In today's health care environment, anesthesiology residency training programs are facing multiple challenges. These programs have been successful against outside market forces, such as the repeal of the teaching rule1; however, more challenges are emerging. With the current Accreditation Council for Graduate Medical Education (ACGME) mandate of maximum 80 duty hours per week because of concerns of fatigue2 and the added implementation of more stringent working hours limitations,3 anesthesiology residency training programs may potentially have to modify the way they train residents. With these new guidelines, all clinical and didactic work activities need to occur within the 80 duty hours per week, including those activities that are not part of their residency. Historically, “moonlighting,” or working >1 job, was common among residents as a way to earn more money, separately from their resident paycheck.4 These extra activities can be either 1 internal moonlighting (originating from the residents' program), or 2 external moonlighting (originating from outside work opportunities). According to the new work duty rules, both internal and external moonlighting need to be included within the calculation of the 80-hour week limit for residents.5 Not only are outside influences changing the way anesthesiology residency training programs train their physicians, but the physicians in training bring their own personal challenges.
Recently, the amount of education debt incurred by medical school graduates in the United States has grown considerably; students are using larger loans to finance their medical school education at both public and private universities. Although the percentage of medical school students graduating with debt nationwide has not changed dramatically since 1984, the total amount of debt per graduate has increased, even outpacing inflation, to a mean of $158,000.6
There has been limited information on how medical school debt loads of anesthesiology physicians impact their decisions concerning moonlighting and future career choices. Thus, we surveyed a sample of the current anesthesiology interns, residents, and fellows to determine their outlook toward moonlighting activities, future career plans, and choice of employer based on their amount of medical school debt. We hypothesized that a considerable amount of residents would have significant debt and this debt load would influence their choices both during and after training.
Our aim was to determine whether the amount of medical school debt would influence 1 their choice to moonlight during residency and/or fellowship, 2 their choice of practice environment, and 3 whether a debt repayment program would influence whether a resident would join a particular group practice.
We reviewed published surveys of residency finances to evaluate their structure and to determine the types of questions that would be of interest within our study.7–10 We then developed a web-based survey instrument, evaluated its content validity by using a small number of residents, fellows, and faculty (n = 10) to complete sample surveys, and refined the tool to ensure that the tool worked well, questions were understandable, and the time to take the survey was reasonable (5–10 minutes). There were 53 multiple-choice questions and 1 optional free text box on the final survey (Appendix, see Supplemental Digital Content 1, http://links.lww.com/AA/A368).
With IRB waiver of written informed consent, we contacted the 132 allopathic and 12 osteopathic anesthesiology residency programs within the United States (approximately 6000 residents) via e-mail through the contact information available on the World Wide Web through the American Medical Association,11 Society of Academic Anesthesiology Associations,12 and the American Osteopathic Association.13 We requested the e-mail contact information from the programs so that we could contact residents directly. When programs were reluctant to share the e-mail addresses with us, we then asked whether they would be willing to forward an e-mail with a generic web address to their residents.
With the direct e-mail group, we used the survey tool to contact all potential participants in this group directly from the web site. When the survey was developed, we set up the survey tool so that the data collected would not be connected to an Internet Protocol address or an e-mail address so that data could be collected while maintaining anonymity of the study participants. By using this method of data collection, the investigators could only tell who had and had not begun the survey, because the data collected was separated from the e-mail address. Two follow-up e-mails were also sent automatically with the survey tool to only those potential subjects who did not complete a survey.
For the group of potential survey participants who received a generic web address to complete the survey, the main contact person at their training program was sent the first invitation to participate in the research survey and 2 subsequent follow-up e-mails were sent via the contact person as well. A secure web server was also used to protect information during the survey process for both groups.
In our survey, the following categories for the amount of medical school debt were used: 1 no debt, 2 $1 to $29,9999, 3 $30,000 to $59,999, 4 $60,000 to $89,9999, 5 $90,000 to $119,999, 6 $120,000 to $149,999, and 7 ≥$150,000. We assessed the associations between the amount of debt and the following 3 main statements/questions: (1a) I plan to participate in moonlighting that is not mandatory in the future during my residency or fellowship, and (1b) the opportunity to moonlight at a particular residency influenced/would influence my decision to go there; 2 How likely are you to pursue an anesthesiology career in the following: fellowship, academic faculty, academic/research faculty, private practice faculty? (Each career path was treated as its own question and analyzed as such); and 3 How likely would an education debt repayment program influence your choice in a particular employer? A 5-point Likert scale (1–5) of strongly disagree to strongly agree or not likely at all to very likely was used for these statements/questions.
