Used as a performance metric since 2001, VA-OAA has administered the LPS annually to all trainees who rotate through VA medical centers. To reflect overall satisfaction, respondents rate “clinical training … on a scale from 0 to 100, where 100 is a perfect score and 70 is a passing score.” We dichotomized overall satisfaction responses from all available surveys (2001–2007) into satisfied (≥70) or otherwise (<70), where 70 was defined by VA as a “passing” score.
Respondents also rated each of five domains on a five-point scale (List 1). For these analyses, to compute the odds that a resident reported being satisfied, we dichotomized responses into “satisfied” (very satisfied, somewhat satisfied) and “otherwise” (neither satisfied nor dissatisfied, somewhat dissatisfied, very dissatisfied). Since 2001, domains included satisfaction with clinical faculty/preceptors, learning, working, and physical environments. A fifth domain, clinical environment, was added with the 2003 survey.
The VA added an ACGME duty hours limits question beginning with the 2004 survey (List 1). The question read, “In July 2003, the Accreditation Council for Graduate Medical Education instituted changes in requirements in duty hours/scheduling for resident education. In your opinion, what effect have these changes had on your educational experience at the VA facility…?” Respondents rated their answers on a five-point scale. To adjust pre–post differences for time trends, we constructed a differencing variable by dichotomizing responses to this question to classify each responder as either a no-effect control (response of “no effect”) or an effect-responder (response of “very positive,” “somewhat positive,” “somewhat negative,” or “very negative” effect).
Several scenarios could explain why residents may have claimed that ACGME duty hours limits had no effect on their VA clinical training settings. For example, a resident may have worked a schedule of hours that was within the duty hours limits whether or not the training program was enforcing the ACGME-mandated duty hours rules. Alternatively, the training program may have ignored duty hours rules, at least during the respondent's rotation.
To adjust for trend biases, we constructed the differencing variable to combine responders who reported either positive or negative perceptions of duty hours effects. We combined the two groups because our purpose was to measure how duty hours limits influenced residents' ratings of their training environment, and not to determine how residents actually perceived duty standards. Although important, the latter research lies outside the scope of the current study.
The LPS also obtained demographic information about each respondent, including gender, training level (postgraduate year), and specialty. For our analyses, we grouped resident specialties into medicine (internal medicine, neurology, physical and rehabilitation medicine), surgery (surgery, anesthesiology), psychiatry (including psychiatric subspecialties), and ancillary care (diagnostic specialties, radiology, pathology). Beginning in 2003, the LPS began collecting information on medical school graduation (date graduated, U.S. versus foreign medical school) and the mix of patients seen during the respondent's VA rotation. We estimated patient mix from survey responses as the percentage of patients the respondent reported seeing during “an average week, at the VA…” for each of seven patient categories: 65 years of age or older, chronic mental illness, chronic medical illness, multiple medical illnesses, alcohol/substance dependence conditions, low-income socioeconomic status, and no social/family support.
To be comparable with other studies, we computed the effect of ACGME duty hours limits on resident satisfaction for each satisfaction domain as a ratio of odds ratios (ROR) describing whether the resident reported satisfaction or otherwise. The ROR numerator is calculated for effect-responders and equals the odds that these respondents would have reported satisfaction in the postperiod divided by the odds that these same respondents would have reported satisfaction in the preperiod. The denominator is calculated in the same way, but only for no-effect controls. ROR = 1 indicates that pre–post changes in satisfaction rates among effect-responders were no different from the pre–post changes among no-effect controls, and suggests that no duty-limit effect on satisfaction was observed. On the other hand, ROR > 1 indicates that pre–post changes in satisfaction rates among effect-responders were greater than their no-effect counterparts, and suggests that duty hours limits were associated with higher satisfaction rates. Similarly, ROR < 1 suggests that duty hours limits led to decreased satisfaction rates.
ROR is adjusted for both covariate and trend biases using a robust differencing variable technique that extends difference-in-differences analyses29 to logistic regression35 by (1) using resident-level training environments as the unit of analyses, (2) identifying control respondents to adjust for trend biases, (3) accounting for missing data without imputation noise, (4) performing an exhaustive model search to adjust for covariate biases, and (5) computing estimates of effect sizes and confidence intervals (CIs) that are robust to model misspecification (see Mathematical Appendix, Supplemental Digital Content 1, http://links.lww.com/ACADMED/A19).
