scholarly journals DO DIAGNOSIS-RELATED GROUPS APPROPRIATELY EXPLAIN VARIATIONS IN COSTS AND LENGTH OF STAY OF HIP REPLACEMENT? A COMPARATIVE ASSESSMENT OF DRG SYSTEMS ACROSS 10 EUROPEAN COUNTRIES

2012 ◽  
Vol 21 ◽  
pp. 103-115 ◽  
Author(s):  
Alexander Geissler ◽  
David Scheller-Kreinsen ◽  
Wilm Quentin ◽  
Keyword(s):  
Length Of Stay ◽  
Hip Replacement ◽  
Diagnosis Related Groups ◽  
Comparative Assessment ◽  
European Countries

scholarly journals Individual and Regional-level Factors Contributing to Variation in Length of Stay After Cerebral Infarction in Six European Countries

2015 ◽  
Vol 24 ◽  
pp. 38-52 ◽  
Author(s):  
Mikko Peltola ◽  
Timo T. Seppälä ◽  
Antti Malmivaara ◽  
Éva Belicza ◽  
Dino Numerato ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Cerebral Infarction ◽  
Regional Level ◽  
European Countries

scholarly journals Characteristics of hospitalizations with influenza diagnosis, France, 2012-2017

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Pivette ◽  
V de Lauzun ◽  
N Nicolay ◽  
A Scanff ◽  
B Hubert
Keyword(s):  
Length Of Stay ◽  
Diagnosis Related Groups ◽  
Laboratory Data ◽  
Incidence Rates ◽  
H3n2 Virus ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Age Group ◽  
Hospital System ◽  
The Impact

Abstract Background Seasonal influenza surveillance in France is based on several data sources (ambulatory data, emergency department and intensive care unit (ICU) admissions, laboratory data, mortality). However, the data do not provide a complete measure of the impact of the epidemics on the hospital system. The objective of the study was to describe the characteristics of influenza hospitalizations from the French national hospital discharge database (PMSI) between 2012 and 2017 and to precise the burden of influenza by age group and by season. Methods All hospitalizations in metropolitan France with at least one ICD-10 code related to influenza (J09, J10, J11) as a principal, related or associated diagnosis between 1 July 2012 to 30 June 2017 were extracted from the PMSI. For each season, the total number of hospitalizations, admissions to ICU, incidence and lethality rates, lengths of stay and classification in diagnosis-related groups were described by age group. Results During the 5 seasons, 91 255 hospitalizations with an influenza-diagnosis were identified. The incidence varied significantly between seasons, from 12.7/100 000 in 2013-2014 to 45.9/100 000 in 2016-2017. A high number of cases was observed in elderlies in 2014-2015 and 2016-2017, marked by the circulation of A (H3N2) virus. The proportion of hospitalizations with an admission in ICU was 10%, and was higher in the 40-79 age group (19%). Lethality increased steadily with age, from 0.5% under 20 years to 10% in 80 years and older. Length of stay also increased with age. Significant regional disparities were observed, with higher incidence rates in South-Eastern France each season. Conclusions The analysis of influenza hospitalizations from the PMSI provides important elements on influenza burden, not available in the current surveillance systems. An annual analysis, stratified by age group, would provide an indicator of the impact of the epidemics on hospital system at the end of each influenza season. Key messages Important influenza incidence variations were observed between seasons by age groups. Severity and impact of influenza (mortality, ICU, length of stay) varied significantly by age group.

Use of a comparative database to identify excessive hematology/medical oncology hospital days and readmissions.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 131-131
Author(s):  
Brian Halbert ◽  
Jessica A. Zerillo
Keyword(s):  
Length Of Stay ◽  
Medical Oncology ◽  
Diagnosis Related Groups ◽  
Third Party ◽  
Clinical Database ◽  
Readmission Rates ◽  
Resource Manager ◽  
Diagnostic Categories ◽  
Expected Length ◽  
Hospital Days

131 Background: Reducing length of stay and readmissions are important goals to improve healthcare quality and decrease costs. We identified diagnostic categories accounting for excessive hospital days and readmissions on our hematology/medical oncology (HO) service. Methods: We used a third-party comparative clinical database (Vizient Inc, Clinical Data Base/Resource Manager) to identify discharges from our HO service in 2015. Cases were categorized by All Patients Refined Diagnosis Related Groups (APR DRG), and we calculated excess hospital days attributed to each using the number of cases and their observed/expected length of stay. We also collected 30-day readmission rates for each APR DRG on our HO service and hospital wide. Readmission rates were compared using Fisher exact tests. Results: We identified 1,361 discharges from our HO service with 134 APR DRG codes and 880.5 excess hospital days. Fourteen APR DRGs accounted for over 80% of the excess hospital days. Readmission rates ranged from 0% to 66.7%, and were generally higher than rates hospital wide. Readmissions for septicemia and major operating room (OR) procedures for lymphatic neoplasms were significantly higher on the HO service. Conclusions: Relatively few APR DRGs accounted for the majority of our excess hospital days, and also had high readmission rates. We will conduct additional review of these APR DRGs to identify opportunities to improve quality and reduce cost. [Table: see text]

scholarly journals Telemedicine support shortens length of stay after fast-track hip replacement

2016 ◽  
Vol 88 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Martin Svoldgaard Vesterby ◽  
Preben Ulrich Pedersen ◽  
Malene Laursen ◽  
Søren Mikkelsen ◽  
Jens Larsen ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hip Replacement ◽  
Fast Track

scholarly journals HOW WELL DO DIAGNOSIS-RELATED GROUPS FOR APPENDECTOMY EXPLAIN VARIATIONS IN RESOURCE USE? AN ANALYSIS OF PATIENT-LEVEL DATA FROM 10 EUROPEAN COUNTRIES

2012 ◽  
Vol 21 ◽  
pp. 30-40 ◽  
Author(s):  
Anne Mason ◽  
Zeynep Or ◽  
Thomas Renaud ◽  
Andrew Street ◽  
Josselin Thuilliez ◽  
...  
Keyword(s):  
Resource Use ◽  
Diagnosis Related Groups ◽  
European Countries ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data
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