Medical referrals between primary care physicians (PC) and specialist care
(SC) physicians profoundly impact patient care regarding quality, satisfaction,
and cost. This paper investigates the influence of professional networks among
medical doctors on referring patients from PC to SC. Using five-year
consultation data from a Portuguese private health provider, we conducted
exploratory data analysis and constructed both professional and referral
networks among physicians. We then apply Graph Neural Network (GNN) models to
learn latent representations of the referral network. Our analysis supports the
hypothesis that doctors' professional social connections can predict medical
referrals, potentially enhancing collaboration within organizations and
improving healthcare services. This research contributes to dissecting the
underlying mechanisms in primary-specialty referrals, thereby providing
valuable insights for enhancing patient care and effective healthcare
management.

医生的专业社交关系可以预测医疗转诊，从而提高组织内的合作和改善医疗服务。

医疗转诊机制剖析：医师专业网络的作用

Dissecting Medical Referral Mechanisms in Health Services: Role of  Physician Professional Networks

Recommendation Systems (RS) are often used to address the issue of medical
doctor referrals. However, these systems require access to patient feedback and
medical records, which may not always be available in real-world scenarios. Our
research focuses on medical referrals and aims to predict recommendations in
different specialties of physicians for both new patients and those with a
consultation history. We use Extreme Multilabel Classification (XML), commonly
employed in text-based classification tasks, to encode available features and
explore different scenarios. While its potential for recommendation tasks has
often been suggested, this has not been thoroughly explored in the literature.
Motivated by the doctor referral case, we show how to recast a traditional
recommender setting into a multilabel classification problem that current XML
methods can solve. Further, we propose a unified model leveraging patient
history across different specialties. Compared to state-of-the-art RS using the
same features, our approach consistently improves standard recommendation
metrics up to approximately $10\%$ for patients with a previous consultation
history. For new patients, XML proves better at exploiting available features,
outperforming the benchmark in favorable scenarios, with particular emphasis on
recall metrics. Thus, our approach brings us one step closer to creating more
effective and personalized doctor referral systems. Additionally, it highlights
XML as a promising alternative to current hybrid or content-based RS, while
identifying key aspects to take into account when using XML for recommendation
tasks.

研究使用极端多标签分类方法解决医生推荐系统中的医疗转诊问题，并提出了一个统一的模型利用患者病历。相比使用相同特征的最先进推荐系统，该方法在有咨询历史的患者中稳定改善了标准推荐度量指标达约 10%。对于新患者，极端多标签分类方法在利用可用特征方面表现更好，在有利情况下胜过基准系统，特别是强调召回度量指标。因此，该研究方法使医生推荐系统更加有效和个性化，并将极端多标签分类方法作为一种有前途的替代选择，并指出使用该方法时需要考虑的关键方面。