Literature DB >> 21263360

Performance evaluations and league tables: do they capture variation between organizational units? An analysis of 5 Swedish pharmacological performance indicators.

Henrik Ohlsson1, Julian Librero, Jan Sundquist, Kristina Sundquist, Juan Merlo.   

Abstract

BACKGROUND: The use of league tables during the last decade has frequently been employed to assess quality in health care. However, few studies have attempted to assess quality by quantifying the variability across the organizational units or attempted to investigate whether the units are the correct context that really influences the outcome under study.
OBJECTIVES: To quantify the variation between different organizational units regarding 5 different Swedish national pharmacological performance indicators and to examine whether the organizational units under study are a valid construct of the context that influences the specific outcome. RESEARCH
DESIGN: A multilevel model with patients nested within health care units that in turn were nested within County councils was used. By using measures of variance (intraclass correlation [ICC]), we quantified the extent to which the 5 indicators of health care quality were conditioned by the specified units.
RESULTS: For all 5 studied indicators, the variation between county councils was small (ICC ranged from 2% to 7%), whereas the variation among health care units seemed to be more important (ICC ranged from 20% to 40%).
CONCLUSION: As the variation between county councils was small, using league tables for performance evaluation seems to be inappropriate. If league tables are to be presented, the relative size of the variation at the higher levels and an analysis regarding the possible influence of the context for the specific outcome should be included. This approach provides useful information for identifying relevant contexts to capture health care variation.

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Year:  2011        PMID: 21263360     DOI: 10.1097/MLR.0b013e31820325c5

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  12 in total

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2.  The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

Authors:  Peter C Austin; Philippe Wagner; Juan Merlo
Journal:  Stat Med       Date:  2016-11-25       Impact factor: 2.373

3.  Chronic Obstructive Pulmonary Disease in Sweden: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy.

Authors:  Sten Axelsson Fisk; Shai Mulinari; Maria Wemrell; George Leckie; Raquel Perez Vicente; Juan Merlo
Journal:  SSM Popul Health       Date:  2018-03-20

4.  Albuminuria measurement in diabetic care: a multilevel analysis measuring the influence of accreditation on institutional performance.

Authors:  Nermin Ghith; Juan Merlo; Anne Frølich
Journal:  BMJ Open Qual       Date:  2019-01-14

5.  Hospital differences in mortality rates after hip fracture surgery in Denmark.

Authors:  Pia Kjær Kristensen; Juan Merlo; Nermin Ghith; George Leckie; Søren Paaske Johnsen
Journal:  Clin Epidemiol       Date:  2019-07-16       Impact factor: 4.790

6.  Acknowledging the role of patient heterogeneity in hospital outcome reporting: Mortality after acute myocardial infarction in five European countries.

Authors:  Micaela Comendeiro-Maaløe; Francisco Estupiñán-Romero; Lau Caspar Thygesen; Céu Mateus; Juan Merlo; Enrique Bernal-Delgado
Journal:  PLoS One       Date:  2020-02-06       Impact factor: 3.240

7.  Disentangling the contribution of hospitals and municipalities for understanding patient level differences in one-year mortality risk after hip-fracture: A cross-classified multilevel analysis in Sweden.

Authors:  Pia Kjær Kristensen; Raquel Perez-Vicente; George Leckie; Søren Paaske Johnsen; Juan Merlo
Journal:  PLoS One       Date:  2020-06-03       Impact factor: 3.240

8.  Short Term Survival after Admission for Heart Failure in Sweden: Applying Multilevel Analyses of Discriminatory Accuracy to Evaluate Institutional Performance.

Authors:  Nermin Ghith; Philippe Wagner; Anne Frølich; Juan Merlo
Journal:  PLoS One       Date:  2016-02-03       Impact factor: 3.240

9.  Bad apples or spoiled barrels? Multilevel modelling analysis of variation in high-risk prescribing in Scotland between general practitioners and between the practices they work in.

Authors:  Bruce Guthrie; Peter T Donnan; Douglas J Murphy; Boikanyo Makubate; Tobias Dreischulte
Journal:  BMJ Open       Date:  2015-11-06       Impact factor: 2.692

10.  The role of the clinical departments for understanding patient heterogeneity in one-year mortality after a diagnosis of heart failure: A multilevel analysis of individual heterogeneity for profiling provider outcomes.

Authors:  Nermin Ghith; Anne Frølich; Juan Merlo
Journal:  PLoS One       Date:  2017-12-06       Impact factor: 3.240

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