Abstract
One of the stages of medical training is the residency programme. Hosting institutions often claim compensation for the training provided. How much should this compensation be? According to our results, given the benefits arising from having residents among the house staff, no transfer (either tuition fee or subsidy) should be set to compensate the hosting institution for providing medical training. This paper quantifies the net costs of medical training, defined as the training costs over and above the wage paid. We jointly consider two effects. On the one hand, residents take extra time and resources from both the hosting institution and the supervisor. On the other hand, residents can be regarded as a less expensive substitute to nurses and/or graduate physicians, in the production of health care, both in primary care centres and hospitals. The net effect can be either positive or negative. We use the fact that residents, in Portugal, are centrally allocated to National Health Service hospitals to treat them as a fixed exogenous production factor. The data used comes from Portuguese hospitals and primary care centres. Cost function estimates point to a small negative marginal impact of residents on hospitals’ (−0.02%) and primary care centres’ (−0.9%) costs. Nonetheless, there is a positive relation between size and cost to the very large hospitals and primary care centres. Our approach to estimation of residents’ costs controls for other teaching activities hospitals might have (namely undergraduate Medical Schools). Overall, the net costs of medical training appear to be quite small.
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Notes
The terms resident, intern and trainee doctor will be used interchangeably. It stands for a student which has graduated from Medical School and has engaged a Graduate Medical Education—specialty or general practice—process.
In most European countries this would be the Ministry of Health.
The importance of the exogeneity assumption will be explained later on.
By teaching hospital/primary care centre we mean an institution which has at least one resident enrolled in either the first stage (foundation years, in the UK) or a specialty/GP training programme.
See [14] for a discussion on the topic and examples.
These techniques will be explained later on, when we address the methodology used.
In order to become a teaching hospital or medical centre, the institution is subject to an accreditation process, having to fulfill a set of prerequisites regarding facilities, services and availability of supervising physicians. In Portugal, the process is coordinated by the National Council of the Resident (CNMI). In the US, the process is lead by the Accreditation Council for Graduate Medical Education (ACGME). The same type of advisory board exists in many other countries.
In the US, the matching process is run by the National Resident Match Program for the majority of GME programmes.
A review of the Portuguese Health System is available [19], including the organizational structure of the Ministry of Health and related councils responsible for GME.
Residents increase medical care production in terms of discharges, even though their contribution is below one could expect, given the higher rate of resource utilization [20, pp. 344–349].
By writing the interaction equations as
$$ \mathbf{L_{m}}=L_{1}+\beta f\left(L_{2}\right) $$$$ \mathbf{L_{n}}=L_{3}+\theta g\left(L_{2}\right), $$we can assume different forms for the substitutability pattern. For example, decreasing returns to scale is given by \(g\left(L_{2}\right)=\sqrt{L_{2}}\).
Along with heteroskedasticity consistent OLS and the robust regression, we were able to estimate a stochastic cost frontier. The advantage of doing so is the possibility of accounting for multiple outputs, quasi-fixed inputs and exogenous input prices, which are important features of our model [21, pp. 131–136].
The value of w 2 is not as straightforward as one could expect, since it has to take into account the increase in wages along residency years. The analysis will consider the total number of residents, treating them as equal. The average wage is a weighted average, combining 2 years of internship and 4 years of specialty residency. Social contribution amounts to 23.75% of the wage, leading to wage cost of 25,539.36€ per resident, per year.
Details available from the authors upon request. See also the previous working paper version.
We take the observations as pooled cross section, without taking into account the possible panel structure of the data. This option is plausible given the changes in management rules, mergers between hospital’s administrative boards and missing observations that occurred in the period. Panel data estimation procedures didn’t add much information to the results.
See “Appendix 1: Data sources” for a full description of the variables’ sources.
We interchangeably use the terms resident, intern, trainee resident to describe a medical student, enrolled in some Graduate Medical Education Programme. The term physician refers to a senior or graduated physician who has already finished medical studies, including graduate medical education. Even if an institution doesn’t host any residency programmes, it will have physicians delivering medical care, hence positive house staff expenditures.
Further discussion of this change is available [19].
The Hospital Case-mix index is defined by:
$$ \frac{\sum^n_i \left( DS^n_i \times PR^n_i \right) }{\sum DS} \qquad \qquad i=1,..., n $$(9)where i stands for each DRG, DS stands for inpatient discharges and PR for the relative price-weight assigned to each DRG (the proxy for the DRG’s complexity).
Hospitals’ dimension is measured by the number of beds. For primary care centres, we resort to the number of physicians.
The chapter by Berk on robust regression provides an overview of this estimation method [31, pp. 292–394].
To estimate the inefficiency term of the stochastic frontier, we have to assume a parametric form for the distribution of the term (exponential or the half-normal distribution) [21].
