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The European Journal of Health Economics
Published in: The European Journal of Health Economics 1/2019

Open Access 01-02-2019 | Original Paper

Are cost differences between specialist and general hospitals compensated by the prospective payment system?

Authors: Francesco Longo, Luigi Siciliani, Andrew Street

Published in: The European Journal of Health Economics | Issue 1/2019

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Abstract

Prospective payment systems fund hospitals based on a fixed-price regime that does not directly distinguish between specialist and general hospitals. We investigate whether current prospective payments in England compensate for differences in costs between specialist orthopaedic hospitals and trauma and orthopaedics departments in general hospitals. We employ reference cost data for a sample of hospitals providing services in the trauma and orthopaedics specialty. Our regression results suggest that specialist orthopaedic hospitals have on average 13% lower profit margins. Under the assumption of break-even for the average trauma and orthopaedics department, two of the three specialist orthopaedic hospitals appear to make a loss on their activity. The same holds true for 33% of departments in our sample. Patient age and severity are the main drivers of such differences.
Appendix
Available only for authorised users
Footnotes
1
There are specialist hospitals in Europe [4, 6], America [7, 8], Asia [9], India [10], and Africa [11].
 
2
More than 60% of hospital income comes from the NTPS. The remaining part is agreed in the NHS standard contract on the basis of actual activity [19].
 
3
The trim point is the maximum expected length of stay for a patient falling under a specific HRG. It is defined by the Department of Health in order to identify unusually long lengths of stay and statistical outliers [21].
 
4
At the time of our study, top-ups were paid for children’s, orthopaedic, spinal, and neurosciences specialised services. While all hospitals can obtain the top-up for specialised orthopaedic services, top-ups for the other specialised services are paid to a restricted number of eligible providers.
 
5
In the English NHS, a hospital trust or acute trust is an authority that provides secondary health care services through one or more acute hospitals.
 
6
The number of patients is expressed by the number of finished consultant episodes (FCEs). A FCE is a hospital episode for a patient under the care of an individual consultant.
 
7
To illustrate this point, suppose that a specialist orthopaedic hospital s and a T&O department in general hospital g have the same volume of patients and excess bed days (\(y_{\text{s}} = y_{\text{g}}\), \(q_{\text{s}} = q_{\text{g}}\)), the same location (\(m_{\text{s}} = m_{\text{g}}\)), and the same proportion of top-up tariffs (\(e_{\text{s}} = e_{\text{g}}\)). Then, differences in profits will be equal to \(\pi_{\text{g}} - \pi_{\text{s}} = \left( {c_{\text{s}}^{\text{IN}} - c_{\text{g}}^{\text{IN}} } \right)y_{\text{g}} + \left( {c_{\text{s}}^{\text{EB}} - c_{\text{g}}^{\text{EB}} } \right)\). For instance, \(\pi_{\text{g}} - \pi_{\text{s}} > 0\) implies that the specialist orthopaedic hospital has lower profit margins than the T&O department in a general hospital. Such a difference will reflect factors not allowed for in the payment mechanism.
 
8
We take the logarithm to improve model fit, since unit costs are left-skewed. All estimated coefficients are therefore interpreted as semi-elasticities.
 
9
The computation of the overall profitability for model IV in Eq. (12) differs from the computation described in Eq. (13). It becomes \({{\left( {\bar{\pi }_{k} - \bar{\pi }} \right)} \mathord{\left/ {\vphantom {{\left( {\bar{\pi }_{k} - \bar{\pi }} \right)} {\bar{C}}}} \right. \kern-0pt} {\bar{C}}} = {{\left[ {\left( {\bar{c}_{k}^{\text{IN}} - \bar{c}^{\text{IN}} } \right)\bar{y} + \left( {\bar{c}_{k}^{\text{EB}} - \bar{c}^{\text{EB}} } \right)\bar{q}} \right]} \mathord{\left/ {\vphantom {{\left[ {\left( {\bar{c}_{k}^{\text{IN}} - \bar{c}^{\text{IN}} } \right)\bar{y} + \left( {\bar{c}_{k}^{\text{EB}} - \bar{c}^{\text{EB}} } \right)\bar{q}} \right]} {\left( {\bar{c}^{\text{IN}} \bar{y} + c^{\text{EB}} \bar{q}} \right)}}} \right. \kern-0pt} {\left( {\bar{c}^{\text{IN}} \bar{y} + c^{\text{EB}} \bar{q}} \right)}}\), where \(\bar{\pi }_{k}\) and \(\bar{c}_{k}\) are the hospital k’s total profit and unit cost respectively averaged across HRGs and hospitals, \(\bar{\pi }\) and \(\bar{c}\) are the total profit and unit cost respectively averaged across HRGs and hospitals, \(\bar{C}\) is the total cost averaged across HRGs and hospitals, \(\left( {\bar{c}_{k}^{\text{IN}} - \bar{c}^{\text{IN}} } \right) = \tilde{\beta }^{\text{IN}} \bar{c}^{\text{IN}}\) and \(\left( {\bar{c}_{k}^{\text{EB}} - \bar{c}^{\text{EB}} } \right) = \tilde{\beta }^{\text{EB}} \bar{c}^{\text{EB}}\). Also in this case, the standard errors of the overall estimates are bootstrapped using 1000 repetitions.
 
