Queues　or　waiting　lines　are　a　common　phenomenon　in　healthcare　facilities.　The　increasing　number　of　admissions　to　hospital　emergency　departments　(EDs)　has　resulted　in　overcrowded　EDs　and　decreased　quality　of　care.　Patients　may　renege　with　dissatisfaction　while　waiting,　prolonged　hospital　stays,　or　the　cancelation　of　surgeries,　especially　for　those　with　acute　diseases.　This　paper　studies　patient　admission　and　queuing　control　policies,　considering　premature　discharge　decisions　and　reneging　behavior.　First,　we　formulate　this　problem　as　an　infinite-horizon　total　discounted　cost　Markov　decision　process　to　balance　bed　utility　and　experiences　of　patients　waiting　for　admission　to　minimize　the　total　expected　cost　for　the　hospital.　Then,　we　characterize　an　optimal　admission　threshold　policy　by　analyzing　the　structural　properties　of　the　optimal　value　function.　Numerical　experiments　are　employed　to　discuss　how　the　optimal　dynamic　policy　depends　on　some　key　parameters　of　the　system.　Finally,　the　performance　of　the　optimal　policy　is　discussed　through　comparison　with　the　benchmark:　A　myopic　rule　(first　come,　first　served)　mimicking　the　decision-making　in　practice　under　different　scenarios.　The　numerical　results　show　that　the　reneging　behavior　can　affect　the　superiority　of　the　optimal　policy　compared　to　the　benchmark,　and　the　optimal　policy　can　significantly　reduce　the　number　of　waiting　patients　without　increasing　the　total　expected　cost　when　the　system　is　overloaded.