Structural factors can influence hospital costs beyond case-mix differences. However, accepted measures on how to distinguish hospitals with regard to cost-related organizational and regional differences are lacking in Switzerland. Therefore, the objective of this study was to identify and assess a comprehensive set of hospital attributes in relation to average case-mix adjusted costs of hospitals. Using detailed hospital and patient-level data enriched with regional information, we derived a list of 23 cost predictors, examined how they are associated with costs, each other, and with different hospital types, and identified principal components within them. Our results showed that attributes describing size, complexity, and teaching-intensity of hospitals (number of beds, discharges, departments, and rate of residents) were positively related to costs and showed the largest values in university (i.e., academic teaching) and central general hospitals. Attributes related to rarity and financial risk of patient mix (ratio of rare DRGs, ratio of children, and expected loss potential based on DRG mix) were positively associated with costs and showed the largest values in children’s and university hospitals. Attributes characterizing the provision of essential healthcare functions in the service area (ratio of emergency/ ambulance admissions, admissions during weekends/ nights, and admissions from nursing homes) were positively related to costs and showed the largest values in central and regional general hospitals. Regional attributes describing the location of hospitals in large agglomerations (in contrast to smaller agglomerations and rural areas) were positively associated with costs and showed the largest values in university hospitals. Furthermore, the four principal components identified within the hospital attributes fully explained the observed cost variations across different hospital types. These uncovered relationships may serve as a foundation for objectifying discussions about cost-related heterogeneity in Swiss hospitals and support policymakers to include structural characteristics into cost benchmarking and hospital reimbursement.
BackgroundWith DRG payments, hospitals can game the system by ’upcoding’ true patient’s severity of illness. This paper takes into account that upcoding can be performed by the chief physician and hospital management, with the extent of the distortion depending on hospital’s internal decision-making process. The internal decision making can be of the principal-agent type with the management as the principal and the chief physician as the agent, but the chief physicians may be able to engage in negotiations with management resulting in a bargaining solution.ResultsIn case of the principal-agent mechanism, the distortion due to upcoding is shown to accumulate, whereas in the bargaining case it is avoided at the level of the chief physician.ConclusionIn the presence of upcoding it may be appropriate for the sponsor to design a payment system that fosters bargaining to avoid additional distortions even if this requires extra funding.
Diagnosis‐related group (DRG) hospital reimbursement systems differentiate cases into cost‐homogenous groups based on patient characteristics. However, exogenous organizational and regional factors can influence hospital costs beyond case‐mix differences. Therefore, most countries using DRG systems incorporate adjustments for such factors into their reimbursement structure. This study investigates structural hospital attributes that explain differences in average case‐mix adjusted hospital costs in Switzerland. Using rich patient and hospital‐level data containing 4 million cases from 120 hospitals across 3 years, we show that a regression model using only five variables (number of discharges, ratio of emergency/ambulance admissions, rate of DRGs to patients, expected loss potential based on DRG mix, and location in large agglomeration) can explain more than half of the variance in average case‐mix adjusted hospital costs, capture all cost variations across commonly differentiated hospital types (e.g., academic teaching hospitals, children's hospitals, birth centers, etc.), and is robust in cross‐validations across several years (despite differing hospital samples). Based on our findings, we propose a simple practical approach to differentiate legitimate from inefficiency‐related or unexplainable cost differences across hospitals and discuss the potential of such an approach as a transparent way to incorporate structural hospital differences into cost benchmarking and payment schemes.
Objectives To assess domain-specific effects of physical activity (PA) in the relationship with health care utilization and to investigate whether a measure that aggregates PA across domains (leisure, transport, work) is appropriate. Methods Data were retrieved from a longitudinal cohort study conducted in Southern Germany (women n = 1330, men n = 766). The number of physician visits was regressed on total PA and on PA differentiated by the domains leisure time, travel time and working time in a negative binomial model. Results For women, no association with physician visits is found for total PA, while high leisure time physical activity (LTPA) is associated with 22% more visits. The effect of high LTPA is statistically different from the effect of high total PA. For men, no significant associations are found for both measures. Conclusions The specific, positive effect of high LTPA on physician visits among women shows that using an aggregate measure of PA is inappropriate for analyzing the relation between PA and health care utilization. Further, the positive relationship should be considered in attempts to promote physical activity.
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