Relationship between hospital structural level and length of stay outliers: Implications for hospital payment systems
Introduction
One of the main features of patient classification systems based on case-mix techniques (for instance diagnosis related groups (DRGs)) is that, by eliminating values too far from the core of the patient group, they exclude cases of extreme resource use (outliers) and pay these cases separately. Outliers can amount to 4.8% of total hospital discharges, 15.1% of total hospital stays and 17.9% of total cost [1]. There are two reasons for treating outliers separately from other discharges. The first reason is to prevent overvaluation of the final mean value (masking effect) due to the exaggeratedly high cost of a small number of patients. Patient cost distribution is heavily skewed to the right and consequently, as several authors have pointed out, cost function distribution is log normal [2]. The second reason is to [3], [4] attempt to reduce the financial risk they represent to providers by paying outliers with an extra payment [5], [6], [7].
The statistical relationship associated with each DRG suggests that the factors leading to intra-group variability could also lead to the existence of outliers. Authors such as Calore and Lezzoni [8], Thomas and Ashcraft [9] and Söderlund et al. [10] have described some of the limitations of DRGs to explain cost and length of stay (LOS) variation and have analyzed various severity measurement indicators to improve their explanatory capacity. These authors report that DRGs fail to explain 83, 94.3, and 80.9% of cost variation in untrimmed data and 70, 93.1, and 76.5% of that in trimmed data, respectively. These results indicate that a high proportion of resource use variation remains unexplained; hospital structural complexity has been put forward as one of the factors that might explain variation in patient resource use [11], [12].
Differences in structural complexity among hospitals mainly arise because hospitals within a national health system with universal coverage are distributed throughout a territory according to criteria aiming to provide equity of access and to maximize specialization. Within a hospital network, small community hospitals tend to be close to the population they serve. Patients requiring more specialized treatment than that provided by small community hospitals are referred to medium-sized teaching and community hospitals. Finally, large urban teaching hospitals centralize specialties and/or facilities with advanced technology and high economic cost and are used to cover the needs of the entire health care system. These characteristics produce differences in cost among the three types of hospitals. The teaching and research activity of large urban teaching hospitals imply greater structural complexity than that found in small community hospitals. Because these centers are highly specialized, structural costs (i.e. fixed costs associated with the hospital structure) tend to be higher and facilities more expensive than those of other hospitals.
The possible relationship between the percentage of outliers and hospital structural level would have clear implications for hospital financing needs. The existence of this relationship remains to be elucidated. Specifically, the question of whether differences in the potential number of outliers between hospitals in different structural levels are due to differences in the complexity of the case-mix treated in the different hospital levels or whether these differences are also due to the structural levels themselves needs to be addressed. If the presence of outliers is due to hospital structural level, independent of the effect produced by patient characteristics, then a separate outlier payment would not be appropriate. This outlier payment would be inappropriate because the difference in cost per case attributable to the hospital characteristics cannot explain such a large cost difference (of a case) in relation to its DRG cost pattern.
Thus, the aim of this study was to analyze the relationship between the probability of a patient requiring extreme resource use and the structural level of the hospital in which the patient was treated. To answer this question, all the inpatient hospital discharges from the acute public hospitals of the Catalan Health Service were analyzed. The Catalan health service uses a hybrid hospital financing system: 65% of the payment weights the activity through hospital structure according to a statistical clustering method called grade of membership (GoM) [13], while 35% of the payment weights the activity through DRGs [14]. Thus, the system does not explicitly recognize the existence of outliers. However, since hospitals with higher structural level receive greater payments and since these hospitals may have more outliers, it could be argued that outliers are incorporated into the system as a structural phenomenon grouped together with many other characteristics.
Section snippets
Methods
A total of 631,096 hospital discharges from the Catalan public hospital system in 1998, registered in the Minimum Data Set defined by the Catalan Health Service, and grouped according to DRGs (Health Care Financing Administration version 13) were analyzed. These discharges were the total hospital activity of the public health sector in Catalonia (Spain), which covers 100% of the population of 6 million inhabitants. The activity carried out by the private sector (which accounts for approximately
Results
Of the 631,096 discharges, 28,234 were identified as outliers, representing 4.5% of the total. Outliers accounted for 20.2% of total days of stay with a mean LOS of 31.4 days, which was five times longer than that of inliers. Outliers accounted for 5.6% of discharges from large urban teaching hospitals, 4.6% of those from medium-sized teaching and community hospitals, and 3.6% of those from small community hospitals (Table 2).
The non-adjusted ORs revealed that structural level directly
Discussion
The results of this study demonstrate that a relationship exists between hospital structural level and LOS outliers and confirm the initial hypothesis that the percentage of outliers in public hospitals increases with hospital structural level. However, the influence of the control variables on the probability of a patient being an outlier also confirms that patient and health care characteristics should also be taken into account when analyzing the causes of differences in the presence of
Acknowledgements
This study was supported by the ‘Fundación de Investigaciones Sanitarias-Instituto de Salud Carlos III-Ministerio de Sanidad y Consumo’, project number 99/0687.
References (19)
- et al.
Medicare’s DRG-Weights in a European environment: the Spanish experience
Health Policy
(2000) - et al.
Insurance aspects of DRG outlier payments
Journal of Health Economics
(1988) - et al.
Cómo pagamos a nuestros hospitales. La referencia de Cataluña y el contrapunto desde Andalucı́a
Gaceta Sanitaria
(2001) - et al.
Risk adjustment: beyond patient’s classification systems
Gaceta Sanitaria
(2001) - Lichtig LK. Hospital information systems for casemix management. New York: Wiley,...
- et al.
The distribution of health care costs and their statistical analysis for economic evaluation
Journal of Health Services Research and Policy
(1998) - et al.
Methods for analyzing health care utilization and costs
Annual Reviews of Public Health
(1999) - Carter GM, Rumpel JD. Payment rates for unusual Medicare hospital cases. Santa Mónica: RAND,...
- et al.
Assenting the FY 1989 change in Medicare PPS outlier policy
Health Care Financing Review
(1992)
Cited by (25)
Reduced length of stay and 30-day readmission rate on an inpatient vascular surgery service
2019, Journal of Vascular NursingDeterminants of the Length of Stay in Stroke Patients
2013, Osong Public Health and Research PerspectivesCitation Excerpt :Undergoing surgery for cerebral infarction and not undergoing surgery for subarachnoid hemorrhage contributed to increased LOS; undergoing surgery for subarachnoid hemorrhage contributed to decreased LOS, but the contributions were not statistically significant. According to a number of non-Korean studies performed based on all pathological types, the larger the number of beds, the longer the LOS [27–30]. According to the studies that connected a higher number of hospital beds with the increased risk for patients [27,28], the severity of disease and LOS were proportional.
Prevention and diagnosis of ventilator-associated pneumonia: A survey on current practices in Southern Spanish ICUs
2005, ChestCitation Excerpt :However, we believe that this study will be useful for many practitioners and health officers in Spain. In summary, similar to other reports, the results of our survey documented a distance between routine and optimal practice that reflects the gap between research and practice.30 Certain interventions may promote behavioral change among health professionals.30