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The demand for medical doctors can be estimated in many ways. The most challenging approach is the model based on population demand and anticipated contextual factors. The eighth national medical education meeting of Thailand in 2009 called for the need to estimate future demand for medical specialists in Thailand to proper plan for postgraduate tr...
Context in source publication
Context 1
... other words, the workloads to estimate the number of internists did not cover diseases that needed multidisciplinary teams such as MDC01 the neurologic diseases and MDC08 the musculoskeletal diseases. Table 2 shows that caseloads at community hospitals were three to four times higher than the loads at regional or general hospitals because more people were living in rural areas and served by community hospitals. Diseases of digestive system were the most prevalent in all hospital levels but the myelo-proliferative neoplasms were mostly seen at teaching hospitals. ...Similar publications
Background
Due to a continuing age shift in the German society hospital providers are concerned about the additional costs associated with the treatment of elderly patients. It is not clear if cardiac catheterization in aged patients leads to higher resource utilization and if DRG-revenues do compensate for this factor. Methods
Procedure-related an...
Citations
... The intensity of seeking medical service and hospitalization, as well as the duration of hospitalization, will directly affect the survivability [30]. These factors have also been used in the literature (e.g., [31][32][33]). Four comorbidities were chosen as variables. ...
... Finally, the frequency of seeking medical service and hospitalization, and the days of hospitalization will affect the expenditure directly [30]. These factors have also been used in Stearns et al. [33], Lin et al. [31], Pannarunothai and Phanthunane [32]. The relationship from hospitalization utilization to inpatient expenditure are constructed. ...
Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R² of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.
Lung cancer is one of the leading causes of mortality, and its medical expenditure has increased dramatically. Estimating the expenditure for this disease has become an urgent concern of the supporting families, medial institutes, and government. In this study, a conditional Gaussian Bayesian network (CGBN) model was developed to incorporate the comprehensive risk factors to estimate the medical expenditure of a lung cancer patient at different stages. A total of 961 patients were surveyed by the four severity stages of lung cancer. The proposed CGBN model identified the correlation and association of 15 risk factors to the medical expenditure of different severity stages of lung cancer patients. The relationships among the demographic, diagnosed-based, and prior-utilization variables are constructed. The model predicted the lung cancer-related medical expenditure with high accuracy of 32.63%, 50.30%, 50.36%, and 66.58%, respectively for stages 1–4, as compared with the reported models. A greedy search was also applied to find the upper threshold of R², while our model also shows that it approached the upper threshold.
