Visualizing Hospital Management Data in R Shiny-A Case Study

Visualizing Hospital Management Data in R Shiny-A Case Study

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

Full description

Saved in:
Bibliographic Details
Published in:Healthcare (Basel) Vol. 12; no. 18; p. 1846
Main Authors: Voellger, Benjamin, Malesevic-Lepir, Milica, Abdelrehim, Mohamed A Hafez, Bockelmann, Dalibor
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 14-09-2024
MDPI
Subjects:
Administration
Business metrics
Case studies
Coronaviruses
COVID-19
hospital management
Hospitals
Length of stay
Market positioning
neurosurgery
Patient satisfaction
performance indicators
R (Programming language)
spatiotemporal analysis
SWOT analysis
Technical Note
Germany
patient satisfaction
spatiotemporal analysis
hospital management
performance indicators
neurosurgery
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. 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. 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. The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare12181846