摘要
目的探析肿瘤患者高额住院费用影响因素的关联规则,为医院优化医疗成本管理措施提供依据。方法在某三级甲等综合医院住院病例信息系统,提取2022年肿瘤科DRG病组住院患者的病案首页。以住院费用的上四分数为患者分组阈值,住院费用≥该阈值的患者为高额费用组,其他患者为对照组;将年龄、性别和入院病情等12个因素作为高额住院费用的潜在影响因素。采用频繁模式增长算法(frequent pattern growth,FP-Growth)和先验(Apriori)算法挖掘高额住院费用影响因素之间的潜在关联规则。采用Logistic回归分析高额住院费用的独立影响因素。结果共纳入5512例住院患者,其中高额费用组患者1378例。经FP-Growth和Apriori算法分析,获得13项高额住院费用影响因素有效强关联规则。规则前项包括年龄≥70岁、住院天数≥7 d、其他诊断≥5种、手术、再入院计划,使用抗生素、入院情况(一般/危急)、入院生活得分(61~99分)、护理级别(一级/二级)、日间病房和危重症。Logistic回归结果显示,除性别、使用抗生素和再入院计划以外,其余9个影响因素均为高额住院费用的独立影响因素(P<0.05)。结论FP-Growth和Apriori算法联合应用可有效挖掘肿瘤患者高额住院费用影响因素的关联规则,预警信息主要包括住院天数、其他诊断数量和手术等。建议医疗机构通过优化临床路径管理、诊疗流程再造、入院风险评估以及多学科协作诊疗等策略合理控制高额住院费用产生。
ObjectiveExploring the association rules of factors influencing high hospitalization costs for cancer patients,providing references for hospitals to optimize medical cost management measures.MethodsIn the inpatient case information system of a tertiary general hospital,the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained.The upper four scores of hospitalization costs was used as the threshold for patient grouping.Patients with hospitalization costs≥this threshold were the high-cost group,while other patients were control group;12 factors,including age,gender,and admission condition,etc,were considered as potential influencing factors of high hospitalization costs.FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs.Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.ResultsA total of 5512 hospitalized patients were included,including 1378 patients in the high-cost group.Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained,of which the rule antecedents included age(≥70 years),number of days in hospital(≥7 days),other diagnoses(≥5),surgery,planned readmission,use of antibiotics,admission(general/critical),living admission score(61~99),level of care(level 1/level 2),non-day ward,criticality during hospitalisation.Logistic regression results showed that all nine influencing factors except gender,use of antibiotics,and readmission plans were independent influences on high hospitalization costs(P<0.05).ConclusionsThe joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients.The early warning information mainly included the number of hospitalization days,the number of other diagnoses,surgeries,and so on.It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management,diagnosis and treatment process reengineering,admission risk assessment,and multidisciplinary collaborative diagnosis and treatment strategies.
作者
叶晶晶
周典
田帝
周苑
张钰
吕曼辰
薛同斌
白寰
郭成
吴烨
Ye Jingjing;Zhou Dian;Tian Di;Zhou Yuan;Zhang Yu;Lyu Manchen;Xue Tongbin;Bai Huan;Guo Cheng;Wu Ye(Doctor-Patient Relations Office,First Affiliated Hospital of Anhui Medical University,Hefei 230022,China;Secretary Office of the Party Committee,Second Affiliated Hospital of Anhui Medical University,Hefei 230601,China;Medical Department,First Affiliated Hospital of Anhui Medical University,Hefei 230022,China;Youth League Committee Office,Second Affiliated Hospital of Anhui Medical University,Hefei 230601,China;School of Health Management,Anhui Medical University,Hefei 230022,China;Health Management Centre,Second Affiliated Hospital of Anhui Medical University,Hefei 230601,China;Institution of Hospital Managment,Second Affiliated Hospital of Anhui Medical University,Hefei 230601,China;Human Resources Department,First Affiliated Hospital of Anhui Medical University,Hefei 230022,China)
出处
《中华医院管理杂志》
北大核心
2025年第3期216-222,共7页
Chinese Journal of Hospital Administration
基金
国家卫健委医院管理研究所医疗质量循证管理持续改进研究项目(YLZLXZ22K001)。
作者简介
通信作者:周典,Email:ahmu_zhoudian@163.com。
