train AI models. This is because of the relative cost of specialized talent and increasing competition with other industries and health IT startups and vendors.
But the latest survey results indicate an important shift in thinking: 92% of leaders said they are prioritizing AI experience and knowledge when hiring. When it comes to the kind of employees they are hiring, 51% said they want people who can develop AI and 49% are looking for people who can apply the result. This is about a 50-50 split, but it shows that many organizations are now outsourcing the technical components of AI.
Technical expertise is no longer the most important skill health care organizations need to hire for. Instead, organizations are hiring for talent that can effectively interpret and use AI models in their day-to-day work. This talent is much easier to find for health systems and can be attained in multiple ways. Organizations can hire new employees, but they can also train and develop their existing clinical and operational staff.
The reality is that AI is never going to be the bread and butter for most health systems—but it doesn't have to be. Health care leaders interested in AI should think practically about their approach and what talent gaps they have. In many cases, hiring will be focused on applying technology and not building it. Having staff that is fluent enough in how AI works may be the most valuable skillset in the coming years.
3. All sectors have unique goals when it comes to AI, but hospitals have goals in common with each sector
Across all sectors, the top three AI applications executives expressed interest in were monitoring data from Internet of Things (IoT) devices (40%), accelerating research for new therapeutic or clinical discoveries (37%), and assigning codes for accurate diagnoses, facilities, and procedures (37%).
However, when it comes to the possible use cases for AI, each sector had unique desires for how they intend to use AI. This chart breaks down how each sector ranked their top three priorities.
At first glance, these rankings appear to be significantly different across each sector. But hospitals and health systems actually have a lot of commonalities with the other three groups. Each of the top three priorities for hospitals is also a first priority for one of the other sectors.
This alignment suggests that there is a lot of opportunity for collaboration in this space. Collaboration would also make for better, more accurate AI models given the mass amounts of data needed to train and maintain AI models. Health care leaders should view AI as a tool to facilitate cross-industry collaboration rather than competition.