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.