This　study　aims　to　develop　a　statistical　model　to　detect　both　high　and　low　outlier　cases　in　terms　of　diagnosis-related　group　(DRG)　distributions.　A　data　set　containing　five　DRGs　with　458　patient　cases　was　selected　for　the　study.　The　distributions　of　DRG　cost　and　length　of　stay　(LOS)　are　examined　firstly,　and　all　the　distributions　of　DRG　costs　are　lognormal　whereas　all　the　distributions　of　LOS　are　not　lognormal　or　normal.　A　statistical　model　referred　to　as　LM　is　set　out　for　outlier　detection　in　terms　of　the　lognormal　distributions　of　DRG　costs.　The　LM　algorithm　is　compared　with　the　geometric　mean　(GM),　Inter-quartile　(IQ)　and　L3H3　algorithms.　LM　has　the　highest　statistics　for　the　Accuracy,　Kappa　coefficient,　Sensitivity　and　Youden＇s　index.　In　addition,　LM　has　the　largest　area　under　the　ROC　curve　(AUC).　We　find　that　LM　is　a　superior　method　to　detect　both　low　and　high　outliers　for　DRG　costs,　thereby　improving　the　efficiency　and　effectiveness　of　DRG　prospective　payment　systems　and　equity　of　healthcare.