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在R Shiny中可视化医院管理数据——一个案例研究

Visualizing Hospital Management Data in R Shiny-A Case Study.

作者信息

Voellger Benjamin, Malesevic-Lepir Milica, Abdelrehim Mohamed A Hafez, Bockelmann Dalibor

机构信息

Department of Neurosurgery, Klinikum Bad Hersfeld, Seilerweg 29, 36251 Bad Hersfeld, Germany.

Department of Neurosurgery, University Hospital Marburg, Baldinger Str., 35043 Marburg, Germany.

出版信息

Healthcare (Basel). 2024 Sep 14;12(18):1846. doi: 10.3390/healthcare12181846.

DOI:10.3390/healthcare12181846
PMID:39337188
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11432085/
Abstract

OBJECTIVE

There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians.

METHODS

We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use case, we recorded basic KPIs (numbers of patients (NPs), reimbursed valuation ratios (RVRs), mean length of stay (LOS)), main diagnoses (MDGNs), main procedures (MPRCs), and catchment area (CA) by district from April 2022 to March 2024 at the index department in central Germany, where a neurotrauma and spinal surgery service was resumed on 1 April 2022. Case mix indexes (CMIs) were calculated. We retrieved information about online-reported patient satisfaction (ORPS) from an online physician rating platform between January 2022 and March 2024. Information on longitudes and latitudes of the index department and neighbouring hospitals was collected. We calculated car travelling isochrones (CTIs) of the hospitals as a proxy variable for accessibility. Chi-square and Fisher's exact served as statistical tests.

RESULTS

During the observation period, the monthly NPs increased from 26 to 43, the RVR showed a 3.96-fold increase, the CMI showed a 2.41-fold increase, and the LOS reached a steady state in the 2nd year after service resumption. CA ( = 0.03), MDGNs, and MPRCs diversified. ORPS trended towards better overall evaluation after service resumption ( = 0.09). CTI mapping identified a unique market position of the index department.

CONCLUSIONS

The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions.

摘要

目的

临床医生需要获取超出基本关键绩效指标(KPI)的医院管理信息。

方法

我们在R Shiny中开发了一个交互式应用程序(IAPP)来可视化此类信息。我们在网上提供了IAPP的源代码。作为一个用例,我们记录了2022年4月至2024年3月德国中部索引科室的基本KPI(患者数量(NP)、报销估值比率(RVR)、平均住院时间(LOS))、主要诊断(MDGN)、主要手术(MPRC)以及按地区划分的服务范围(CA),该科室于2022年4月1日恢复了神经创伤和脊柱外科服务。计算了病例组合指数(CMI)。我们从一个在线医生评级平台上检索了2022年1月至2024年3月期间在线报告的患者满意度(ORPS)信息。收集了索引科室和邻近医院的经纬度信息。我们计算了医院的驾车等时线(CTI)作为可达性的替代变量。使用卡方检验和费舍尔精确检验作为统计检验。

结果

在观察期内,每月的NP从26增加到43,RVR增长了3.96倍,CMI增长了2.41倍,LOS在恢复服务后的第二年达到稳定状态。CA( = 0.03)、MDGN和MPRC呈现多样化。恢复服务后,ORPS的总体评价呈上升趋势( = 0.09)。CTI映射确定了索引科室独特的市场地位。

结论

IAPP使临床医生能够获取扩展的医院管理数据,可为医疗保健领域的其他利益相关者提供信息,并且可以根据当地情况进行定制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/b182d1d891ce/healthcare-12-01846-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/c9b16a912404/healthcare-12-01846-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/d37e3fbf9515/healthcare-12-01846-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/833c9de65cc8/healthcare-12-01846-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/6dd575e1222d/healthcare-12-01846-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/b182d1d891ce/healthcare-12-01846-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/c9b16a912404/healthcare-12-01846-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/d37e3fbf9515/healthcare-12-01846-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/833c9de65cc8/healthcare-12-01846-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/6dd575e1222d/healthcare-12-01846-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/48a0/11432085/b182d1d891ce/healthcare-12-01846-g001.jpg

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