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Introduction: The objectives of this research are (i) to describe the medico-administrative characteristics of inpatients aged 65 and more who are hospitalized for hip joint replacement, (ii) to evaluate the complete hospital cost into costs of medical procedures, drugs costs, prostheses costs, and the administrative costs, and (iii) to identify and to evaluate from administrative database predictors influencing the complete hospital costs. Methods: The study was based on 961 inpatient stays aged 65 and more, with the APR-DRG 301 “Hip joint replacement”. The sample for this study was based on data collected in 2014 among nine Belgian general hospitals. We used the linear regression method for isolating predictors of hospital cost. Results: The study highlights three different types of patients hospitalized for hip replacement, depending on the primary diagnosis: osteoarthritis problems (57%), femur neck fracture (30%), or other reasons (13%) (complications, infections, or problems with the existing hip prosthesis). The median length of stay (P25–P75) was 9 days (6.29–20.91). The median cost (P25–P75) was 8,023.91 EUR (6678.32–13,670.78). The total cost was composed of the direct hospital cost (30%), the cost of medical procedures (31%), cost of drugs (4%), the cost of hip prosthesis (18%), and other costs (17%). The linear regression reveals that an extreme SOI or risk of mortality, an ICU stay, an in-hospital death, an index of Charlson comorbidities of 4 or 5, to be hospitalized for a hip replacement because of complications, infections, or problems with the existing hip prosthesis, and the length of stay, were predictors of an increase in hospital cost. Conclusion: The cost is not increasing with the age of the patient, but mainly with the length of stay and the comorbidities linked to the age which are considered in the severity of illness and the Charlson comorbidities index. The hospital cost is higher for patients hospitalized for complications linked to an existing hip prosthesis than for a hip replacement linked to osteoarthritis problems., SCOPUS: ar.j, info:eu-repo/semantics/published

The objective of this study was to estimate the total hospital cost per patient admitted through the emergency department with a primary diagnosis of pulmonary embolism (PE), and to identify the main components and predictors of costs.Actual costs of care of 652 consecutive patients hospitalised in 10 general hospitals in Belgium, including 31 outlier patients in terms of length of stay (4.8%), were obtained by aggregating all cost components contributing to care of each patient.In both inlier and outlier patients, the mean total cost per patient increased linearly with the degree of severity of illness classes related to the All Patient Refined Diagnosis Related Group (pAn increasing burden of comorbid illness was strongly associated with increasing actual cost for caring hospitalised patients for PE. Increasing age was associated with an increase in all main cost components.

The objective of this study was to compare costs data by diagnosis related group (DRG) between Belgium and Switzerland. Our hypotheses were that differences between countries can probably be explained by methodological differences in cost calculations, by differences in medical practices and by differences in cost structures within the two countries.Classifications of DRG used in the two countries differ (AP-DRGs version 1.7 in Switzerland and APR-DRGs version 15.0 in Belgium). The first step of this study was to transform Belgian summaries into Swiss AP-DRGs. Belgian and Swiss data were calculated with a clinical costing methodology (full costing). Belgian and Swiss costs were converted into US$ PPP (purchasing power parity) in order to neutralize differences in purchasing power between countries.The results of this study showed higher costs in Switzerland despite standardization of cost data according to PPP. The difference is not explained by the case-mix index because this was similar for inliers between the two countries. The length of stay (LOS) was also quite similar for inliers between the two countries. The case-mix index was, however, higher for high outliers in Belgium, as reflected in a higher LOS for these patients. Higher costs in Switzerland are thus probably explained mainly by the higher number of agency staff by service in this country or because of differences in medical practices.It is possible to make international comparisons but only if there is standardization of the case-mix between countries and only if comparable accountancy methodologies are used. Harmonization of DRGs groups, nomenclature and accountancy is thus required.

Objectives The objectives of this study are to analyze the variation of the surgical time and of disposable costs per surgical procedure and to analyze the association between disposable costs and the surgical time. Methods The registration of data was done in an operating room of a 419 bed general hospital, over a period of three months (n = 1556 surgical procedures). Disposable material per procedure used was recorded through a barcode scanning method. Results The average cost (standard deviation) of disposable material is €183.66 (€183.44). The mean surgical time (standard deviation) is 96 min (63). Results have shown that the homogeneity of operating time and DM costs was quite good per surgical procedure. The correlation between the surgical time and DM costs is not high (r = 0.65). Conclusions In a context of Diagnosis Related Group (DRG) based hospital payment, it is important that costs information systems are able to precisely calculate costs per case. Our results show that the correlation between surgical time and costs of disposable materials is not good. Therefore, empirical data or itemized lists should be used instead of surgical time as a cost driver for the allocation of costs of disposable materials to patients.

In 2010, a Belgian study [1] explored the feasibility of introducing all-inclusive case-based payments for Belgian hospitals. In this kind of financing system, hospital services and patient mix are described in a simplified way through Diagnosis Related Groups (DRGs). A tariff is fixed in advance for each DRG. DRGs are groups of patients based on economic and clinical homogeneity. Clinical homogeneity is achieved on the basis of medical diagnosis, co-morbidities, medical procedures, complications, etc. Economic homogeneity is achieved by using, first of all, the length of stay (LOS) or cost (or charges) of hospitalization as a classification criterion. As soon as DRGs were introduced, most nursing research revealed that DRGs were not very amenable to homogeneous integration with nursing care. DRGs only explained 20% to 40% of the variability in nursing care. Coefficients of variation for nursing care per DRG have been reported as varying from 0,22 to 2,56 [2-5]. This is the reason why some researchers try to refine DRG classification into classes of nursing cost per DRG [6]. However, it is difficult to find recent data that deals with this. The objectives of this study are to: • Discover if nursing activity is homogeneous by DRG and severity of illness. • Evaluate the correlation between LOS of patients and nursing activity per patient.

This study examined the impact of cost outliers in term of hospital resources consumption, the financial impact of the outliers under the Belgium casemix-based system, and the validity of two "proxies" for costs: length of stay and charges. The cost of all hospital stays at three Belgian general hospitals were calculated for the year 2001. High resource use outliers were selected according to the following rule: 75th percentile +1.5 xinter-quartile range. The frequency of cost outliers varied from 7% to 8% across hospitals. Explanatory factors were: major or extreme severity of illness, longer length of stay, and intensive care unit stay. Cost outliers account for 22-30% of hospital costs. One-third of length-of-stay outliers are not cost outliers, and nearly one-quarter of charges outliers are not cost outliers. The current funding system in Belgium does not penalize hospitals having a high percentage of outliers. The billing generated by these patients largely compensates for costs generated. Length of stay and charges are not a good approximation to select cost outliers.