Comparing hospital costs: What is gained by accounting for more than a case-mix index?

Efficiency and Resource Allocation in Government Hospitals in Saudi Arabi: A Casemix Index Approach

In Saudi Arabia, the evaluation of healthcare institutions' performance and efficiency is gaining prominence to ensure effective resource utilization. This study aims to assess the efficiency of government hospitals in Saudi Arabia using the case mix index (CMI) approach. Comprehensive data from 67 MoH hospitals were collected and analyzed. The CMI was calculated by assigning weights to different patient groups based on case complexity and resource requirements, facilitating comparisons of hospital performance in terms of resource utilization and patient outcomes. The findings reveal variations in the CMI across hospitals in relation to size and type. The average CMI was 1.26, with the highest recorded at 1.67 and the lowest at 1.02. Medical cities demonstrated the highest CMI (1.47), followed by specialized hospitals (1.32), and general hospitals (1.21). The study highlights opportunities for enhancing productivity and efficiency, particularly in hospitals with lower CMI, by benchmarking against peer institutions with similar capacities and patient case mix. These findings have significant implications for hospital operations and resource allocation policies, supporting ongoing efforts to improve the efficiency of government hospitals in Saudi Arabia. By incorporating these insights into healthcare strategies, policymakers can work towards enhancing the overall performance and effectiveness of the healthcare system.

Adding a Social Risk Adjustment Into the Estimation of Efficiency: The Case of Chilean Hospitals

BACKGROUND AND OBJECTIVES

There is much interest in adding social variables to hospital performance assessments. Many of the existing analyses, however, already include patients' diagnosis data, and it is not clear that adding a social adjustment variable would improve the quality of the results: the growing literature on this issue provides mixed results. The purpose in this study was to add evidence from a developing country into this discussion.

METHODS

We estimate the efficiency of hospitals controlling for casemix, with and without adjusting the hospital's casemix for the patients' sociodemographic variables. The magnitude of the adjustment is based on the observed impact of age, sex, and income on length of stay, conditional on the diagnosis related group (DRG). We use a data envelopment analysis (DEA) to assess the efficiency of 50 Chilean hospitals' discharges, including 780 DRGs and covering about 60% of total discharges in Chile from 2013 to 2015.

RESULTS

We found that the sociodemographic adjustment introduces very small changes in the DEA estimation of efficiency. The underlying reason is the relatively low influence of sociodemographics on hospital costs, conditional on DRG, and the changing pattern of sociodemographics across DRGs for any given hospital.

CONCLUSION

We conclude that the casemix-adjusted estimation of hospital efficiency is robust to the heterogeneity of patients' sociodemographic heterogeneity across hospitals. These results confirm, in a developing country, what has been observed in developed countries. For management purposes, then, the processing costs of adding social variables into hospitals' performance assessments might not be justified.

Assessing physician productivity following Norwegian hospital reform: A panel and data envelopment analysis

BACKGROUND

Although health care reforms may improve efficiency at the macro level, less is known regarding their effects on the utilization of health care personnel. Following the 2002 Norwegian hospital reform, we studied the productivity of the physician workforce and the effect of personnel mix on this measure in all nineteen Norwegian hospitals from 2001 to 2013.

METHODS

We used panel analysis and non-parametric data envelopment analysis (DEA) to study physician productivity defined as patient treatments per full-time equivalent (FTE) physician. Resource variables were FTE and salary costs of physicians, nurses, secretaries, and other personnel. Patient metrics were number of patients treated by hospitalization, daycare, and outpatient treatments, as well as corresponding diagnosis-related group (DRG) scores accounting for differences in patient mix. Research publications and the fraction of residents/FTE physicians were used as proxies for research and physician training.

RESULTS

The number of patients treated increased by 47% and the DRG scores by 35%, but there were no significant increases in any of the activity measures per FTE physician. Total DRG per FTE physician declined by 6% (p < 0.05). In the panel analysis, more nurses and secretaries per FTE physician correlated positively with physician productivity, whereas physician salary was neutral. In 2013, there was a 12%-80% difference between the hospitals with the highest and lowest physician productivity in the differing treatment modalities. In the DEA, cost efficiency did not change in the study period, but allocative efficiency decreased significantly. Bootstrapped estimates indicated that the use of physicians was too high and the use of auxiliary nurses and secretaries was too low.

CONCLUSIONS

Our measures of physician productivity declined from 2001 to 2013. More support staff was a significant variable for predicting physician productivity. Personnel mix developments in the study period were unfavorable with respect to physician productivity.

