The main questions in this article were: a) how is list size related to consultation length, waiting time to get an appointment, and the likelihood that GPs conduct home visits? And b) to what extent are the relationships between list size and these three variables affected by remuneration?
Our results indicate that list size is negatively related to consultation length, especially among GPs with relatively large lists. A correlation between list size and waiting times and list size and likelihood of a home visit was only found for GPs with small practices. These correlations are modified by the proportion of patients for whom GPs receive capitation fees. The associations are, however, relatively weak compared to correlations with patient characteristics such as sex, age and health.
Our theoretical approach led us to assume that, in general, a large patient list would be associated with shorter consultations, longer waiting times and fewer home visits. We expected these correlations for all GPs, because these are ways to manage patient care and maintain control of their workload. We expected these correlations to be stronger when the financial consequences are more favourable. These financial consequences are determined by the share of the population for which GPs receive payment based on FFS. We also expected these interaction effects to be stronger in smaller practices than in larger practices, because small lists provide more room for decision-making.
The consultation length correlates negatively with list size. This finding was in line with hypothesis 1. The relation is, however, weak: a difference of approximately 1 minute per 1000 listed patients. Previous studies also found a negative relationship between consultation length and measures of workload [2
]. Hypothesis 1a was not confirmed: the relationship between list size and consultation length seems not to be affected by remuneration. Contrary to our expectations, the relationship between list size and consultation length was stronger and only statistically significant among GPs with large practices. In most studies where the relationship between workload and consultation length was investigated, list size was used as workload measure. In these studies, conflicting results have been reported (see for an overview Wilson et al. and Hofman-Okkes [16
]). Many of these studies showed that consultation length is not, or only weakly, related to list size. It has been shown that some other doctor-related factors affect consultation length positively; these include a positive attitude towards the profession and job satisfaction. Mechanic found that those with a high level of job satisfaction were prepared to 'let the patient talk for half an hour or more' [16
]. Another interesting finding is that low self-rated health is positively related to consultation length. The proportion of patients with low self-rated health is, however, negatively related to consultation length. This probably illustrates the effect of choices that have to be made with respect to the division of time between patients. Obviously, patients with bad health often require more time, but when there are many of them, there is less time per patient.
The hypothesis (2) that the list size lengthens the waiting time to get an appointment, was not confirmed. The interaction-coefficient that we found is in contrast with our hypothesis (2a). We did find a significant interaction between list size and proportion of publicly insured only among the GPs with small practices. This negative interaction indicates that waiting times tend to become shorter with increasing list size when there is a high proportion of publicly insured patients. This finding is remarkable. After all, longer waiting times seem to be more attractive in the case of publicly insured patients for whom GPs only receive capitation fees.
Home visits are more often carried out with female patients, older patients and patients who are relatively unhealthy. Hypothesis 3 was not confirmed. The finding that home visiting is not related to the list size in our total model is in line with previous findings [33
]. In the smaller practices we found a slight negative interaction between list size and the proportion of publicly insured patients. This means that the likelihood of a home visit rises with increasing list size when the proportion of publicly insured is relatively low. This is in line with our hypothesis (3b). Patient characteristics seem to be the most important determinants of home visiting rates. Especially those with a poor self rated health have a substantially higher chance to be visited. Obviously, people with a poor health more often suffer with complaints that restrained them to go to the practice. Calnan and Butler did find a negative relationship between list size and home visiting rates, but did not take population characteristics into account [19
We hypothesized that the relationship between remuneration on the one hand, and indicators for decisions about how they provide consultations on the other hand, are stronger for GPs with a relatively small list size than for those with a relatively large list size. This is partly confirmed by our findings. In the analyses of waiting times and of home visits we did find a significant interaction among the small practices and not among the large practices. A possible explanation for the finding that remuneration seems only to have a small influence might be that Dutch GPs earn a high income compared to most other countries, and therefore, earning enough income is not much of an issue [34
]. Another explanation for the absence of the expected relationships is that the influence of their payment is small compared to the other factors that influence GPs' behaviour such as medical assessments and the care for the patients' wellbeing. It is also possible that the workload of most GPs is simply so high that they cannot afford to base their decisions on remuneration factors. After all, beyond a certain limit, all GPs will try to reduce their workload no matter whether the extra work is compensated for or not. Another explanation could be that the effect of factors related to morbidity was insufficiently taken into account. Publicly insured patients are on average less healthy than privately insured patients. We tried to correct for this by controlling for self-rated health but more detailed corrections may be possible.
Some shortcomings of this study include the following. First, the design of the study is not ideal for investigating coping behaviour. Obviously, since the study is cross-sectional, we can only talk about statistical relations, and real causal relations cannot be shown. Yet, theoretically grounded hypotheses that are tested in a cross-sectional study are at least strong indications for causal relations. Furthermore, as in all studies that use routinely collected data, the data can be biased by the recording behaviour of GPs. However, the type of data that we used contains relatively simple data such as consultation type (home visit, office consultation). Moreover, the data collection was intensively controlled by field workers. In our analyses of the likelihood of a home visit, we dichotomised the dependent variable into 1 (home visit) and 0 (office consultation or telephone consultation). Another way to analyze this is to estimate a multinominal model which compares the three types of contacts. This would be an interesting approach for future research. However, we were especially interested in home visits, because we assume that this issue is of greater importance for patients. It is very unlikely that a GP would refuse an office consultation if the patient asks for it, but with regard to the decision to conduct a home visit, GPs are much stricter. A last shortcoming that should be pointed out concerns the waiting time to get an appointment. We asked this in a GP-questionnaire, which means that we only have one measure per GP. Obviously, it would be better to ask patients after every consultation. Yet, as we mentioned earlier, this measure appeared to correlate strongly with other measures for accessibility that were measured on patient level. This indicates that the answers GPs gave were fairly reliable.