On average, HSMR scores in the Netherlands declined between 2003 and 2005. The variation between hospitals, however, remained substantial (approximately 1.8 higher HSMR scores for the worst-five compared to the best-five hospitals). Furthermore, most hospitals maintained a stable relative position between 2003 and 2005, which suggests that the reliability of the HSMR is good. The explanatory analysis showed that the variables year, GPs per 10,000 inhabitants in the hospital region and hospital type were significantly associated with the HSMR.
In the literature various predictors of hospital mortality have been studied [3
]. The goal of this paper was to explain (between and within) variation in new Dutch HSMRs for the first time. In doing so, we were able to place Dutch results in an international perspective. Furthermore, we used multilevel modeling to account for the hierarchical structure of the data. Finally, we clearly explained the possibilities of HSMR scores: they can be useful from a societal perspective and they should not be used from a patient perspective.
The results should be interpreted with a number of study limitations in mind. First, the dataset used to calculate HSMR scores was based upon hospital episodes (an admission followed by a discharge) and not upon patients. Several episodes may involve one patient. Hospitals may have different policies regarding the number of episodes per patient, which influences the number of registered episodes. This could affect the HSMR score without reflecting differences in quality. Second, case-mix correction through the Dutch HSMR model may not capture all case-mix differences. Mortality was corrected for age, sex, primary diagnosis, length of stay and admission urgency. However, especially for secondary diagnoses, it was unknown whether specific comorbidities were present. Still, Aylin et al. [31
] argue that routinely collected administrative data (such as our data) can produce valid case-mix corrected measures of hospital mortality. A final consideration could be made with respect to the inputs. Remarkably, the labour input data did not explain any HSMR variation. It may well be possible that a further distinction between different types of labour or different personnel qualifications will give us more information and may in fact explain some of the variation.
The results and considerations show that the HSMR needs to be studied carefully, before making it public or incorporating it in policy decision making. Variation between hospitals would indeed seem to point at systematic differences in processes between hospitals leading to systematic HSMR variation. This is underlined by the ICC, which showed relatively large between-hospital variation.
What is notable here is the – on average – high HSMR for academic hospitals. Various explanations are possible. First, academic hospitals may perform more high-risk procedures which have a higher risk of death. These high-risk procedures may combine better health outcomes with higher risk of acute death. Therefore, they could be considered high quality care that causes higher HSMRs. Consequently, high HSMRs can result from good quality of care. Second, with respect to mortality, academic hospitals may perform worse than the others. This could happen as a result of organizational deficiencies. Academic hospitals may be too large, inefficient or have more inexperienced doctors. Table , however, shows that size hardly influenced the HSMR, and having inexperienced doctors (teaching status) did not have the sign to support this conclusion. Third, we may not have captured all the case-mix differences; rendering an HSMR comparison with other hospitals invalid. Model misspecification could be due to measurement errors, misspecified functional forms and omitted variable bias. One example of such an omitted variable is the readmission rate per hospital. Hospitals with high readmission rates may have more severe patients. However, the variable readmissions was not included due to underreporting.
While the third cause calls for an improved standardization of the HSMR, the other two causes do not. Good quality high-risk care will lead to better outcomes on other indicators of quality of care, and they remind us that no indicator will fully capture quality of care. For that goal we need global measures, not indicators. Moreover, the choice to provide high-risk care can be influenced by the hospital and therefore is no environmental factor. This also holds for organizational deficiencies. Further research should indicate which of the three explanations mentioned above contributes to the variation in HSMRs we observe and to what extent. Such research is required as without it we cannot rule out the possibility of incomplete standardization that is required to compare all hospitals.
Another remarkable result is the influence of the number of GPs in the hospital region. The presence of more GPs in the region is associated with a lower HSMR. This relationship was also found in the UK [4
]. This may confirm the hypothesis that in areas with relatively few GPs, GPs may experience a heavy workload. This could result in worse risk-management performance, affecting the health of the patients sent to the hospital. Alternatively, GPs may be less prone to settle in less attractive areas, and whatever makes these areas less attractive could lead to higher HSMRs.
In addition to global outcome measures, outcome indicators such as the HSMR clearly are indicators of interest. We argue that the HSMR can be a useful indicator to monitor hospital performance over time and to compare hospital performance between hospitals. While the HSMR is suited for that goal, it is estimated using varying populations and thus is not directly usable for individual prospective patients to choose a hospital.