Identifying Performance Outliers for Stroke Care Based on Composite Score of Process Indicators: an Observational Study in China

Wang, Chao; Su, Shaofei; Li, Xi; Li, Jingkun; Bao, Xiaoqiang; Liu, Meina

doi:10.1007/s11606-020-05923-x

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…In addition, the multidisciplinary nature of treatment makes assessment with single indicator complicated. 5,9 Depending on the two sets of trajectories exhibited by the HPCP over time, process-outcome associations were considered; we observed key indicators that influence the distribution of care quality. Although this was the only data-based estimate, it does illustrate the differences in care for AIS, which may provide evidence for rational selection of QIs for performance improvement.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…4 Process-based QIs have also been invaluable in discriminating quality of care. 2,5 The standardization and integrity of clinical care has an important implication for patient outcomes. However, it is unclear about the quality measures that should be prioritized for improving inter-hospital variation in care quality of stroke.…”

Section: Introductionmentioning

confidence: 99%

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The quality disparity of stroke care over time: An analysis based on the national dataset from 2011 to 2017

Chen

Wang

et al. 2022

International Journal of Stroke

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Background: Hospital adherence to evidence-based stroke care is variable and changes over time. We take full account of these variations and determined indicators which could reduce adverse outcomes and narrow inter-hospital variation in stroke patients under the current protocol of clinical treatment for stroke. Methods: We analyzed data from 123259 patients diagnosed with acute ischemic stroke (AIS) who were treated at 109 large tertiary hospitals from January 2011 to May 2017. Hospitals were divided into subgroups according to the time trend of hospital process composite performance (HPCP, that were calculated by the method of opportunity-based score on patient-level) estimated by the Group Based Model. We analyzed the influence of hospital subgroups on the patient outcomes using a multi-level model and explored the quality indicators (QIs) that led to variation. Results: The HPCP trends for stroke indicators of 109 hospitals over 7 years were divided into two groups (Group 1, low-HPCP; Group 2, high-HPCP). Patients in Group 2 tended to have a higher rate of independence (modified Rankin Scale≤2). The multi-level model showed that there was a statistically significant difference in the utilization rate between the two groups, which mainly focused on the emergency examination and function evaluation indicators. Conclusions: Variation in the quality of stroke care exists across hospitals. We found that QIs related to emergency examination and functional assessment were the main factors in different trends of stroke HPCP over time and identifying hospital care differences, suggesting that quality improvement in stroke care could prioritize these QIs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The quality disparity of stroke care over time: An analysis based on the national dataset from 2011 to 2017

Chen

Wang

et al. 2022

International Journal of Stroke

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“…As starting point, we assume that a binary quality indicator is used to measure a certain aspect of the quality of care provided by a hospital (or some other healthcare provider), for which data is collected and analyzed. Typical examples include the adherence to specified processes (12,13) or outcome indicators such as 30-day mortality after some procedure (14). Data is collected for a pre-specified time period and the population of interest are all patients fulfilling certain inclusion criteria defined for the indicator within the selected time period.…”

Section: Methodsmentioning

confidence: 99%

“…In the pathway of external reporting, measurements of quality of care are used to reveal and compare the performance of hospitals (4). In general, external reporting and the classification of hospital performances can help to guide patient decision regarding their hospital choice (19) or support decisions for financial rewards or punishment (3,13). For simplicity, we focus in the following on external reporting with binary consequences such as accreditation of hospitals or selection for a financial reward or punishment and thus assume that the utilities are independent from the underlying quality of care of the hospital, θ.…”

Section: External Reportingmentioning

confidence: 99%

Hospital profiling using Bayesian decision theory

Hengelbrock

Rauh

Cederbaum

et al. 2021

Preprint

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Background For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. We study a setting where the decision to classify hospitals as performance outliers or non-outliers is based on the observed result of a single binary quality indicator. Methods We propose to embed the classification of providers into a Bayesian decision theoretical framework which enables the derivation of optimal decision rules with respect to the expected decision consequences. We argue that these consequences depend upon for which pathway to quality improvement the profiling of hospitals takes place. We propose paradigmatic utility functions for the two pathways external reporting and change in care delivery and compare the resulting optimal decision rules with regard to their threshold values, sensitivity and specificity. We further apply them to the area of hip replacement surgeries, for which we re-evaluate hospital performances for five quality indicators in Germany during 2017. Results Based on the utilities we assigned to the classification decisions, the decision rule for change in care delivery classifies more high-volume providers as outliers compared to the decision rule for external reporting, with consequences for both sensitivity and specificity. The re-evaluation of the five quality indicators illustrates that classification decisions are highly dependent upon the underlying utilities. Conclusion Analyzing the classification of hospitals as a decision theoretic problem and considering pathway-specific consequences of decisions can help to derive an appropriate decision rule. Contrasting decision rules with regard to their underlying assumptions about the utilities of classification consequences can be helpful to make implicit assumptions transparent and justifiable.

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Effect of admission time of arrival on quality of in-hospital care in acute ischemic stroke patients in China

Liu,

Wang,

et al. 2024

Public Health

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Identifying Performance Outliers for Stroke Care Based on Composite Score of Process Indicators: an Observational Study in China

Cited by 7 publications

References 29 publications

The quality disparity of stroke care over time: An analysis based on the national dataset from 2011 to 2017

The quality disparity of stroke care over time: An analysis based on the national dataset from 2011 to 2017

Hospital profiling using Bayesian decision theory

Effect of admission time of arrival on quality of in-hospital care in acute ischemic stroke patients in China

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