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Many approaches on the economic aspect of hospital acquired infections (HAIs) have two major limitations: first, the lack of distinction between resources attributable to the management of HAI and resources absorbed by the main clinical problem for which the patient was hospitalized, and second, the lack of an adequate method for calculating the relative costs. These assume that the resources used by HAI can be determined by measuring the extra days of length of days (LOS) of infected patients versus non‐infected patients and attribute to extra‐LOS a value to the mean total cost. The aim of the article is to test a cost‐modelling method that could overcome these limitations by applying the appropriateness evaluation protocol to the medical charts of patients with hospital‐acquired symptomatic urinary tract infection (UTI) or sepsis, and by using cost‐centre accounting.

The paper explains and tests a model for calculating costs of HAIs.

The data analysis showed that it is not always true that infections protract LOS: five out of 25 sepsis cases have extra‐LOS and eight out of 25 UTI cases have extra‐LOS, while the cases of sepsis that arose in surgery ward and intensive care units and urinary tract infections in ICU are without prolongation of LOS. The data analysis also showed that, using the mean total cost, the three cases of sepsis in the general surgery and the six in the ICU did not incur costs, nor did the two cases of UTI in ICU, so that they appear to be infections at zero cost. Moreover, the weight of the cost for the bed, or for the diagnostic services, or for the pharmacological treatment, varied widely depending on the site of the HAI and the ward where the patient was hospitalized.

The method can be applied in any hospital.

The purpose of this paper is to examine empirically how operational performance and contextual factors contribute to differences in overall patient care costs across different hospitals.

Administrative data are employed from a sample of hospitals in New York State to construct measures of contextual factors, operational performance, and cost per patient. Operational performance and cost variables are adjusted to account for case mix differences across hospitals. Hierarchical regression is used to analyze the effects of contextual and operational variables on cost performance.

Increased length of stay, increased patient volume, and educational mission were associated with higher cost per patient. Mortality performance was associated with lower cost per patient. However, it was not found that location, size, or ownership status had a significant relationship with cost performance.

This paper identifies several significant relationships between contextual and operational variables and hospital costs. From a managerial perspective, these findings highlight the fact that some drivers of cost in hospitals are under the control of managers. One of the primary cost drivers in the study is length of stay, which implies that there is significant room for improvement in healthcare performance through a focus on operational excellence.

For researchers, the present study highlights the relative importance of operational versus contextual factors, with respect to cost performance in hospitals. The results of this study also provide direction for additional research into the role operational performance might play in determining the overall organizational performance in a hospital.