Full estimates are available in the “Appendix 1”. Standard errors and significance levels are as shown in all tables.
Changes concerned mostly financing, budget management and human resources, including major differences in the type of contract between the hospital and the employee [32].
The SRS hosts the residency programmes and determines how are the residents to be allocated to the primary care centres under its jurisdiction. It is also responsible for the funding of these programmes, together with the payment schemes and the budget of each primary care centre.
All continuous variables are in the logarithmic form, except for the Residents variable. The referees suggested different ways to deal with non-linearities of effects. Further work on the different possibilities suggested by the both referees and on other possible treatments of non-linear effects lead to adoption of the following models.
Examples of such cost functions are available in [14].
The variable Residents was not included in the estimation due to collinearity. Summing the variables Residents and R 2Q phys to R 4Q phys yields a column of ones. We chose to take the interaction term with the first quartile as the baseline, and that is why this variable isn’t listed in the estimation results as well.
See “Appendix 1: Data sources” for the full estimates (Tables 20 and 21). We have omitted SRS parameter estimates to focus on the effects we are most concerned on.
See “The cost of teaching” for the estimation results of the cost effect of the teaching status.
The net cost effect is defined as the difference between the average marginal cost effect of residents and the reference annual wage paid to residents (25,540€).
We focus on the robust regression estimates, mainly due to the restriction it imposes on the effect of the outliers on costs. These outliers explain the differences between heteroskedasticity-consistent OLS and robust regression estimates.
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This paper is based on a working paper, “The net costs of medical training” (joint work with Ana Simões), available at SSRN: http://ssrn.com/abstract=1119822. We have benefited from useful comments and suggestions of Andrew Street, Miguel Gouveia, Carlota Quintal, three referees and several seminar participants. The usual disclaimer applies.
Appendices
Appendix 1: Data sources
Appendix 2: Stochastic frontier estimation
Appendix 3: The cost of teaching
We will now focus on the simple teaching cost effect, which can be done by adding an indicator variable of the teaching status to the estimated cost function.
Hospitals’ cost function parameter estimates (Table 17) point to a significant impact of teaching on the cost structure. Furthermore, there is a positive relationship between dimension and costs. The effects of the other covariates are similar to the ones obtained in Section 6.
The results regarding primary care centres show (Table 18) that teaching institutions have higher costs. However, large teaching institutions can overcome this negative effect and up spending less, on average. Once again, the cost function parameter estimates (Tables 18 and 19) are similar to the ones obtained previously (Section 7).
Appendix 4: Estimation results—primary care centres
Appendix 5: complementary view
The quality of data is always a debatable issue and our case is not different. There is strong variation across health care providers, be it hospitals or primary care centres. Since our empirical statistical analysis is deeply rooted in the nature of labor substitution between residents and senior doctors, there is the danger that our assumptions on this may be leading the results.
To check on the issue, interviews with residents were conducted, where a description of the typical working week of a resident was sought. The interviews were conducted as a cross check for the estimation results. Four of the interviews were scheduled and lasted between 1 and 1.5 h. One of the residents was enrolled in a GP specialty programme, and the others were enrolled in inpatient care specialties. Six other interviews were conducted without scheduling the interview. In each of them, strict confidentiality was ensured. In particular, we were interested in identifying time lost by senior doctors on training as well as situations where residents’ activities replaced those of senior doctors.
According to our sample of residents, their 42 h schedule can be divided into five tasks: 12 h are spent in emergency room shifts (they can devote more than 12 h to emergency room, but they are paid extra for it); paper work amounts to 10 h (which would have to be done by senior doctors in the absence of residents), including writing clinical reports and patient histories; 8 h are spent with the supervisor; studying the materials asked by the supervisor takes up to 5 h; residents spend 7 h per week visiting patients and talking to patients’ families. It is clear residents take up the bureaucratic part of the job, leaving their supervisor with some extra available time, even taking into account the time they have to spend with the student.
Residents’ work has some drawbacks. Technically, they are not as good as senior doctors, above all because of the extra time and resources (mostly diagnosis procedures) residents spend when treating patients. However, much of this difference depends on the chosen specialty. Globally, the total effect of residents’ work benefits the institution, either directly (work) or indirectly (supervisors can spend extra time providing health care, instead of doing paper work).
By being so, having residents learning at one’s institution is a way of enhancing the workload distribution among the different types of labour comprised by the house staff. Therefore, the qualitative information is in line with the econometric results obtained earlier.
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Barros, P.P., Machado, S.R. Money for nothing?. Health Care Manag Sci 13, 234–255 (2010). https://doi.org/10.1007/s10729-010-9126-7
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DOI: https://doi.org/10.1007/s10729-010-9126-7