To account for covariate biases between pre- and postperiods, and effect- and control responders, we adjusted satisfaction outcomes based on responder characteristics and clinic experience. To account for nonlinear associations, we used a maximum-likelihood recoding strategy to transform all continuous and ordinal variables into binary covariates. Specifically, each continuous and ordinal variable was independently dichotomized using nonparametric, bootstrapped, maximum-likelihood cut-point estimates for each of the five domains and overall satisfaction score.36 For each dependent variable, we determined a model containing the most predictive covariates from an exhaustive model search37 using the generalized Akaike information criteria38 based on data from postlimits periods. We then validated these empirically motivated models using a 10-fold cross-validation approach.39
Next, we constructed a theoretically motivated model to contain three specific variables. First, a period indicator variable assumed a value of zero if the respondent answered the LPS survey in prelimits years (2001–2003), or a value of one if the respondent had answered the survey during postlimits years (2004–2007). Second, a differencing variable was constructed to assume a value of zero if the respondent was a no-effect control, and a value of one if the respondent reported either a positive or negative effect to the ACGME duty hours limits question (effect-responder). Third, a period × differencing variable interaction term was computed by multiplying the period indicator and differencing variables for each respondent.
We constructed a final model by combining the terms that made up the theoretically and empirically motivated models. All models included a constant term. We then used a nonnested model selection test40–43 to compare the fit of all three models. If the final model fit the data better than either theoretically or empirically motivated models, then duty hours limits effects were estimated by exponentiating the estimated coefficient to the period × differencing variable interaction term to the final model. To semantically interpret the interaction term, we assumed that the adjusted impact of the differencing variable on satisfaction is invariant with time (see Mathematical Appendix, Supplemental Digital Content 1, http://links.lww.com/ACADMED/A19).
The LPS did not ask respondents about duty hours limits in prelimits periods when ACGME rules were not enforced (missing-data problem). However, the concept of a no-effect control in prelimits periods is still relevant. Although one can only speculate about the actions of VA staff during 2001–2003, it is possible that some residents were assigned to work schedules that complied naturally with the duty hours rules. Residents may also have been supervised by attending physicians who would have done business as usual and ignored duty hours limits had such rules been mandatory. Assigning values to the differencing variable for prelimits responders is treated as a missing-at-random problem. That is, by knowing the year of the survey, one knows whether the value of the differencing variable is missing.44 Concerning missing data, the period × differencing variable interaction term will always equal “zero” during prelimits periods. Thus, only the differencing variable as a main effects term will have missing data. Rather than using imputation, we computed maximum-likelihood estimates by taking into account all possible patterns of values for the missing data. Missing values among covariates caused by later additions to the LPS survey were also treated in this way. Thus, model coefficients were computed directly from the observable component of the data without imputation noise.
Final models were tested for fit, the presence of model misspecification, and multicollinearity. Because of the potential for misspecification, robust estimation methods that are valid in the presence of model misspecification were used to compute both parameters and CIs.29,45,46
Table 1 presents characteristics and satisfaction scores for 19,605 LPS physician resident responders classified by reporting period. Variation in responder characteristics underscores the need to adjust for covariate biases. Compared with all residents in ACGME-accredited programs in 2008–2009,47 the LPS sample had slightly fewer females at 7,102 of 18,323 (39%) versus 48,823 of 108,176 (45%) residents in ACGME-accredited programs, had fewer international medical school graduates at 3,602 of 14,177 (25%) versus 29,488 of 108,176 (27%) ACGME residents, and fewer first-year residents at 5,498 of 19,605 (28%) versus 38,404 of 108,176 (36%) ACGME residents.
Table 2 reports estimates of duty hours limits effects measured as an ROR based on the robust differencing variable technique. The wide CIs reflect the uncertainty associated with working with incomplete datasets.
Overall, respondents tended to report higher satisfaction with their VA clinical training environment when duty hours limits applied. For instance, respondents overall were 2.46 times (95% CI [1.49, 4.05], P < .001) more likely to report satisfaction with VA as a clinical training environment under duty hours limits than without such standards. These findings held across each of the five domains, for all residents taken together, and for medicine residents only. Surgery residents tended to report higher levels of satisfaction only for clinical faculty or preceptors and clinical environment. Estimates for ancillary and psychiatry specialties were inconclusive.
To understand its relevance to education, we recalculated ROR estimates of duty hours limits effect sizes (Table 2) to reflect the adjusted estimate of the percentage of respondents who would change their response from “not satisfied” to “satisfied” as duty hours limits became mandatory (Table 3). The largest change occurred in the clinical environment domain. For surgery residents (ROR = 9.10, 95% CI [2.62, 31.61], P = .0005), satisfaction rates for clinical environments increased from a prelimits period rate of 60% (Table 1) to an expected 93% under mandatory limits, adjusted to reflect differences in the mix of respondents and other time trends in the data. That is, we estimate that 33 out of 100 respondents, who otherwise would not have been satisfied, would have reported satisfaction under mandatory duty hours limits. For medicine (ROR = 3.46, 95% CI [1.37, 8.70], P = .0084), the prelimits period satisfaction rate of 58% increased to 83%, for an adjusted net increase of 25% under the mandatory duty hours rules. Similarly, these data suggest that an expected 12% more surgery residents and 11% more medicine residents would have reported satisfaction with faculty or preceptors under ACGME mandatory duty hours rules than without such rules.
