Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application
, and | Jul 15, 2024
Published Online: Jul 15, 2024
Page range: 59 - 94
Received: May 28, 2024
Accepted: Jun 14, 2024
DOI: https://doi.org/10.2478/jhsm-2024-0003
Keywords
© 2024 Jarosław Stefan Kozera et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Background
The application of business intelligence (BI) tools in hospitals can enhance the quality and efficiency of care by providing insights into diagnostic, therapeutic, and business processes. BI tools aid in infection monitoring, clinical decision -making, and analysis of hospitalisation durations within Diagnostic-Related Groups (DRGs), identifying inefficiencies and optimizing resource use.
Objectives
This study aims to analyse hospital length of stay and identify the DRGs with the most inefficient hospitalization times using the BI -driven Smart Hospital application.
Materials and methods
The Smart Hospital application, developed on the Qlik Sense BI platform, analysed data from the National Health Fund (NFZ), Statistics Poland, e -health Centre (CEZ), and hospitalisations billed by DRG sections. The dataset included 20,376,405 hospitalisations from 2017–2019.
Results
The average length of stay (ALOS) was 6.2 days, with an effective length of stay (ELOS) of 4.33 days. Ineffective hospitalisation days totalled 30,307,086, accounting for 28.99% of all hospitalizations. The most inefficient DRGs were E53G (Cardiovascular failure), A48 (Complex stroke treatment), N01 (Childbirth), T07 (Trauma conservative treatment), and D28 (Respiratory and thoracic malignancies), contributing to about 14% of all ineffective hospital days.
Conclusions
Understanding the factors influencing hospitalisation durations in DRGs can improve patient flow management. Future research should compare treatment effectiveness concerning hospitalisation duration to develop optimal strategies for specific patient groups.