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IntroductionIn the background of aging in place, home and community-based services (HCBS) have been playing an increasingly important role in long-term care (LTC) security systems. However, it is still uncertain whether and how HCBS use affects hospital utilization and the corresponding expenditures.MethodsUsing data from the China Health and Retirement Longitudinal Survey (CHARLS) and the China City Statistical Yearbook, the instrumental variable (IV) approach is applied to identify the causal effects of HCBS use on hospital utilization and hospital expenditure among disabled elders.ResultsWe find that HCBS use significantly reduces the probability of being hospitalized, the times of hospitalization, and the length of inpatient stay, as well as the total, out-of-pocket and reimbursement inpatient expenditures, demonstrating not only the substitution impact of HCBS for hospital care but also the effectiveness of medical expenditure control in LTC security systems. Heterogeneity analysis shows that the impacts of HCBS use on hospital utilization and hospital expenditure concentrate on disabled elders who are younger, male, living in urban areas, or from higher-income households; both healthcare and spiritual consolation services have significant negative effects, while the anticipated effects of daily care service use are not supported. The possible mechanisms are the substitution of HCBS for hospital care and the improvements in both the physical and psychological health of disabled elders. However, the mechanism of adverse events decrease is not verified, which needs to be investigated further with more proxy variables.ConclusionThis study provides empirical evidence that HCBS use can not only reduce hospital utilization and hospital expenditure among disabled elders but also improve their physical and psychological health. Policy designs should emphasize the orientation of HCBS, ensure the fundamental and central position of HCBS in the formal care service system, pay more attention to the accessibility and affordability of HCBS for fragile groups, and diversify and optimize the development of the health service and the spiritual consolation service.

BackgroundIn 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). MethodsWe 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. ResultsOur 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.ConclusionThe 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.

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]

This research explores the association of public health expenditure with economic performance across the United States. Healthcare expenditure can result in better provision of health opportunities, which can strengthen human capital and improve the productivity, thereby contributing to economic performance. It is therefore important to assess the phenomenon of healthcare spending in a country. Using visual analytics, we collected economic and health data from the Bureau of Economic Analysis and the Bureau of Labor Statistics for the years 2003–2014. The overall results strongly suggest a positive correlation between healthcare expenditure and the economic indicators of income, GDP, and labor productivity. While healthcare expenditure is negatively associated with multi-factor productivity, it is positively associated with the indicators of labor productivity, personal spending, and GDP. The study shows that an increase in healthcare expenditure has a positive relationship with economic performance. There are also variations across states that justify further research. Building on this and prior research, policy implications include that the good health of citizens indeed results in overall better economy. Therefore, investing carefully in various healthcare aspects would boost income, GDP, and productivity, and alleviate poverty. In light of these potential benefits, universal access to healthcare is something that warrants further research. Also, research can be done in countries with single-payer systems to see if a link to productivity exists there. The results support arguments against our current healthcare system's structure in a limited way.

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]

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]

Introduction: Preventable hospitalizations are responsible for increasing the cost of health care and reflect ineffectiveness of the health services in the primary care setting. The objective of this study was to assess expenditure for hospitalizations and utilize expenditure differentials to determine factors associated with ambulatory care - sensitive conditions (ACSCs) hospitalizations., Methods: A cross-sectional study of hospitalizations among Medicaid enrollees in comprehensive managed care plans in 2009 was conducted. A total of 25 581 patients were included in the analysis. Expenditures on hospitalizations were examined at the 50th, 75th, 90th, and 95th expenditure percentiles both at the bivariate level and in the logistic regression model to determine the impact of differing expenditure on ACSC hospitalizations., Results: Compared with patients without ACSC admissions, a larger proportion of patients with ACSC hospitalizations required advanced treatment or died on admission. Overall mean expenditures were higher for the ACSC group than for non-ACSC group (US$18 070 vs US$14 452). Whites and blacks had higher expenditures for ACSC hospitalization than Hispanics at all expenditure percentiles. Patient's age remained a consistent predictor of ACSC hospitalization across all expenditure percentiles. Patients with ACSC were less likely to have a procedure on admission; however, the likelihood decreased as expenditure percentiles increased. At the median expenditure, blacks and Hispanics were more likely than other race/ethnic groups to have ACSC hospitalizations (odds ratio [OR]: 1.307, 95% confidence interval [CI]: 1.013-1.686 and OR 1.252, 95% CI: 1.060-1.479, respectively)., Conclusion: Future review of delivery and monitoring of services at the primary care setting should include managed care plans in order to enhance access and overall quality of care for optimal utilization of the resources., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.