Assessing the excess costs of the in-hospital adverse events covered by the AHRQ's Patient Safety Indicators in Switzerland.

There currently exists no comprehensive and up-to date overview on the financial impact of the different adverse events covered by the Patient Safety Indicators (PSIs) from the Agency for Healthcare Research and Quality. We conducted a retrospective case-control study using propensity score matching on a national administrative data set of 1 million inpatients in Switzerland to compare excess costs associated with 16 different adverse events both individually and on a nationally aggregated level. After matching 8,986 cases with adverse events across the investigated PSIs to 26,931 controls, we used regression analyses to determine the excess costs associated with the adverse events and to control for other cost-related influences. The average excess costs associated with the PSI-related adverse events ranged from CHF 1,211 (PSI 18, obstetric trauma with instrument) to CHF 137,967 (PSI 10, postoperative acute kidney injuries) with an average of CHF 27,409 across all PSIs. In addition, adverse events were associated with 7.8-day longer stays, 2.5 times more early readmissions (within 18 days), and 4.1 times higher mortality rates on average. At a national level, the PSIs were associated with CHF 347 million higher inpatient costs in 2019, which corresponds to about 2.2% of the annual inpatient costs in Switzerland. By comparing the excess costs of different PSIs on a nationally aggregated level, we offer a financial perspective on the implications of in-hospital adverse events and provide recommendations for policymakers regarding specific investments in patient safety to reduce costs and suffering.

Patient-reported experience is associated with higher future revenue and lower costs of hospitals.

BACKGROUND: Despite the established positive association between patient experience and patient volume, the relationship between patient experience and the financial performance of hospitals has not been studied thoroughly. METHODS: To investigate this relationship, we used longitudinal data from 132 Swiss acute-care hospitals from 2016 to 2019 to examine the associations between patient experience and the proportion of elective patients, revenue, costs, and profits of hospitals. To account for a potential time lag effect, we utilized annual patient experience data and employed multilevel mixed-effects regression modeling to investigate its association with the aforementioned financial performance indicators for the following year. RESULTS: Data for private and public hospitals were analyzed both separately and in combination, to account for the different proportions of elective patients in these types of hospitals. The resulting mixed models, revealed that for each year studied, the previous year's patient experience was positively associated with the current year's proportion of elective patients (ß = 0.09, p = 0.004, all hospitals) and revenue (ß = 1789.83, p = 0.037, private hospitals only), and negatively associated with costs (ß = - 1191.13, p = 0.017, all hospitals); but not significantly associated with future profits (ß = 629.12, p = 0.240, all hospitals). CONCLUSIONS: This analysis showed that better patient experience is associated with a higher proportion of elective patients, greater revenue, and lower costs. Our findings may assist hospital managers and regulators in identifying strategies to increase revenue and reduce costs.

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.