To show the importance of adjusting for covariate mix and time trends, the unadjusted pre–post period change in satisfaction with VA training environments is OR = 1.00 (95% CI [0.91, 1.11], P = .96). There was also little adjusted cross-sectional difference in overall satisfaction between effect-responders and no-effect controls (OR = 1.12, P = .66). This finding is comparable with those of studies showing few differences in patient outcomes between teaching and nonteaching VA hospitals.48 Females were generally more likely to report overall satisfaction for VA training (OR = 1.12, P = .038) as well as clinical (OR = 1.09, P = .039) and working (OR = 1.15, P < .001) environments. The higher rates of satisfaction among females are consistent with other surveys.49 Respondents who reported that 50% or more of the patients they saw were without family support, or were substance abusers, were only 56% (OR = 0.56, P < .0001) and 73% (OR = 0.73, P < .0001), respectively, as likely to report satisfaction with VA clinical training environments as their counterparts who saw fewer than 50% of such patients.
Using advanced statistical techniques to adjust for trend and covariate biases, we found that the 2003 ACGME standards significantly and materially enhanced learning satisfaction rates for medicine and surgery residents rotating through VA medical centers. The statistical tools, along with our large sample size and robust survey, provided a comprehensive estimate of the impact of duty hours limits on residents' satisfaction with their educational environment. Understanding these effects can provide useful information to government agencies, accrediting bodies, teaching hospitals, and program directors in assessing the effects of duty hours limits, and to understand how residents' satisfaction with their training environments can improve as duty hours limits rules are enforced.
These findings were consistent with subanalyses conducted across domain elements, and when satisfaction scales were “cut” at different levels. However, our results both compared to and contrasted with those of previous studies. Specifically, these findings are consistent with reported associations between reduced work hours and residents' perceptions of more time to read and learn independently,24,28,50 greater attending supervision,28,51 and attending physicians' increased role in patient care.52 In contrast, these findings differ from postsurveys22–25,53 and pre–post surveys26–28 that reported clinical experiences and patient-care quality remained unchanged, or even worsened, with fewer duty hours.
There are several possible reasons for the disparity between these survey findings and ours. First, the robust differencing variable technique applied here was designed to adjust for time trends using respondent-level controls with pre–post survey data. Such corrections, in fact, had an important effect on our study findings. For example, we found no ACGME duty hours limits effect on satisfaction rates (OR = 1.00, 95% CI [0.91, 1.11], P = .96) with LPS data when effect sizes were based entirely on unadjusted pre–post differences. Adjusting for time trends alone, the estimated effect size increased to an ROR of 2.13 (95% CI [1.27, 3.58], P = .004), and to 2.46 (95% CI [1.49, 4.05], P < .001) (Table 2) when estimates were further adjusted to account for differences in responder mix across periods and duty hours limits effect settings.
A second explanation for the discrepancies may involve differences in survey designs. For purposes of identifying control respondents, the LPS survey asked responders to rate satisfaction about current clinical rotations and whether duty hours limits (including limits on schedules and shifts) had an effect (good or bad) on the respondent's actual VA training environment. Postsurvey designs often focused on previous clinical training experiences and actual hours worked, which are subject to underreporting biases.54
A third difference may be attributable to the sample and the survey design. One-third of the nation's residents rotate through VA medical centers under VA affiliation agreements with 107 U.S. medical schools,55 with VA second only to Medicare and Medicaid as the largest funder of residency training in the United States.56 Although VA teaching medical centers likely differ from non-VA teaching hospitals, this is the largest survey of physician resident satisfaction to date and involves a variety of facility sizes and medical school affiliations in diverse geographic areas across the United States. Furthermore, the confidential LPS survey is administered by a federal agency under strict rules of confidentiality enforced under federal oversight by the Office of Management and Budget. Promoted as an administrative tool designed to improve VA as a clinical training environment,33,34 the LPS survey began with the 2001 academic year, three years before duty hours limits were first implemented, and one full year after full implementation of VA's quality improvement initiatives had been completed.57,58
Fourth, by classifying respondents individually into “effect” respondents and “no-effect” controls, we avoided aggregation errors created when respondents were grouped by educational program or facility. Overall, 36% of LPS respondents claimed that duty hours limits did not impact their VA clinical rotations during postlimits academic years (2004–2007). Such reports occurred across programs, specialties, and facilities, indicating the diversity of experiences residents encountered within the same programs and teaching facilities.
