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Background: Abnormal results of diagnostic laboratory tests can be difficult to interpret when disease probability is very low. Although most physicians generally do not use Bayesian calculations to interpret abnormal results, their estimates of pretest disease probability and reasons for ordering diagnostic tests may - in a more implicit manner influence test interpretation and further management. A better understanding of this influence may help to improve test interpretation and management. Therefore, the objective of this study was to examine the influence of physicians’ pretest disease probability estimates, and their reasons for ordering diagnostic tests, on test result interpretation, posttest probability estimates and further management. Methods: Prospective study among 87 primary care physicians in the Netherlands who each ordered laboratory tests for 25 patients. They recorded their reasons for ordering the tests (to exclude or confirm disease or to reassure patients) and their pretest disease probability estimates. Upon receiving the results they recorded how they interpreted the tests, their posttest probability estimates and further management. Logistic regression was used to analyse whether the pretest probability and the reasons for ordering tests influenced the interpretation, the posttest probability estimates and the decisions on further management. Results: The physicians ordered tests for diagnostic purposes for 1253 patients; 742 patients had an abnormal result (64%). Physicians’ pretest probability estimates and their reasons for ordering diagnostic tests influenced test interpretation, posttest probability estimates and further management. Abnormal results of tests ordered for reasons of reassurance were significantly more likely to be interpreted as normal (65.8%) compared to tests ordered to confirm a diagnosis or exclude a disease (27.7% and 50.9%, respectively). The odds for abnormal results to be interpreted as normal were much lower when the physician estimated a high pretest disease probability, compared to a low pretest probability estimate (OR = 0.18, 95% CI = 0.07-0.52, p < 0.001). Conclusions: Interpretation and management of abnormal test results were strongly influenced by physicians’ estimation of pretest disease probability and by the reason for ordering the test. By relating abnormal laboratory results to their pretest expectations, physicians may seek a balance between over- and under-reacting to laboratory test results. Background Laboratory tests are frequently ordered in routine primary care as part of the diagnostic process, even though the physician’s pretest expectation may often be that the probability of disease is low, and they often order tests for other than purely medical reasons, such as patient

Background To perform out-of-hours primary care, Dutch general practitioners (GPs) have organised themselves in large-scale GP cooperatives. Roughly, two models of out-of-hours care can be distinguished; GP cooperatives working separate from the hospital emergency department (ED) and GP cooperatives integrated with the hospital ED. Research has shown differences in care utilisation between these two models; a significant shift in the integrated model from utilisation of ED care to primary care. These differences may have implications on costs, however, until now this has not been investigated. This study was performed to provide insight in costs of these two different models of out-of-hours care. Methods Annual reports of two GP cooperatives (one separate from and one integrated with a hospital emergency department) in 2003 were analysed on costs and use of out-of-hours care. Costs were calculated per capita. Comparisons were made between the two cooperatives. In addition, a comparison was made between the costs of the hospital ED of the integrated model before and after the set up of the GP cooperative were analysed. Results Costs per capita of the GP cooperative in the integrated model were slightly higher than in the separate model (ε 11.47 and ε 10.54 respectively). Differences were mainly caused by personnel and other costs, including transportation, interest, cleaning, computers and overhead. Despite a significant reduction in patients utilising ED care as a result of the introduction of the GP cooperative integrated within the ED, the costs of the ED remained the same. Conclusion The study results show that the costs of primary care appear to be more dependent on the size of the population the cooperative covers than on the way the GP cooperative is organised, i.e. separated versus integrated. In addition, despite the substantial reduction of patients, locating the GP cooperative at the same site as the ED was found to have little effect on costs of the ED. Sharing more facilities and personnel between the ED and the GP cooperative may improve cost-efficiency.