BACKGROUND: Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system.
METHODS: Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity.
RESULTS: The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011-5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system.
CONCLUSIONS: The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

BACKGROUND: The coronavirus 2019 (COVID-19) pandemic led to major disruptions in surgical activity, particularly in the first year (2020). The objective of this study was to assess the impact of surgical reorganization on surgical outcomes in Northern Italy in 2020 and 2021.
METHODS: A retrospective cohort study was conducted among 30 hospitals participating in the surveillance system for surgical site infections (SSIs). Abdominal surgery procedures performed between 2018 and 2021 were considered. Predicted SSI rates for 2020 and 2021 were estimated based on 2018-2019 data and compared with observed rates. Independent predictors for SSI were investigated using logistic regression, including procedure year.
RESULTS: 7605 procedures were included. Significant differences in case-mix were found comparing the three time periods. Observed SSI rates among all patients in 2020 were significantly lower than expected based on 2018-2019 SSI rates (p 0.0465). Patients undergoing procedures other than cancer surgery in 2020 had significantly lower odds for SSI (odds ratio, OR 0.52, 95 % confidence interval, CI 0.3-0.89, p 0.018) and patients undergoing surgery in 2021 had significantly higher odds for SSI (OR 1.49, 95 % CI 1.07-2.09, p 0.019) compared to 2018-2019.
CONCLUSIONS: Enhanced infection prevention and control (IPC) measures could explain the reduced SSI risk during the first pandemic year. IPC practices should continue to be reinforced beyond the pandemic context.

This study aims to assess time trends in case-mix and to evaluate the risk of revision and causes following primary THA, TKA, and UKA in private and public hospitals in the Netherlands.
We retrospectively analyzed 476,312 primary arthroplasties (public: n = 413,560 and private n = 62,752) implanted between 2014 and 2023 using Dutch Arthroplasty Register data. We explored patient demographics, procedure details, trends over time, and revisions per hospital type. Adjusted revision risk was calculated for comparable subgroups (ASA I/II, age ≤ 75, BMI ≤ 30, osteoarthritis diagnosis, and moderate-high socioeconomic status (SES).
The volume of THAs and TKAs in private hospitals increased from 4% and 9% in 2014, to 18% and 21% in 2022. Patients in private hospitals were younger, had lower ASA classification, lower BMI, and higher SES compared with public hospital patients. In private hospitals, age and ASA II proportion increased over time. Multivariable Cox regression demonstrated a lower revision risk for primary THA (HR 0.7, CI 0.7-0.8), TKA (HR 0.8, CI 0.7-0.9), and UKA (HR 0.8, CI 0.7-0.9) in private hospitals. After initial arthroplasty in private hospitals, 49% of THA and 37% of TKA revisions were performed in public hospitals.
Patients in private hospitals were younger, had lower ASA classification, lower BMI, and higher SES com-pared with public hospital patients. The number of arthroplasties increased in private hospitals, with a lower revision risk compared with public hospitals.

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Diagnosis-related groups (DRG) based hospital payment systems are gradually becoming the main mechanism for reimbursement of acute inpatient care. We reviewed the existing literature to ascertain the global use of DRG-based hospital payment systems, compared the similarities and differences of original DRG versions in ten countries, and used ischemic stroke as an example to ascertain the design and implementation strategies for various DRG systems. The current challenges with and direction for the development of DRG-based hospital payment systems are also analyzed. We found that the DRG systems vary greatly in countries in terms of their purpose, grouping, coding, and payment mechanisms although based on the same classification concept and that they have tended to develop differently in countries with different income classifications. In high-income countries, DRG-based hospital payment systems have gradually begun to weaken as a mainstream payment method, while in middle-income countries DRG-based hospital payment systems have attracted increasing attention and increased use. The example of ischemic stroke provides suggestions for mutual promotion of DRG-based hospital payment systems and disease management. How to determine the level of DRG payment incentives and improve system flexibility, balance payment goals and disease management goals, and integrate development with other payment methods are areas for future research on DRG-based hospital payment systems.

In analyzing direct hospitalization cost and clinical data from an academic medical center, commonly used metrics such as diagnosis-related group (DRG) weight explain approximately 37% of cost variability, but a substantial amount of variation remains unaccounted for by case mix index (CMI) alone. Using CMI as a benchmark, we isolate and target individual DRGs with higher than expected average costs for specific quality improvement efforts. While DRGs summarize hospitalization care after discharge, a predictive model using only information known before admission explained up to 60% of cost variability for two DRGs with a high excess cost burden. This level of variability likely reflects underlying patient factors that are not modifiable (e.g., age and prior comorbidities) and therefore less useful for health systems to target for intervention. However, the remaining unexplained variation can be inspected in further studies to discover operational factors that health systems can target to improve quality and value for their patients. Since DRG weights represent the expected resource consumption for a specific hospitalization type relative to the average hospitalization, the data-driven approach we demonstrate can be utilized by any health institution to quantify excess costs and potential savings among DRGs.

