Learning and Optimization of Patient-Physician Matching Index in Specialty Care

It is challenging for a patient without medical knowledge to select a `capable' physician based on nontransparent medical information. The situation becomes pervasive in specialty care. Motivated by this existing patient-physician matching problem, we propose a novel physician matching index (PMI) obtained by an analytical framework integrated with an improved multi-disease pre-diagnosing Bayesian network (BN) model. The pre-diagnosis BN structure learning is critical since it provides the causal map among diseases and symptoms, but it has been proved to be NP-hard. To improve the computational tractability of the BN structure learning, we propose a dynamic programming based cache calculation algorithm integrated with expert knowledge. The optimal BN structure is obtained through an improved branch-and-bound algorithm. Given patients' symptoms and physicians' specialty information, we apply the trained pre-diagnosis BN model to obtain PMI, which can be extended to the weighted matching index by considering patient preferences. A case study of the patient-physician matching problem in the ear, nose, and throat (ENT) department is conducted. The branchand-bound algorithm with the proposed cache calculation algorithm learns the optimal BN structure with high pre-diagnosing accuracy and time efficiency. We disclose that the proposed PMI can rectify the misdiagnosis since the highly related diseases usually belong to one specialty. Moreover, we demonstrate the significance of the consistency between the physicians' specialty and the patients' disease distribution. We also highlight that the proposed PMI guides the patients in choosing physicians more appropriately under independent patient preferences.