An application of lifetime models in estimation of expected length of stay of patients in hospital with complexity and age adjustment

Expected length of stay (ELOS) of patients in hospital is an important measure in hospital resource utilization management. Previous work has shown that estimation of ELOS is improved using complexity and age adjustment. These improved estimates have the potential to improve the accuracy of estimates of resource use. Recently other authors have applied the linear regression model to make complexity and age adjustments in the estimation of ELOS. However, these estimates using linear regression estimates are likely flawed on the basis that the assumptions regarding the distribution of data for the linear regression model are unjustifiable. The non-normal distributions of most hospital patient discharge data demand that an alternative method be described to provide accurate estimates of ELOS. The purpose of this paper is to describe an alternative method which uses lifetime models to initially estimate the expected length of stay. The paper then provides an approach to estimate the adjusted expected length of stay (AELOS) using several influencing factors by application of lifetime models. Depending on whether or not the proportional hazards assumption is appropriate for the data, the Cox proportional hazards model or the Kaplan-Meier adjustment is recommended.