Finally, it may not be unusual to find “no-effect” environments after 2003 because some training programs had failed on occasion to adhere to mandatory duty hours rules. In one study, respondents reported exceeding the 80-hour rule at least once during six months in surgical (89%) and nonsurgical (74%) specialties while underreporting their work hours to their program directors (73% and 38%, respectively).54 In a national survey of interns after ACGME implementation, 67% reported working shifts beyond the 30-hour rule, 43% more than the 80-hour rule, and 44% less than the one-in-seven day rule.59 Despite having regulated resident duty hours since 1989, New York State found 54 of the state's 82 teaching hospitals were in noncompliance.60
The present study has certain limitations. VA clinic rotations may not necessarily represent experiences at non-VA locations. Second, respondents may not know when duty hours limits affected their training environments, thus leading to overreporting of “no effect” on the ACGME duty hours limits question. However, overreporting “no-effect” would bias estimates of duty hours limits effect sizes toward zero. Third, it is unknown whether resident satisfaction with clinical training is related to objective measures of education outcomes, such as in-service competencies examinations, board scores, and attending physician evaluations. Fourth, covariates we used to adjust for differences in respondent mix may not have controlled for all relevant factors that drive satisfaction rates. The study did not address why satisfaction may have changed, but this shift could be explained by many factors in addition to duty hours limits, including changes in workload, work life,61 resident cross-coverage, night-float systems, redistribution of workload, reassignment of noneducational tasks to midlevel and lower-level providers,62 clinical schedules that minimize sleep interruption,63 or reduced in-house on-call duties. Fifth, the results are based on resident perceptions and may not necessarily reflect true differences in the quality of patient care or the effectiveness of the teaching environment. Finally, it is unknown whether further restrictions on duty schedules will continue to improve resident satisfaction.
In summary, applying advanced statistical methods to robust survey data, we found the 2003 ACGME mandatory duty hours limits were associated with improved training satisfaction rates. With the prospect that ACGME may adopt new standards for resident duty hours,16 education researchers may wish to consider using the LPS survey design with robust differencing analyses to assess the impact of new standards across U.S. teaching hospitals.64
Sincere gratitude is expressed for the very generous review, academic direction, and support from Malcolm Cox, MD, chief academic affiliations officers, and Karen M. Sanders, MD, deputy chief academic affiliations officer, Veterans Health Administration, Department of Veterans Affairs (Washington, DC).
Gratitude is also expressed for the guidance and direction from Tetyana K. Kashner, MD, physician resident at the Pennsylvania State University Hershey School of Medicine (Hershey, Pennsylvania); Linda Godleski, MD, associate professor of psychiatry at the Yale University School of Medicine (West Haven, Connecticut); Catherine P. Kaminetzky, MD, assistant professor of medicine at the Duke University School of Medicine (Durham, North Carolina); Susan Kirsh, MD, associate professor of medicine at Case Western Reserve University School of Medicine (Cleveland, Ohio); and Edward H. Livingston, MD, professor of surgery at the University of Texas Southwestern Medical Center (Dallas, Texas).
Gratitude is also expressed for administrative and data management support from Keith Hoffman, database administrator at the Veterans Health Administration Allocation Resource Center (Braintree, Massachusetts); Robert S. Hinson, executive assistant, and Linda McInturff, Evert Melander, Cynthia Miller, and Dilpreet Singh from Veterans Health Administration Office of Academic Affiliations (Washington, DC); and Christopher T. Clarke, director, and David S. Bernett, Terry V. Kruzan, George E. McKay, and Laura Stefanowycz from the Department of Veterans Affairs Office of Academic Affiliations Data Management Center (St. Louis, Missouri).
This study was funded in part by the Small Business Innovation Research (SBIR) program from the National Cancer Institute (NCI) (R44CA139607; PI: S.S. Henley) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (R43AA014302, R43AA013670, R43/44AA013768, R43/44AA013351, R43/44AA011607; PI: S.S. Henley), and by the Department of Veterans Affairs' Health Services Research and Development Service (SHP #08-164; PI: T.M. Kashner).
The analyses for this study were conducted for administrative purposes by, and were under the direct supervision of, the Office of Academic Affiliations, Veterans Health Administration, Department of Veterans Affairs (VA-OAA), under review by OMB Information Collection (#2900-0691) approved for VA Form #10-0439, for all data collected through January 2010.
The opinions expressed herein do not necessarily reflect the views of the Department of Veterans Affairs or its affiliates, the National Cancer Institute, or the National Institute of Alcohol Abuse and Alcoholism.
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