10
Unlike elective and day case patients, the admission of non-elective patients is unplanned. Day case and short non-elective patients do not have an overnight stay in hospital, while elective and long non-elective patients have at least one overnight stay.
 
11
10 T&O departments in general hospitals did not report data on PROMs for hip or knee replacement and they are, therefore, dropped from the sample.
 
12
We count specialised services following the rules defined in the Prescribed Specialised Services, and not the criteria specified in the Specialised Services National Definition Sets. We use the overall Index of Multiple Deprivation (IMD) as a measure of socio-economic status. This index is constructed through the combination of seven IMD domains such as income, employment, health deprivation and disability, education, skills and training, barriers to housing and services, crime, and living environment. A value of one indicates extreme deprivation while 32,482 indicates no deprivation.
 
13
The salary of a doctor employed in the T&O specialty is estimated through a s-shape function of age, minimum and maximum salary. Further details are provided in the "Appendix 1".
 
14
More precisely, the risk-adjustment methodology comprises three steps. The first step consists of estimating a generalised least-square fixed-effects model in which the dependent variable is the post-surgery EQ-5D score of each patient, the covariates are pre-surgery EQ-5D score, patient characteristics (e.g., gender, age, ethnicity), economic deprivation, comorbidity, procedure and post-operative length of stay. This regression also controls for unobserved hospital heterogeneity through fixed effects. In the second step, the model is used to estimate predictions. The third step aggregates such predictions to obtain the adjusted average post-surgery EQ-5D score for each provider, from which the national average pre-surgery EQ-5D score is subtracted for the calculation of the adjusted average health gain.
 
15
Tables 6 and 7 in the "Appendix 2" show descriptive statistics of the variables measured at HRG level for the sample with day case and elective observations, and short non-elective and long-non elective observations, respectively.
 
16
The exponential transformation is applied to all the figures reported in the text in this section. This explains the differences with the coefficients reported in Table 3.
 
17
Recall that the unit cost is the dependent variable in model II (III or IV) while tariffs are on the right-hand side of the equation. Under such a regression design, β reflects the difference between unit costs and tariffs instead of the definition of profit margins, i.e., difference between tariffs and unit costs. To abide by the correct definition of profit margins, the interpretation of β must be reversed so that, for example, a positive estimate indicates lower profit margins in specialist orthopaedic hospitals relative to T&O departments in general hospitals.
 
18
To reinforce this finding, we provide the results of a stepwise regression in Table 8 in the "Appendix 3". These results show that age, number of diagnoses, and procedures together drive the differences in inlier unit costs between specialist orthopaedic hospitals and T&O departments in general hospitals, with there being a seeming difference between the hospital types if any of these patient characteristics are omitted. Table 9 shows that differences between hospital types in per diem unit costs are always statistically insignificant whether or not patient characteristics are accounted for.
 
19
As a further robustness check, we estimate model V, which is akin to model III but also includes hospital random effects. Unlike the hospital fixed-effects model, the hospital random-effects model can be estimated when the specialist orthopaedic hospital dummy is included, although this requires the additional assumption that the covariates are uncorrelated with the HRG-invariant unobserved hospital heterogeneity. Table 11 in the "Appendix 3" shows that the results for model V are very similar to those for model III.
 
20
Recall that the percentage change (\(\tilde{\beta }\)) is obtained through the exponential transformation of the estimated coefficient (\(\hat{\beta }\)).
 
21
We count only hospitals for which confidence intervals do not overlap the dashed horizontal line at zero, i.e., hospitals for which the deviation of profit margins from the mean is statistically different from zero.
 
22
Recall that βk in model IV captures the deviation of hospital k’s profit margins from the mean profit margins: a positive βk means that hospital k’s profit margins are lower than the mean, while a negative βk suggests that hospital k’s profit margins are higher than the mean (see also footnote 17 for details on the interpretation). For ease of interpretation, we multiply the estimate of βk by minus one and, therefore, the negative sign now indicates profit margins that are lower than the mean. All coefficients in Table 5 indicate the percentage change (\(\tilde{\beta }_{k}\)) obtained through the exponential transformation of \(\hat{\beta }_{k}\).
 
23
The full-time equivalent ratio is the proportion of the total number of paid hours during a period over the number of working hours in that period.
 
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Metadata
Title
Are cost differences between specialist and general hospitals compensated by the prospective payment system?
Authors
Francesco Longo
Luigi Siciliani
Andrew Street
Publication date
01-02-2019
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 1/2019
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-017-0935-1

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