研究背景：2021年-2024年中国医保启动住院患者按病组分值付费改革行动计划。定点医疗机构如何根据自己的定位和职责进行科学的病案管理，如何走出盲目根据疾病诊断分组和医保支付标准计算盈亏和绩效的误区，是医院管理的挑战。研究目的：本研究基于R医院病案首页信息，对心律失常疾病患者进行病例分型研究和建立判别模型。分析其住院费用分布情况和费用影响因素，不同病例分型特点，形成微观管理工具。研究方法：采用IBM SPSS 26.0对心律失常患者相关病案数据进行分析，检验水准定位α=0.05。通过线性逐步判别分析筛选出病例分型模型的判别参数，通过Bayes判别分析法建立病例分型的判别分析模型。对心律失常患者的病案信息数据进行描述性统计分析，对其住院费用通过Kolmogorov- Smirnov（K-S）检验进行正态性检验。通过Mann-Whitney U检验、Kruskal-Wallis H检验对心律失常疾病患者住院费用、各类型费用进行单因素分析。通过多因素线性回归分析方法进一步分析其总住院费用和不同病例分型的住院费用的影响因素。研究结果：根据疾病严重程度对心律失常疾病患者进行病例分型，A型568例，B型1172例，C型520例，D型237例。根据是否含有手术操作，分别建立非手术组、手术组病例分型模型。住院患者中男性有1293例，女性有1204例，60岁以上患者占比约64.72%。患者入院途径以门诊为主，约占比84.94%，住院时间1-2周。不同病例分型的住院费用结构存在差异，主要影响因素有年龄、性别、付款方式、是否有出院31日内再住院计划、PCCL、总诊断个数、是否有手术操作、病例分型、住院天数。对于不同病例分型而言，住院费用影响因素略有差别。研究结论：本研究以分析和管理心律失常患者住院费用为例，探索了医院微观管理工具。随着病例分型的增加，患者平均年龄、住院天数、住院费用呈上升趋势。心律失常患者以老年人群为主，应当重点关注疾病的筛查、诊疗。费用结构可能存在不合理之处，需要进一步优化控费。应当针对患者住院费用的影响因素制定相应措施，合理控制心律失常患者医疗费用增长。
Background: From 2021 to 2024, China‘s medical insurance launched the reform action plan of Diagnosis-Intervention Packet. How to manage medical records scientifically according to hospital institutions‘ positioning and how to get out of the misunderstanding of profit and loss acount according to Diagnoses Related Groups and medical insurance payment standards without clear and reasonable goals are challenges for Medical institutions.Objectives: Based on the information from the front page of R hospital‘s medical records, this study studied the case classification of patients with arrhythmia disease and established a case classification discriminant analysis model. In this study, the information of hospitalization costs and the factors affecting the costs of patients with arrhythmia were studied and analyzed, and the classification characteristics of different cases were studied and explored, so as to create a micro-management tool to control of hospitalization costs of patients.Methods: IBM SPSS 26.0 was used to analyze the relevant medical data of patients with arrhythmia, and the test level positioning α=0.05. The discriminant parameters of the case classification model were screened by linear step discriminant analysis. The case classification model of arrhythmia disease was established by Bayes discriminant analysis. Descriptive statistical analysis was carried out on the medical record data of patients with arrhythmia. Kolmogorov-Smirnov （K-S） test was used to test the normality of hospitalization expenses of patients with arrhythmia. Mann-Whitney U test and Kruskal-Wallis H test were used to analyze the hospitalization costs and various types of hospitalization costs in patients with arrhythmia. Multivariate linear regression analysis was used to further analyze the factors affecting the total hospitalization cost of patients with arrhythmia and the hospitalization cost of patients with different case types of arrhythmia.Results: In this study, patients with arrhythmia disease who met the inclusion and criteria were classified into case types according to the severity of the disease, and the classification results were 568 cases of type A, 1172 cases of type B, 520 cases of type C, and 237 cases of type D. The case classification models of patients with arrhythmia in the non-operation group and the operation group were established according to whether surgery was involved or not. Among the hospitalized patients with arrhythmia, 1293 were male and 1204 were female, and the patients mainly concentrated in the elderly population over 60 years old, accounting for about 64.72%. Among the admission routes of patients, outpatient service was the main method, accounting for 84.94%, and the length of stay was mainly distributed in 1-2 weeks. There are differences in the structure of hospitalization cost in different cases of arrhythmia. The main factors influencing the hospitalization cost of patients with arrhythmia were age, gender, medical payment method, whether there was a 31-day re-hospitalization plan, PCCL, total number of diagnoses, whether there was surgery, case type, and length of stay. For patients of different case types, the influencing factors of hospitalization cost were different..Conclusions: This study explores hospital micro-management tools which based on the front page of medical records by analyzing and managing hospitalization expenses for patients with arrhythmia. With the increase of case classification, the average age of patients, the length of stay and the cost of hospitalization showed positive relevance. The patients with arrhythmia are mainly elderly people. The screening, diagnosis and treatment of the disease should be paid more attention to. The structure of hospitalization cost may be unreasonable, and it is necessary to further control the cost. Corresponding measures should be formulated according to the influencing factors of hospitalization costs to control the increase of medical costs for patients with arrhythmia reasonably.
