<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p> 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.</p>
</sec>
<sec><h3>Objectives</h3>
<p> 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.</p>
</sec>
<sec><h3>Materials and methods</h3>
<p> 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.</p>
</sec>
<sec><h3>Results</h3>
<p> 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.</p>
</sec>
<sec><h3>Conclusions</h3>
<p> 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.</p>
</sec>
</abstract>

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.

Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application

Department of Epidemiology and Biostatistics

Journal of Health Study and Medicine

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...

Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application

Pubblicato online: 15 lug 2024

Pagine: 59 - 94

Ricevuto: 28 mag 2024

Accettato: 14 giu 2024

DOI: https://doi.org/10.2478/jhsm-2024-0003

Parole chiave
length of stay, hospitalisation, health data, diagnostic-related groups, business intelligence

© 2024 Jarosław Stefan Kozera et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application

Pubblicato online: 15 lug 2024

Pagine: 59 - 94

Ricevuto: 28 mag 2024

Accettato: 14 giu 2024

DOI: https://doi.org/10.2478/jhsm-2024-0003

Parole chiavelength of stay, hospitalisation, health data, diagnostic-related groups, business intelligence

© 2024 Jarosław Stefan Kozera et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
length of stay, hospitalisation, health data, diagnostic-related groups, business intelligence