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Dissecting the primary to specialist referrals using graph neural networks: pattern comparison in different referral time windows (30-day and 90-day) given physicians specialties
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Resumo(s)
The healthcare referral system affects all points of the healthcare ecosystem – access to
care, patient satisfaction, physician utilization, and healthcare costs. The state of these variables
plays a critical role in determining healthcare efficiency. In this paper, we dissect the medical
referrals from primary to secondary care in Florida in 2015 and tackle them from three
perspectives – the influence of physician experience in referral choice, the relationship between
physician referral choice and Medicare spending, and the pattern detection given different
referral windows. To accomplish our goal of identifying patterns in primary to secondary
referral mechanisms, we use Graph Neural Networks (GNN) unsupervised model to learn the
vectorial representation of our physician nodes and their properties in the network. This work
provides new discoveries on factors that influence referral patterns and can be used to make
better decisions when aiming to improve the efficiency of referrals.
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Graph neural networks National provider identifier Physician referral network Medicare