Comparison between a multi-pavilion hospital and a single pavilion hospital

Background and aim

Defining and measuring hospital efficiency is a hard task, in spite of the agreement that hospitals need to be efficient. Thus, while research might focus on the relationship between costs and outcomes, measurements differ significantly across studies.

The aim of the present study is to compare a multi-pavilion hospital with a single hospital from Cluj-Napoca, Romania.

Methods

Statistical and financial (effective expenses, salaries, drugs, materials, reagents, food) indicators were used to compare two hospitals from Cluj-Napoca: the Adults’ Clinical Hospital in Cluj-Napoca, and the Rehabilitation Hospital from Cluj-Napoca respectively. Data related to these indicators were collected at each hospital level, between 2004 and 2010.

Results

When investigating the expenses on medicine, data showed the two hospitals had similar values in 2004, 13.09% and 14.43% for the multi-pavilion hospital and single hospital, respectively. After 2004, the expenses started to drop simultaneously, being around 11% in 2006 and 2007 for both hospitals. The mortality rate was significantly different for the two hospitals. The multi-pavilion had a much higher mortality rate, when compared to the single hospital. From 2004 until 2007 a steady increase was observed for the multi-pavilion hospital, from 1.09 to 2.57 respectively.

Conclusion

The significant differences found between the two hospitals look being unavoidable, as long as they seem to stem from the hospitals’ ownership, their addressability and their targeted diseases and associated procedures.

Cost-Efficiency Analysis of a Multi-Pavilion Hospital in Cluj County

Background and aim

Multi-hospital health systems have become the most popular administrative structure in healthcare, leading to both opportunities and challenges for hospital administrators. In government-funded healthcare systems, there is a balance between costs and the provision of health services.

The aim of the present study is to assess the efficiency in terms of costs of a multi-pavilion hospital from Cluj County, Romania.

Methods

The institution analyzed in this article is the Adults’ Clinical Hospital in Cluj-Napoca. A descriptive retrospective study collected data from January 2004 to December 2010. A set of indicators were compiled, divided into three main categories: personnel, statistics, and financial.

Results

Twenty-one financial indicators were investigated. Heterogeneity between different years was observed for the continuous hospitalization indicator and the wage budget indicator. The highest variability was observed between the budget and expenses indicators, while a smaller variability was observed at the average costs per patient. The costs per patient have increased at all pavilions in the studied time frame, the higher costs being at the Internal Medicine and Surgery pavilions: 10,203 RON in 2010 (1 euro ~ 4.4 RON)

Conclusion

The pavilions included in the Adults’ Clinical Hospital Cluj-Napoca have different expenses patterns, as each pavilion is focused on different specialties. Each pavilion serves different target groups, requiring different procedures. This in turn results in different expense patterns across each pavilion.

Costs and Quality at the Hospital Level in the Nordic Countries

This article develops and analyzes patient register-based measures of quality for the major Nordic countries. Previous studies show that Finnish hospitals have significantly higher average productivity than hospitals in Sweden, Denmark, and Norway and also a substantial variation within each country. This paper examines whether quality differences can form part of the explanation and attempts to uncover quality-cost trade-offs. Data on costs and discharges in each diagnosis-related group for 160 acute hospitals in 2008-2009 were collected. Patient register-based measures of quality such as readmissions, mortality (in hospital or outside), and patient safety indices were developed and case-mix adjusted. Productivity is estimated using bootstrapped data envelopment analysis. Results indicate that case-mix adjustment is important, and there are significant differences in the case-mix adjusted performance measures as well as in productivity both at the national and hospital levels. For most quality indicators, the performance measures reveal room for improvement. There is a weak but statistical significant trade-off between productivity and inpatient readmissions within 30 days but a tendency that hospitals with high 30-day mortality also have higher costs. Hence, no clear cost-quality trade-off pattern was discovered. Patient registers can be used and developed to improve future quality and cost comparisons.

The development of differences in hospital costs accross income groups in Finland from 1998 to 2010

OBJECTIVE

To quantify differences in hospital costs between socioeconomic groups and the development over time.

METHODS

Register data on somatic specialised hospital admissions for patients aged between 25 and 84 in Finland in 1998-2010 were used with income data individually linked to them. The cost of an admission was calculated by multiplying the number of a patient's inpatient days by the inpatient day cost of the patient's DRG. We calculated age-standardised admission costs per resident and per user as well as costs per inpatient day and concentration indices separately for men and women.

RESULTS

Hospital admission costs reduced with increasing income. The difference between the extreme income quintiles was more than 50% throughout the study period, and this difference widened. However, the cost per inpatient day was more than 20% higher in the highest income group. The differences between income groups were the most prominent in disease categories involving surgery.

