A Network-Theoretic Analysis of Hospital Admission, Transfer, and Discharge Data

Comprehending complex behavior of flow within a graph is of interest to clinicians and mathematicians alike. In this study we examine admission, discharge, and transfer data of patients within a hospital system, and process the importance of nodes through several graph metrics. One common metric, which measures population densities through a continuous time Markov process, will be compared against centrality measures, a technique more often used in social media studies. Our findings show that centrality measures capture behavior related to the topology of the network that may be missed by Markov processes. This suggests that, for determining the allocation of resources between departments of a hospital, centrality measures in some cases may prove more suitable for interpreting patient flow data. Departmental rankings and suitable instances for the application for each graph metric are provided.