We assessed the univariable associations between amount of medical school debt and responses to each of the above mentioned statements/questions (a total of 7) using the Mantel-Haenszel χ2 test (which tests for a linear association between 2 ordinal variables); Spearman rank-order correlations and confidence intervals (CIs) were also reported. We also assessed the internal consistency of the above 7 questions using the standardized Cronbach α coefficient. As our primary analysis, we assessed each relationship of interest using a multivariable proportional odds model, adjusting for all available baseline potentially confounding factors (listed in Table 1, including age, gender, marital status, whether or not their significant other works, number of dependents, type of medical school, and current anesthesiology level of training). Second, we compared participants with >$150,000 medical school debt with participants with no debt on the same questions of interest, each using a multivariable proportional odds model with the same covariable adjustment. Bonferroni correction was used to adjust for multiple comparisons. Thus, P < 0.007 was considered statistically significant (i.e., 0.05/7), and the corresponding 99.3% CIs were reported.
With 537 completed questionnaires, we had >90% power to detect a correlation of ≥0.2 at an overall significance level of 0.05 (assuming Bonferroni correction to adjust for 7 analyses). Also, a sample size of 537 gave a correlation coefficient 99.3% CI width of 0.06 to 0.12 for correlations ranging from 0.1 to 0.4, respectively. SAS software version 9.3 (SAS Institute, Cary, NC) was used for all statistical analyses.
Completed Surveys, Debt Groupings, and Question Consistency
Fourteen programs supplied direct e-mail addresses for their residents (n = 510) and 35 programs agreed to forward a generic web address so that their residents could participate in the survey (n = 1876). Taken together, we had access to 2386 residents. Of these, 561 residents started the survey and 537 completed it (response rate of 22.5%). Residents for whom we had direct access had a response rate of 34%, whereas the rate was only 20% for those we accessed through a generic web address. A schematic of the selection process can be seen in Figure 1.
Table 1 shows the summary statistics of demographics and baseline characteristics for the 537 participants who completed the survey. Among these participants, 54 (10%) did not have debt for their medical school and 231 (43%) had debt of ≥$150,000 (Table 2).
We assessed the internal consistency of the above 7 questions using the standardized Cronbach α coefficient; the items were weakly consistent with each other, indicated by a standardized Cronbach α coefficient of 0.31.
Medical School Debt Versus Desire to Moonlight During Residency or Fellowship
Participants with a larger amount of debt were more likely to have the desire to participate in moonlighting activities during residency or fellowship both univariably (P = 0.002) and after adjusting for the baseline covariables (multivariable P = 0.006) (Table 3). The estimated multivariable odds ratio (OR) of selecting a higher ordered value (i.e., being more likely to agree) for the following statement “I plan to participate in moonlighting that is not mandatory in the future during my residency or fellowship” for those respondents with a 1-category-larger amount of medical school debt (i.e., $30,000) were associated with 7% (99.3% CI: 0%, 13%) increased odds of having the desire to moonlight during residency/fellowship after the covariable adjustment.
Interestingly, the amount of medical school debt was not associated with a similar statement of “The opportunity to moonlight at a particular residency influenced/would influence my decision to go there,” after adjusting for baseline covariables (OR [99.3% CI]: 1.04 [0.97, 1.10] for 1-category increase in the amount of debt, P = 0.11; Table 3). As expected, the 2 items related to moonlighting activities were moderately correlated with a coefficient of 0.65 when using the standardized Cronbach α coefficient.
Medical School Debt Versus Career Choice
Survey participants with a larger amount of medical school debt were less likely to say they would choose an academic faculty career (univariable P < 0.001 and multivariable P = 0.002; Table 3). The estimated OR (99.3% CI) of selecting a higher ordered value (i.e., more likely to say they would choose academic) was 0.93 (0.87, 0.99) for 1-category increase in the amount of debt. However, choice of other anesthesiology career paths was not significantly associated with the amount of medical school debt (Table 3). Using the standardized Cronbach α coefficient, the choice of academic faculty and academic/research faculty was highly correlated with a coefficient of 0.80; the correlations between choice of fellowship and academic (0.61) and academic/research faculty (0.52) were moderate; also, the correlations between choice of private practice faculty was negatively correlated with choice of other career paths.
Medical School Debt Versus a Group that Offers a Debt Repayment Program
Those with a larger amount of medical school debt were more likely to be interested in an anesthesiology group with an education debt repayment program (P < 0.001 both with and without covariable adjustment; Table 3). The covariable-adjusted OR of selecting a higher ordered value (i.e., being more likely to be interested in) was estimated as 1.30 (1.22, 1.39) for a 1-category increase in debt amount. Furthermore, within the subset of participants with either ≥$150,000 or no debt, we found that those with ≥$150,000 debt were more likely (OR: 4.6 [2.8, 7.5]) to say they would be interested in an anesthesiology group with an education debt repayment program as compared with those with no debt. However, no difference was found on any other questions of interest (Table 4). All of the remaining survey question responses are summarized in the Appendix (see Supplemental Digital Content 1, http://links.lww.com/AA/A368).
