Displaying random variation in comparing hospital performance

Dishoeck, A M van; Looman, Caspar W.N.; Lier, E C M van der Wilden-van; Mackenbach, Johan P.; Steyerberg, Ewout W.

doi:10.1136/bmjqs.2009.035881

Cited by 24 publications

(27 citation statements)

References 28 publications

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“…Van Dishoeck et al 9 visualised uncertainty in differences between the quality of cardiovascular care in hospitals using three graphical techniques and concluded that random variation is a crucial factor that must be taken into account when judging individual hospitals. Hernandez et al 18 noted poor agreement between different methods of ranking hospital-based quality of care for heart failure.…”

Section: Discussionmentioning

confidence: 99%

Hospital performance based on treatment delays: comparison of ranking methods

et al. 2013

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Section: Discussionmentioning

confidence: 99%

Hospital performance based on treatment delays: comparison of ranking methods

et al. 2013

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“…The usefulness of the quality indicators was based on three criteria [14]: feasibility [15], discriminability [16,17], and statistical uncertainty [15,18,19]. As no previous studies report thresholds on these criteria, we set a priori thresholds based on consensus.…”

Section: Validation Of the Quality Indicatorsmentioning

confidence: 99%

Quality indicators for patients with traumatic brain injury in European intensive care units: a CENTER-TBI study

et al. 2020

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Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measurement and improvement.Methods: Our analysis was based on 2006 adult patients admitted to 54 ICUs between 2014 and 2018, enrolled in the CENTER-TBI study. Indicator scores were calculated as percentage adherence for structure and process indicators and as event rates or median scores for outcome indicators. Feasibility was quantified by the completeness of the variables. Discriminability was determined by the between-centre variation, estimated with a random effect regression model adjusted for case-mix severity and quantified by the median odds ratio (MOR). Statistical uncertainty of outcome indicators was determined by the median number of events per centre, using a cut-off of 10.Results: A total of 26/42 indicators could be calculated from the CENTER-TBI database. Most quality indicators proved feasible to obtain with more than 70% completeness. Sub-optimal adherence was found for most quality indicators, ranging from 26 to 93% and 20 to 99% for structure and process indicators. Significant (p < 0.001) between-centre variation was found in seven process and five outcome indicators with MORs ranging from 1.51 to 4.14. Statistical uncertainty of outcome indicators was generally high; five out of seven had less than 10 events per centre. Conclusions: Overall, nine structures, five processes, but none of the outcome indicators showed potential for quality improvement purposes for TBI patients in the ICU. Future research should focus on implementation efforts and continuous reevaluation of quality indicators. Trial registration: The core study was registered with ClinicalTrials.gov, number NCT02210221, registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

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“…The preset PMR sample size, regardless of the hospital's volume of activity, might introduce a bias. The bias and likelihood of an erroneous classification were limited by combining two approaches: (1) by introducing uncertainty (Van Dishoeck et al recently showed how, depending upon the method used, account can be taken of uncertainty 27 ) and (2) by comparing three categories of hospitals rather than individual hospitals. A second limitation concerns the quality of the data in the PMR sample.…”

Section: Limitationsmentioning

confidence: 99%

Method for developing national quality indicators based on manual data extraction from medical records

et al. 2012

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Developing quality indicators (QI) for national purposes (eg, public disclosure, paying-for-performance) highlights the need to find accessible and reliable data sources for collecting standardised data. The most accurate and reliable data source for collecting clinical and organisational information still remains the medical record. Data collection from electronic medical records (EMR) would be far less burdensome than from paper medical records (PMR). However, the development of EMRs is costly and has suffered from low rates of adoption and barriers of usability even in developed countries. Currently, methods for producing national QIs based on the medical record rely on manual extraction from PMRs.We propose and illustrate such a method. These QIs display feasibility, reliability and discriminative power, and can be used to compare hospitals. They have been implemented nationwide in France since 2006. The method used to develop these QIs could be adapted for use in large-scale programmes of hospital regulation in other, including developing, countries.

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Displaying random variation in comparing hospital performance

Cited by 24 publications

References 28 publications

Hospital performance based on treatment delays: comparison of ranking methods

Hospital performance based on treatment delays: comparison of ranking methods

Quality indicators for patients with traumatic brain injury in European intensive care units: a CENTER-TBI study

Method for developing national quality indicators based on manual data extraction from medical records

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