CONCLUSIONS

The growth between socioeconomic groups in hospital costs is presumably mainly due to increasing differences in morbidity. More attention needs to be paid to prevention of health inequalities and access to and content of primary care among low-income groups in order to decrease the need for hospitalisations.

Explaining length of stay variation of episodes of care in the Netherlands

OBJECTIVES

Diagnosis Related Group (DRG) systems aim to classify patients into mutually exclusive groups of patients, with the patients in each group having the same expected length of stay (LOS). We examined the ability of current classification variables to explain LOS variation between DRG-like Diagnosis Treatment Combination (DBC)s for ten episodes of care in the Netherlands, including breast cancer, stroke and inguinal hernia repair. Additionally, we assessed the predictive ability of some other classification variables.

METHODS

For each episode of care, the relevant DBC codes of all hospitalizations in 2008 were identified and all available determinants that may serve as classification variables were acquired from the national database. Ordinary least squares regression was used to examine the predictive ability of these classification variables.

RESULTS

The current classification variables are not sufficiently distinct to classify patients into mutually exclusive groups of patients. ICU admissions and hospital type may serve as valuable classification variables. Additionally, episode-specific variables may improve the Dutch grouping algorithm.

CONCLUSIONS

Although it may not be feasible in the short term, grouping algorithms would benefit greatly from the introduction of classification variables tailored to the needs of specific episodes of care. A first step would be to focus on 'general' classification variables meaningful for specific episodes of care.

Treatment costs of acute myocardial infarction in the Netherlands

BACKGROUND

This study aimed to calculate the treatment costs of acute myocardial infarction (AMI) in the Netherlands for 2012. Also, the degree of association between treatment costs of AMI and some patient and hospital characteristics was examined.

METHODS

For this retrospective cost analysis, patients were drawn from the database of the Diagnosis Treatment Combination (Diagnose Behandeling Combinatie, DBC) casemix system, which contains data on the resource use of all hospitalisations in the Netherlands. All costs were based on Euro 2012 cost data.

RESULTS

The analysis was based on data of 25,657 patients. Mean treatment costs were estimated at <euro> 5021, with significant cost increases for patients with percutaneous coronary intervention (PCI) treatment. ST-segment elevation myocardial infarction (STEMI) patients receiving thrombolysis incurred the lowest (<euro> 4286), while non-STEMI patients receiving PCI the highest costs (<euro> 6060). Length of stay and hospital type were strong predictors of treatment costs.

CONCLUSIONS

This study is the most extensive cost assessment of the treatment costs of AMI in the Netherlands thus far. Our results may be used as input for health-economic models and economic evaluations to support the decision making of registration, reimbursement and pricing of interventions in healthcare.

How well do diagnosis-related group systems group breast cancer surgery patients? Evidence from 10 European countries

We analysed patient-level data (n = 72,235) from 563 hospitals in 10 European countries to assess the ability of national diagnosis-related group (DRG) systems to account for patient-level variation in cost or lengths of stay of breast cancer surgery patients against a standard set of patient characteristics, treatment and quality variables. We find that European DRG systems use very different types of classification variables and numbers of DRGs (range: 3-7) to classify these patients. In 6 of 10 countries, the set of patient characteristics, treatment and quality variables, which we were able to define across countries, perform better than the set of national DRGs in accounting for patient-level variation in resource consumption. Moreover, there appear to be factors that are consistently significant determinants of cost/length of stay of breast cancer surgery cases but are not, or at least not fully, considered in European DRG systems. Our results therefore raise concerns as to whether all systems rely on the most appropriate classification variables. In several countries, policymakers should reevaluate the appropriateness of their DRG algorithm for breast cancer surgery and of specific DRG weights.

Examining cost variation across hospital departments--a two-stage multi-level approach using patient-level data

Studies of hospital efficiency seldom lead to changes in practice, partly because recommendations are unspecific or results are not seen as robust. We describe a method to compare hospital costs that utilises patient-level data. We perform a two-stage analysis in which we first consider factors that explain costs among patients and then across hospital departments. We illustrate our approach by examining the costs and characteristics of almost one million patients admitted to 136 English NHS hospital obstetrics departments in 2005/2006. We identify those departments with significantly higher costs that need to take action.

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

Background

Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.

Methods

The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.

Results

The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R² = 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R² = 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R² = 0.382) performed better than the models adjusted by Rx-MGs (R² = 0.360) or ADGs (R² = 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R² = 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R² = 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.

Conclusions

The medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.