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 branch-and-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.
Note to Practitioners
—This research is motivated by a potential healthcare quality issue caused by the mismatch between patient and physician in specialty care, which is critical for both referral and non-referral systems. We propose a novel physician matching index (PMI) according to patients’ symptoms and physicians’ specialties based on potential disease(s) pre-diagnosed by a Bayesian network model. In the case study of the ear, nose, and throat (ENT) department, we show that PMI can rectify a large portion of misdiagnosed cases. Under the consideration of independent patient preferences, we also find that PMI improves the matching performance. To improve the systematic matching outcome, we suggest that physicians’ specialty cross-training programs be consistent with the patient’s disease distribution. However, we assume that patients’ symptoms are known and physicians’ specialties are clearly described, which might need to be further investigated. The framework proposed in this research can be extended to other specialty care departments, e.g., neurology and orthopedics.