Presentations on the anesthesiology workforce often include a graph like Figure 1, which shows the estimated number of practicing anesthesiologists by age. It is especially interesting because of the drop in the supply of anesthesiologists ages 46-54 years (as of December 2021), which resulted from the substantial decrease in anesthesiology residents in the 1990s. In Figure 1, I identified the baby boomers, partly because they are approaching retirement, and partly because that is the “generation” to which I belong. But is such a designation meaningful?

Figure 1: Age distribution of anesthesiologists as of December 31, 2021. Estimates by the ASA Center for Anesthesia Workforce Studies based on AMA data and the ASA member database.

Figure 1: Age distribution of anesthesiologists as of December 31, 2021. Estimates by the ASA Center for Anesthesia Workforce Studies based on AMA data and the ASA member database.

Of course, there are generations besides the baby boomers. I’m reminded of this frequently by emails from news services I subscribe to that have generational references in the titles of stories. I became curious about generations research and its use in workforce and health services-related research. A quick search on PubMed shows that generational research has been a topic in the life sciences for more than three decades (Figure 2) (asamonitor.pub/3UD88G8).

Figure 2: Number of articles in health sciences literature with reference to a specific generation in the title, 1986-2022. Based on PubMed search on 11/29/2022. See Table for descriptions of these three Generations.

Figure 2: Number of articles in health sciences literature with reference to a specific generation in the title, 1986-2022. Based on PubMed search on 11/29/2022. See Table for descriptions of these three Generations.

There are currently six generations described in the research literature. Researchers define the generations by age cohort and describe each by its unique characteristics, major influencing events, and primary concerns (Table). The article associated with the Table notes that members of Generation X are self-reliant (Ochsner J 2016;16:101-7). However, according to research at Stanford’s Center for Advanced Study in the Behavioral Sciences, Generation Z are also self-reliant, pragmatic, and highly collaborative (asamonitor.pub/3F93xWf). Members of Generation Z value diversity and finding their own unique identities. What about people near generation border years? How different is someone born in December 1964 (baby boomer) compared to someone born in January 1965 (Generation X)? My curiosity quickly edged toward skepticism.

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“Do all Generation X anesthesiologists ‘work to live’? Are they all self-reliant with the primary concern of a work-life balance? Are there baby boomer anesthesiologists or Generation Y anesthesiologists who also share these characteristics and concerns?”

Do all Generation X anesthesiologists “work to live”? Are they all self-reliant with the primary concern of a work-life balance? Are there baby boomer anesthesiologists or Generation Y anesthesiologists who also share these characteristics and concerns?

However, researchers acknowledge the challenges of generalizing and the nuances of those individuals born on the cusp between generations. Researchers also recognize multiple factors influencing each generation. The three major effects cited in the literature are life cycle, period, and cohort effects (asamonitor.pub/3P6gq8d).

  • Life cycle effects are directly age-related. For example, young people are less likely than older adults to vote and engage in politics. Related to health care, people tend to develop medical conditions as they age and use more health care services.
  • Period effects occur when events and circumstances (such as wars, social movements, economic booms or busts, and scientific or technological breakthroughs) and broader social forces simultaneously impact everyone, regardless of age. For example, the COVID-19 pandemic is a period effect, and period effects typically have lasting impacts on an entire population, that is, across generations.
  • Cohort effects include attitudes, beliefs, and behaviors common to people of a particular generation. Differences between generations can be the byproduct of the unique historical circumstances that members of an age cohort experience, especially during the years when they are in the process of forming opinions. Some cohort effects may result from a period effect an older generation experienced that subsequent generations did not.

Generations vary by race composition, marriage statistics, and political affiliation. There are differences among generations in policy views of topics such as same-sex marriage and the legalization of marijuana. However, there does not seem to be a variance across generations in gun control preferences (asamonitor.pub/3P6gq8d). In addition, there is substantial variation within a generation across many dimensions. Overall, factors associated with generational differences are complex and overlapping. Additional factors that likely contribute to differences across and within generations include place of birth, socioeconomics, race, education, religion, travel experiences, and influence of family and friends. There are relatively more females in younger cohorts of anesthesiologists (e.g., Generation Y), which may confound attempts to apply generation research to anesthesiology workforce economics.

I agree that certain education, communication, and marketing approaches may be more effective for a particular generation, but I question the usefulness of generation research in workforce economics. Although trends in the ages of anesthesiologists are essential to understand the workforce and related economics, observations from generation research seem less relevant. Moreover, I am surprised that stereotypes based on seemingly arbitrarily defined “generations” are widely accepted, given that generalizing about people based on gender, race, sexuality, or nationality is unacceptable.

At least one book author also questions the usefulness of the generalizations in generations research and refers to the “Generation Myth” (The Generation Myth: Why When You’re Born Matters Less Than You Think. 2021). Still, I was intrigued by some of the titles identified in my PubMed search and read several articles. Although I found a few interesting and worthwhile to inform conversations among friends, most articles seemed to fall short of any practical application to the anesthesiology workforce. However, if undertaken carefully, stratification by generations and other age-based analyses can have value beyond generalized observational correlations (The Generation Myth: Why When You’re Born Matters Less Than You Think. 2021).

ASA uses a form of age-based stratification that focuses on career stages. The “Your Career” menu on the ASA website organizes relevant information based on career stages (asahq.org). These cohorts are Students, Residents and Fellows, Early-Career Anesthesiologists, Mid-Career Anesthesiologists, Late-Career Anesthesiologists, and Retired Anesthesiologists. ASA’s approach has strong merit because the needs and wants of anesthesiologists understandably vary by these career stages.

In addition, ASA conducts a biennial member value and satisfaction research survey to understand preferences and perspectives not only by age cohort but by practice setting and area of interest. Regardless of generation, anesthesiologists with a common interest can communicate via the ASA Community (community.asahq.org/home). Finally, ASA provides individualized customer service via chat on the ASA website, email, and telephone.

From a health services research and workforce economics perspective, age will always matter. People’s health status and factors that influence health change as they age. The focus and delivery of health care services differ substantially between pediatric and geriatric patients, regardless of the generation label placed on these age cohorts. In workforce economics, age affects individual physician preferences around job choice and the relative importance of compensation.

I prefer using an economic framework to study anesthesia workforce challenges, especially during perceived shortages. The economic principles that are critical building blocks to a rational and evidence-based framework include individual utility curves, relative pricing, the effect of substitutes and complements in the labor markets, the role of compensating differentials, and diminishing marginal returns. In addition, I think economic models have superior predictive ability compared to generational profiling.

For example, statements produced by the workgroups from ASA’s Anesthesia Workforce Summit held in June 2022 include the recommendation that practice leadership be flexible and adaptable to physician work-life blend preferences. Although there are likely differences in work-life blend preferences between generations, the recommendation recognizes variability in work preferences regardless of generation. In addition, the workgroups acknowledge that compensating differentials (e.g., higher compensation, additional paid time off) are appropriate to address longer or less desirable hours and work situations.

There are no doubt similarities among anesthesiologists within each generation, but there seem to be limited actionable implications for analyzing workforce economics, health care quality, or patient safety. When it comes to meeting the needs of a medical specialty, ASA gets it right by focusing on career stages, identifying perceptions and preferences through its biennial member surveys, and providing excellent individualized customer service.

I see value in using generations research to facilitate the marketing of consumer goods, the use of technology, and the tailoring of education services. However, I’m weary of generational profiling and a bit skeptical of its use in understanding health care workforce economics. Of course, my skepticism probably is because I’m a left-handed Virgo born in the Year of the Goat.