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Background: In recent years the use of time to death (TTD) variables in the modeling of individual health expenditures has been of interest to health economics researchers. The aim of this study was to investigate the effect of age and TTD on hospital inpatient expenditure (HIE). Methods: We used a claims database from Iran Health Insurance Organization of Tehran city that includes considerable proportion of Tehran residents and contains information on insured individuals' HIE. We included HIE of all insured decedents (30 to 90 years old) who died during March 2013 and March 2014 (n = 1018). No sampling was required. According to the decedents' date of death, we extracted their last 24 months HIE. The period of time March 30, 2011 until March 30, 2014 (3 years) was used to guarantee a full 24 months of observations for decedents. A two-part econometric model was employed to investigate the effect of age, TTD, and some demographic variables on probability and conditional amount of individuals' hospital expenditure. Stata software (version 16.0) was used for data processing and analysis. Results: Our results demonstrated that the month-based TTDs especially near months before death of decedents (TTD1 to TTD10) significantly affected both probability and conditional amount of HIE. One month before death incurred more HIE than the rest of the months. A further interesting finding is that after including TTD, age variable as a conditional driver of HIE loses its direct effect on decedents' HIE, but age TTD interaction effect on HIE is still positive and statistically significant. Conclusion: The results confirm that TTD as a proxy of mortality indicator has a considerable effect on decedents' HIE. The age variable has not directly affected decedents' HIE but indirectly and through its interaction with TTD has a statistically significant effect on HIE. In addition to age, policy-makers should consider TTD to make better predictions of future HIE. [ABSTRACT FROM AUTHOR]

Very limited information exists from Italian hospitals about the utilization and expenditure of medical devices. The ratio expenditure/DRG reimbursement (expressed as a percentage) is a useful parameter, particularly when it is separately calculated for each individual Azienda and, within each Azienda, according to the medical discipline. To generate benchmarks in this area, we have made reference to the expenditure incurred for medical devices from January to June 2019 in all hospitals of the Tuscany region. These expenditures divided by medical discipline have been compared with the DRG reimbursements that hospitals receive from our Region for the same disciplines. This benchmark is represented by percent ratio expenditure/reimbursement for each of the eight Aziende and, within each Azienda, according to the medical specialty. These percentages indicate, for each medical discipline, to what extent medical devices typically consume the reimbursement resulting from the DRGs. These benchmarks facilitate the interpretation of values estimated locally. Determinants leading to 'excessive' values (e.g. >100%) of this parameter can include an extensive use of costly devices, a high frequency of non-remunerative procedures, the presence of inappropriate treatments, the need to update specific DRGs, deficiencies in coding expenditures and procedures, and so on. [ABSTRACT FROM AUTHOR]

Some researchers claim that health care expenditures for older people are growing faster than for the rest of the population. This process is referred to as steepening. The aim of this paper is to test steepening, applying new data and revised methods. Furthermore, we explain the connection between the terms red herring hypothesis, i.e., that time to death and not age per se drives the health care expenditures, and steepening. We also present the mechanisms that may induce steepening, as presented in the literature. When testing steepening, we apply data from all inpatient stays in somatic hospitals in Norway in the period 1998-2009, i.e., the data has no self-selection and covers the entire population of Norway (5 million). Our analysis does not reject steepening, with the exception of the 0-year-olds. The results also hold when controlling for mortality-related expenditures. Furthermore, we observe an increase in expenditures for the 0-year-olds. Finally, we find increasing mortality-related expenditures over time. We find the link between steepening and the red herring hypothesis to be vague, and we find steepening and the red herring hypothesis to be independent. [ABSTRACT FROM AUTHOR]

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Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients' age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance. [ABSTRACT FROM AUTHOR]