A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression
Despite the benefits of studying multiple patient outcomes together, research on between-hospital variation has often focused on single outcomes or disease-specific study populations. In this study we examined nationwide temporal trends and between-hospital variation in in-hospital mortality, 30-day...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442522000636 |
| _version_ | 1811178718595383296 |
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| author | Astrid Van Wilder Bianca Cox Dirk De Ridder Wim Tambeur Guy Vanden Boer Jonas Brouwers Fien Claessens Luk Bruyneel Kris Vanhaecht |
| author_facet | Astrid Van Wilder Bianca Cox Dirk De Ridder Wim Tambeur Guy Vanden Boer Jonas Brouwers Fien Claessens Luk Bruyneel Kris Vanhaecht |
| author_sort | Astrid Van Wilder |
| collection | DOAJ |
| description | Despite the benefits of studying multiple patient outcomes together, research on between-hospital variation has often focused on single outcomes or disease-specific study populations. In this study we examined nationwide temporal trends and between-hospital variation in in-hospital mortality, 30-day readmissions and length-of-stay above the All-Patient-Refined Diagnoses-Related-Group (APR-DRG)-specific 90th percentile (pLOS). We modelled 13,660,187 admissions derived from an administrative database occurring between 2008 and 2018 in 90 (89%) Belgian acute-care hospitals. We applied an APR-DRG-specific logistic regression to study temporal trends in outcomes, hospital-level associations between outcomes, associations of outcomes with hospitals characteristics, and to evaluate how many and which APR-DRGs explained between-hospital variation. Our proposed analytical model managed to achieve novel insights into healthcare quality of care, illustrating the high potential administrative databases can provide. It was revealed that between-hospital variation in outcomes is likely due to systemic hospital factors. This is illustrated by the fact that baseline bottom-performing hospitals remained underperforming throughout the study period and vice versa. APR-DRG-specific between-hospital variation assessments further confirmed this. When hospitals have overall outcome ratios that significantly deviate from the benchmark, this seems to be driven by a considerable number of APR-DRGs, comprising a diverse set of pathologies. This urges a healthcare policy reform wherein longitudinal follow-up and benchmarking of patient outcomes should become the starting point towards targeted quality improvement interventions. |
| first_indexed | 2024-04-11T06:24:11Z |
| format | Article |
| id | doaj.art-804abde206f3471387d192f28009ffb3 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-04-11T06:24:11Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-804abde206f3471387d192f28009ffb32022-12-22T04:40:28ZengElsevierHealthcare Analytics2772-44252022-11-012100123A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regressionAstrid Van Wilder0Bianca Cox1Dirk De Ridder2Wim Tambeur3Guy Vanden Boer4Jonas Brouwers5Fien Claessens6Luk Bruyneel7Kris Vanhaecht8Leuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, Belgium; Correspondence to: Leuven Institute for Healthcare Policy, KU Leuven - University of Leuven, Belgium.Leuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, BelgiumLeuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, Belgium; Department of Quality, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium; Department of Urology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, BelgiumUniversity Hospitals Leuven, Herestraat 49, 3000 Leuven, BelgiumDepartment of Management, Information and Reporting, University Hospitals Leuven, Herestraat 49, 3000 Leuven, BelgiumLeuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, Belgium; Department of Orthopaedics, University Hospitals Leuven, Herestraat 49, 3000 Leuven, BelgiumLeuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, BelgiumLeuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, BelgiumLeuven Institute for Healthcare Policy, KU Leuven, Kapucijnenvoer 35 Blok D, bus 7001, 3000 Leuven, Belgium; Department of Quality, University Hospitals Leuven, Herestraat 49, 3000 Leuven, BelgiumDespite the benefits of studying multiple patient outcomes together, research on between-hospital variation has often focused on single outcomes or disease-specific study populations. In this study we examined nationwide temporal trends and between-hospital variation in in-hospital mortality, 30-day readmissions and length-of-stay above the All-Patient-Refined Diagnoses-Related-Group (APR-DRG)-specific 90th percentile (pLOS). We modelled 13,660,187 admissions derived from an administrative database occurring between 2008 and 2018 in 90 (89%) Belgian acute-care hospitals. We applied an APR-DRG-specific logistic regression to study temporal trends in outcomes, hospital-level associations between outcomes, associations of outcomes with hospitals characteristics, and to evaluate how many and which APR-DRGs explained between-hospital variation. Our proposed analytical model managed to achieve novel insights into healthcare quality of care, illustrating the high potential administrative databases can provide. It was revealed that between-hospital variation in outcomes is likely due to systemic hospital factors. This is illustrated by the fact that baseline bottom-performing hospitals remained underperforming throughout the study period and vice versa. APR-DRG-specific between-hospital variation assessments further confirmed this. When hospitals have overall outcome ratios that significantly deviate from the benchmark, this seems to be driven by a considerable number of APR-DRGs, comprising a diverse set of pathologies. This urges a healthcare policy reform wherein longitudinal follow-up and benchmarking of patient outcomes should become the starting point towards targeted quality improvement interventions.http://www.sciencedirect.com/science/article/pii/S2772442522000636Temporal trendsLogistic regressionHospitalMortalityLength of stayReadmissions |
| spellingShingle | Astrid Van Wilder Bianca Cox Dirk De Ridder Wim Tambeur Guy Vanden Boer Jonas Brouwers Fien Claessens Luk Bruyneel Kris Vanhaecht A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression Healthcare Analytics Temporal trends Logistic regression Hospital Mortality Length of stay Readmissions |
| title | A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression |
| title_full | A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression |
| title_fullStr | A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression |
| title_full_unstemmed | A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression |
| title_short | A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression |
| title_sort | comprehensive analysis of temporal trends of between hospital variation in mortality readmission and length of stay using logistic regression |
| topic | Temporal trends Logistic regression Hospital Mortality Length of stay Readmissions |
| url | http://www.sciencedirect.com/science/article/pii/S2772442522000636 |
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