Analyzing Trends of Hospital Length of Stay Using Phase-Type Distributions

As the Singapore´s population ages rapidly, the number of geriatric inpatients in Singapore is expected to rise significantly. This will certainly exert greater pressures on the efficient management of hospital resources. Hospital length of stay (LOS) is an important indicator of hospital activity and management because of its direct relation to resource consumption. Planning of hospital resources according to identified trends of LOS is therefore an effective way to meet such future needs. In this paper, we propose a method to analyze the temporal trends of LOS based on the Coxian phase-type distributions, a special type of continuous-time Markov process. By fitting and regressing the probabilities of discharge from each phase of the distribution on time, we have found evidence of an growing trend in the proportion of long-staying patients in our sample of stroke-related patients. The datasets were also robustified by the method of bootstrapping to aid our analysis.