  In countries that enabled patients to choose their own providers, a common
problem is that the patients did not make rational decisions, and hence, fail
to use healthcare resources efficiently. This might cause problems such as
overwhelming tertiary facilities with mild condition patients, thus limiting
their capacity of treating acute and critical patients. To address such
maldistributed patient volume, it is essential to oversee patients choices
before further evaluation of a policy or resource allocation. This study used
nationwide insurance data, accumulated possible features discussed in existing
literature, and used a deep neural network to predict the patients choices of
hospital levels. This study also used explainable artificial intelligence
methods to interpret the contribution of features for the general public and
individuals. In addition, we explored the effectiveness of changing data
representations. The results showed that the model was able to predict with
high area under the receiver operating characteristics curve (AUC) (0.90),
accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly
imbalanced label. Generally, social approval of the provider by the general
public (positive or negative) and the number of practicing physicians serving
per ten thousand people of the located area are listed as the top effecting
features. The changing data representation had a positive effect on the
prediction improvement. Deep learning methods can process highly imbalanced
data and achieve high accuracy. The effecting features affect the general
public and individuals differently. Addressing the sparsity and discrete nature
of insurance data leads to better prediction. Applications using deep learning
technology are promising in health policy making. More work is required to
interpret models and practice implementation.
