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A clinical intervention known as 'bridging to transplant', in which a patient is placed on life-sustaining support, can be used to increase the chance of an individual surviving until a donor heart becomes available. However, the impact of this on other patients on the waiting list and the wider implications for the resourcing of cardiac units remains unclear. Initial insights have previously been generated using a birth-death queuing model, but this model did not incorporate realistic donor-recipient assumptions regarding blood type and weight. Here we report on a complementary simulation study that examined how estimates from the analytical model might change if organ matching were better taken into account. Simulation results showed that system metrics changed substantially when recipient donor compatibility was modelled. However, the effects of blood type compatibility were countered by that of weight compatibility and when combined, these have a relatively small net effect on results.

In this paper we propose a new approach capable of determining clinically meaningful patient groups from a given dataset of patient spells. We hypothesise that the skewed distribution of length of stay (LOS) observations, often modelled in the past using mixed exponential equations, is composed of several homogeneous groups that together form the overall skewed LOS distribution. We show how the Gaussian mixture model (GMM) can be used to approximate each group, and discuss each group's possible clinical interpretation and statistical significance. In addition, we show how the health professional can use the outcome of the grouping approach to answer several questions about individual patients and their likely LOS in hospital. Our results demonstrate that the grouping of stroke patient spells estimated by the GMM resembles the clinical experience of stroke patients and the different stroke recovery patterns.

In the Bulgarian health care system patients are transferred between different health care units within the health care facility, and occasionally between units that are far apart, depending on the stage of the patient's treatment progress. Determining patient resource consumption prior to utilization is vital for the management and planning of health care resources. In this paper we propose a generalized net model of patient flows that can be used to evaluate the current design (or alternative designs) in order to improve the management of the diagnostic consultative centres.

On-Line Analytical Processing (OLAP) tools use multidimensional views to provide quick access to information. They have become the de facto standard in the business world for analytical databases. In health care, care givers and managers could benefit from being able to perform interactive data exploration, ad-hoc analysis and possibly discover hidden trends and patterns in health data. However, health data have unique characteristics that distinguish them from common business examples, an aspect that makes the direct adaptation of the already established business oriented solutions difficult. In this paper we report the development of an OLAP system for analyzing hospital discharge data and for modeling hospital length of stay.