BACKGROUND: Transitional hospital-to-home care programs support safe and timely transition from acute care settings back into the community. Case-mix systems that classify transitional care clients into groups based on their resource utilization can assist with care planning, calculating reimbursement rates in bundled care funding models, and predicting health human resource needs. This study evaluated the fit and relevance of the Resource Utilization Groups version III for Home Care (RUG-III/HC) case-mix classification system in transitional care programs in Ontario, Canada.
METHODS: We conducted a retrospective analysis of clinical assessment data and administrative billing records from a cohort of clients (n = 1,680 care episodes) in transitional home care programs in Ontario. We classified care episodes into established RUG-III/HC groups based on clients\' clinical and functional characteristics and calculated four case-mix indices to describe care relative resource utilization in the study sample. Using these indices in linear regression models, we evaluated the degree to which the RUG-III/HC system can be used to predict care resource utilization.
RESULTS: A majority of transitional home care clients are classified as being Clinically complex (41.6%) and having Reduced physical functions (37.8%). The RUG-III/HC groups that account for the largest share of clients are those with the lowest hierarchical ranking, indicating low Activities of Daily Living limitations but a range of Instrumental Activities of Daily Living limitations. There is notable heterogeneity in the distribution of clients in RUG-III/HC groups across transitional care programs. The case-mix indices reflect decreasing hierarchical resource use within but not across RUG-III/HC categories. The RUG-III/HC predicts 23.34% of the variance in resource utilization of combined paid and unpaid care time.
CONCLUSIONS: The distribution of clients across RUG-III/HC groups in transitional home care programs is remarkably different from clients in long-stay home care settings. Transitional care programs have a higher proportion of Clinically complex clients and a lower proportion of clients with Reduced physical function. This study contributes to the development of a case-mix system for clients in transitional home care programs which can be used by care managers to inform planning, costing, and resource allocation in these programs.

OBJECTIVE: Lack of professional and accurate diagnosis of malnutrition led to a reduction in Diagnosis Related Group (DRG) payment and a decrease in Case-Mix Index (CMI). The aim of this study was to explore the effects of adding a proper nutritional diagnosis and modifying complication groups on DRG payment and CMI.
METHODS: Retrospective analysis was performed on patients ad-mitted to the hospital from January to June 2022 who had received a nutritional assessment. Patients were diagnosed as well-nourished, mild malnutrition, moderate malnutrition or severe malnutrition according to patient-generated subjective global assessment (PG-SGA) scores within 24 hours of admission. CMI and DRG hospital internal control standards were recalculated and compared with the original values.
RESULTS: A total of 254 patients were enrolled, including 40 patients with mild malnutrition, 74 patients with moderate malnutrition and 122 patients with severe malnutrition. Of all subjects, 111 changed complication groups. The median of the DRG hospital internal control standard (12006.09 vs. 13797.19, p=0.01) and the median of CMI (0.91 vs. 1.04, p=0.026) were significantly higher than those before the diagnostic change. In patients with inflammatory bowel disease (IBD), the CMI value, hospital control standard of DRG, and the classification of DRG were significantly different from those before diagnosis revision (p<0.001).
CONCLUSIONS: Fully identification and correct coding of malnutrition cases are conducive for hospitals to receive appropriate DRG compensation, and further contribute to the improvement of medical quality and the economic sustain-ability of hospitals.

Hospital activity is often measured using diagnosis-related groups, or case mix groups, but this information does not represent important aspects of patients\' health outcomes. This study reports on case mix-based changes in health status of elective (planned) surgery patients in Vancouver, Canada.
We used a prospectively recruited cohort of consecutive patients scheduled for planned inpatient or outpatient surgery in six acute care hospitals in Vancouver. All participants completed the EQ-5D(5L) preoperatively and 6 months postoperatively, collected from October 2015 to September 2020 and linked with hospital discharge data. The main outcome was whether patients\' self-reported health status improved among different inpatient and outpatient case mix groups.
The study included 1665 participants with completed EQ-5D(5L) preoperatively and postoperatively, representing a 44.8% participation rate across eight inpatient and outpatient surgical case mix categories. All case mix categories were associated with a statistically significant gain in health status (p < .01 or lower) as measured by the utility value and visual analogue scale score. Foot and ankle surgery patients had the lowest preoperative health status (mean utility value: 0.6103), while bariatric surgery patients reported the largest improvements in health status (mean gain in utility value: 0.1515).
This study provides evidence that it was feasible to compare patient-reported outcomes across case mix categories of surgical patients in a consistent manner across a system of hospitals in one province in Canada. Reporting changes in health status of operative case mix categories identifies characteristics of patients more likely to experience significant gains in health.