Education debt loads have been increasing over the years with the increase in medical school tuition,14,15 outpacing inflation,6 and this is clearly documented by our results. It is quite possible, however, that the upper end of the scale we used for debt (≥$150,000) could have understated the magnitude of the problem. We based the ranges of debt on several other studies9,14,16; however, from some of the comments we received in the “free text” section, this might have been an underestimation. One of the survey participants reported that he/she had >$350,000 in medical school debt alone and knew others who were in the $400,000 range.
Moonlighting is a theme in residency that is a polarizing topic that other specialties have assessed.9,16 In our survey, we found that those residents with the most medical school debt were most likely to “strongly agree” or “agree” that they were going to moonlight during their training. What was fascinating, however, was that the opportunity to moonlight at a particular residency or fellowship did not influence one group over another to attend a particular residency or fellowship. This shows that one group is not influenced to join one residency/fellowship based on the opportunities to moonlight, but once in residency, those with the higher amounts of medical school debt are more likely to want to moonlight during their training years. However, offering moonlighting activities to residents can be fraught with problems due to a highly demanding work schedule and accumulation of additional moonlighting hours.3 The new ACGME guidelines in effect since July 2011 will include external moonlighting in the duty hour calculations as well, thus creating limitations on available time for moonlighting.17 Even if the work duty hours are kept within the 80-hour work week rule, programs will still have to provide funding for those moonlighting activities. Financial management is already a challenge for anesthesiology residency training programs.18 However, moonlighting residents could also potentially help a residency program's bottom line because it is still less expensive to use anesthesiology residents to cover anesthesia locations than to hire physician extenders.19
Most of our responders with higher medical school debt loads were more likely than other groups to be interested in pursuing private practice. This is not a surprising finding in light of the 10- to 15-year history of challenges of recruitment and retention within academic anesthesiology departments.18,20 When academic departments are grooming residents into academic medicine, they should consider incorporating financial education within its curriculum at the resident or fellow level. Also, academic departments might have another recruitment tool of interest for the new generation of anesthesiologists: a debt reduction program.
Those with a larger amount of medical school debt were more likely to be interested in a debt repayment program. It is unknown whether a debt reduction program versus an actual increase in salary would have the same response among this group of potential employees; however, if a debt reduction program allows for more flexibility in funding from the employer or if there are government subsidy programs that would assist with debt repayment programs, then these types of programs would be a potential viable recruitment tool to use.
For the reasons stated above, it is quite possible that academic anesthesiology departments are failing to reach good, talented people because of debt loads accrued in medical school; the medical school experience may be shaping our future anesthesiologists in ways that may be difficult to undo.
Although the impact of debt load on anesthesiology residents is important to evaluate, it can only be addressed by using a survey instrument, and conducting a large-scale survey brings with it certain limitations. Because we had to contact the programs to have access to their residents, there was some selection bias on behalf of their programs. It is also possible that there was some self-selection by residents to participate in the survey if they had strong opinions about the topics covered. Even though we had a sufficient number of completed surveys to satisfy statistical analysis, with the above limitations, we cannot guarantee that the obtained sampling of residents was representative of the group as a whole.
In conclusion, those respondents with larger debt loads were more likely to have the desire to moonlight during residency/fellowship, less likely to say they would choose an academic faculty career, and more likely to be interested in a debt repayment program once they graduated. In an effort to compete with private practice anesthesiology groups and to reduce the impact of debt on future career choices of residents/fellows, academic anesthesiology groups would do well to 1 promote moonlighting activities that are within the ACGME and institutional guidelines, 2 develop a financial curriculum for residents/fellows, and 3 offer debt repayment programs as an incentive for new faculty to join academic medicine.
Name: Jeffrey W. Steiner, DO.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Jeffrey W. Steiner has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
Name: Radu Pop, MS.
Contribution: This author helped design the study and conduct the study.
Attestation: Radu Pop has seen the original study data and approved the final manuscript.
Name: Jing You, MS.
Contribution: This author helped analyze the data and write the manuscript.
Attestation: Jing You has seen the original study data and approved the final manuscript.
Name: Stephen Q. Hoang, MD.
Contribution: This author helped write the manuscript.
Attestation: Stephen Q. Hoang approved the final manuscript.
Name: Charles W. Whitten, MD.
Contribution: This author helped design the study and write the manuscript.
Attestation: Charles W. Whitten approved the final manuscript.
Name: Catherine Barden, MD.
Contribution: This author helped write the manuscript.
Attestation: Catherine Barden approved the final manuscript.
Name: Peter Szmuk, MD.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Peter Szmuk has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
This manuscript was handled by: Franklin Dexter, MD, PhD.
We thank the Residency Programs and Residents who participated in this study. We could not have produced this body of work without your help.