As　the　development　of　Electronic　Medical　Records　in　hospitals,　more　and　more　＂real　world　data＂　became　available　for　clinical　researches,　especially　for　the　clinical　effectiveness　evaluation.　Besides　traditional　statistical　methods,　more　Machine　Learning　methods　are　also　used　to　analyze　the　data.　In　this　study,　one　high　value　medical　consumable　-gel,　which　is　used　in　large　quantities　in　cleaning　surgical　incision,　is　analyzed　using　both　a　traditional　statistical　method　and　a　Machine　Learning　method　to　evaluate　its　clinical　applicability,　efficacy　and　safety.　The　Electronic　Medical　Records　for　three　years　are　collected　including　patient　gender,　age,　quantity　of　gel　usages,　surgical　incision　grades,　preoperative　diagnosis,　and　information　about　postoperative　recoveries　and　so　on.　Through　the　two　analysis　methods,　the　difference　among　incision　healing,　antibiotic　use,　and　the　postoperative　hospital　days　after　using　gel　or　common　saline　in　the　wound　are　analyzed.　The　results　show　that　the　two　methods　can　reveal　different　aspects　in　clinical　evaluation.　The　decision　tree　classification　can　provide　valuable　suggestions　for　the　reasonable　use　of　the　gel　and　making　reasonable　medical　policy　with　the　applicable　conditions　of　gels.