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

Abstract

Background:

Adherence to evidence-based hospital stroke care is variable and may change over time. It is important to determine which process measures are associated with variation in outcome. In a large dataset, we analyzed the association between process and outcome and the fluctuations of indicators over time, and identified quality indicators (QIs) that should be prioritized for improving the quality of stroke care.

Methods:

We analyzed data from 123,259 patients diagnosed with acute ischemic stroke (AIS) who were treated at 109 large tertiary hospitals in China between January 2011 and May 2017. In total, 12 stroke treatment indicators were selected to calculate the hospital process composite performance (HPCP). Hospitals were divided into subgroups according to the time trend of HPCP 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 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). After adjusting for patient age, medical insurance, comorbidities, patterns of admission, and NIHSS-scores, patients in the high-HPCP group presented higher rate of independence and longer length of stay compared to the low-HPCP group. The multi-level model showed that there was a statistically significant difference in the utilization rate between the two groups, with most marked differences seen in emergency assessment and function evaluation indicators.

Conclusion:

Variation in the quality of stroke care exists across hospitals, and better adherence to guideline-based care is associated with improved outcomes. We found that QIs related to emergency examination and functional assessment were the main factors differing between good and poor adherers to stroke indicators, suggesting that quality improvement in stroke care could prioritize these QIs.

Keywords

Stroke care disparity time trend group-based model quality improvement quality indicators

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