  The aging world population requires a sustainable and high-quality healthcare
system. To examine the efficiency of medical cooperation, medical provider and
physician networks were constructed using patient claims data. Previous studies
have shown that these networks contain information on medical cooperation.
However, the usage patterns of multiple medical providers in a series of
medical services have not been considered. In addition, these studies used only
general network features to represent medical cooperation, but their expressive
ability was low. To overcome these limitations, we analyzed the medical
provider network to examine its overall contribution to the quality of
healthcare provided by cooperation between medical providers in a series of
medical services. This study focused on: i) the method of feature extraction
from the network, ii) incorporation of the usage pattern of medical providers,
and iii) expressive ability of the statistical model. Femoral neck fractures
were selected as the target disease. To build the medical provider networks, we
analyzed the patient claims data from a single prefecture in Japan between
January 1, 2014 and December 31, 2019. We considered four types of models: a
model using node strength and linear regression to a model using feature
representation by node2vec and regression tree ensemble, which is a machine
learning method. The results showed that a stronger medical provider reduces
the duration of hospital stay. The overall contribution of the medical
cooperation to the duration of hospital stay extracted from the medical
provider network using node2vec is approximately 20%, which is approximately 20
times higher than the model using strength.
