某三甲妇儿医院门急诊患者就诊特征与耗时分析

2021-08-24颜勇卿YANYongqing徐军XUJun鲁秀明LUXiuming

医院管理论坛 2021年5期

关键词：小儿

□ 颜勇卿 YAN Yong-qing 徐军 XU Jun 鲁秀明 LU Xiu-ming

Objective To study the outpatient visit time and its influencing factors by the method of data science, and to explore the ways to improve the efficiency of outpatient visits. Methods All the records of outpatient services in 2019 in a tertiary specialized hospital were analyzed by python under Anaconda 1.9.7. p＜ 0.05 was considered statistically significant, and scikit-learn 0.22.1 was used for machine learning. Results The volume change among different periods within a day were statistically significant, but not among days within a week or months within a year. The time spent by the patients for outpatient visit did not change significantly in the above three time ranges. The time spent by the patients for outpatient visit was affected by several factors, including age (0.169), outpatient volume of the day (0.140),number of visits in the past year (0.127), and periods of visit (0.124). Conclusion The time spent for outpatient visit is affected by several factors. The efficiency of outpatient visits can be improved by optimizing the procedures of outpatient visits based on data science in the absence of large investment.

就诊时间是影响门诊服务满意度的重要因素，是医疗机构整体管理水平、医疗质量及服务质量的重要体现[1]，以往针对患者就诊时间的研究多用随机抽样方法，医院信息化发展之后，也有学者利用医院信息平台数据进行分析。随着以大数据为代表的数据科学兴起，各种方法及分析框架不断涌现，使对门诊大数据的分析成为可能[2-3]。本研究选取某妇儿三甲医院2019年全年的数据，利用数据科学软件和方法分析患者就诊时间分布和影响因素，探索门诊管理及流程进一步优化的措施。

研究对象与方法

1.研究对象。以该院2019年全年门诊儿童和成人患者为研究对象，数据来源为医院信息化平台数据库，以数据库中记录的各时间节点作为时间序列(在各时间节点的耗时分析中去除需要预约且预约时间超过1天的特殊化验和检查，其余分析包括全部可用数据)，因患者离院时间难以明确，研究中以患者单次就诊的首次时间节点和末次时间节点差值(文中简述为“整体时间”)间接反映单次就诊总耗时。

2.研究方法。数据处理在Anaconda1.9.7下进行(编译器python3.76；虚拟环境conda 4.83；开发环境Jupyter Notebook 6.03，数据清洗pandas1.02、numpy1.18.1；统计计算statsmodels0.11、scipy1.4.1；可视化Seaborn0.1，pyecharts1.71；机器学习scikit—learn0.22.1)，以p＜0.05作为具有统计学意义。