Using exogenous organizational and regional hospital attributes to explain differences in case-mix adjusted hospital costs.

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.

Identification and assessment of a comprehensive set of structural factors associated with hospital costs in Switzerland.

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.

Choice of reserve capacity by hospitals: a problem for prospective payment.

This contribution analyzes the impact of prospective payment on hospital decisions with regard to reserve capacity, using Swiss hospital data covering the years 2004-2009. This data set is unique because it permits distinguishing of institutional characteristics (e.g., ownership status) from the mode of payment as determinants of hospital efficiency, due to the fact that some Swiss cantons introduced prospective payment early while others waited for federal legislation to be enacted in 2012. Since a hospital's choice of reserve capacity depends also on the risk preferences of management while affecting the cost function, heterogeneity is predicted even in the presence of identical technology and factor prices. For estimating hospitals' marginal costs, we employ the flexible representation of risk preferences by Pope and Chavas [Am J Agric Econ 76, 196-204 (1994)]. Production uncertainty is measured as the difference between actual admissions and admissions predicted by an autoregressive moving average model. Its effect on hospital cost is analyzed using a multilevel stochastic cost frontier model with random coefficients reflecting unobserved differences in technology. Public hospitals are found to opt for a higher probability of meeting unexpected demand, as predicted. Their operating cost is 1.1% higher than for private hospitals and even 1.9% higher than for teaching hospitals, creating an incentive to turn away patients or to keep them waiting for treatment.

Does prospective payment increase hospital (in)efficiency? Evidence from the Swiss hospital sector.

Several European countries have followed the USA in introducing prospective payment for hospitals with the expectation of achieving cost efficiency gains. This article examines whether theoretical expectations of cost efficiency gains can be empirically confirmed. In contrast to previous studies, the analysis of hospitals in Switzerland provides a comparison of a retrospective per diem payment system with a prospective global budget and a payment per patient case system. Using a sample of approximately 90 public financed Swiss hospitals during the years 2004-2009 and Bayesian inference of a standard and a random parameter frontier model, cost efficiency gains are found, particularly with payment per patient case. Prospective payment, designed to put hospitals at operating risk, is more effective in terms of cost reduction than the retrospective alternative. However, hospitals are heterogeneous with respect to their production technologies, making a random parameter frontier model the superior specification for Switzerland.