Why the study?
Does the use of survival curves to inform quality improvement efforts reduce 30-day mortality in hospitals with excess mortality after AMI, stroke, and hip fracture?
Does the use of survival curves to inform quality improvement efforts reduce 30-day mortality in hospitals with excess mortality after AMI, stroke, and hip fracture?
Using survival curves to supplement 30-day mortality data can help identify suboptimal care phases and guide quality improvement projects to reduce excess hospital mortality.
Survival curve monitoring was associated with normalized hospital mortality; leaves open whether this extends to causal QI benefit in prospective trials.
OBJECTIVES: To evaluate survival curves (Kaplan-Meier) as a means of identifying areas in the clinical pathway amenable to quality improvement. DESIGN: Observational before-after study. SETTING: In Norway, annual public reporting of nationwide 30-day in-and-out-of-hospital mortality (30D) for three medical conditions started in 2011: first time acute myocardial infarction (AMI), stroke and hip fracture; reported for 2009. 12 of 61 hospitals had statistically significant lower/higher mortality compared with the hospital mean. PARTICIPANTS: Three hospitals with significantly higher mortality requested detailed analyses for quality improvement purposes: Telemark Hospital Trust Skien (AMI and stroke), Østfold Hospital Trust Fredrikstad (stroke), Innlandet Hospital Trust Gjøvik (hip fracture). OUTCOME MEASURES: Survival curves, crude and risk-adjusted 30D before (2008-2009) and after (2012-2013). INTERVENTIONS: Unadjusted survival curves for the outlier hospitals were compared to curves based on pooled data from the other hospitals for the 30-day period 2008-2009. For patients admitted with AMI (Skien), stroke (Fredrikstad) and hip fracture (Gjøvik), the curves suggested increased mortality from the initial part of the clinical pathway. For stroke (Skien), increased mortality appeared after about 8 days. The curve profiles were thought to reflect suboptimal care in various phases in the clinical pathway. This informed improvement efforts. RESULTS: For 2008-2009, hospital-specific curves differed from other hospitals: borderline significant for AMI (p=0.064), highly significant (p≤0.005) for the remainder. After intervention, no difference was found (p>0.188). Before-after comparison of the curves within each hospital revealed a significant change for Fredrikstad (p=0.006). For the three hospitals, crude 30D declined and they were non-outliers for risk-adjusted 30D for 2013. CONCLUSIONS: Survival curves as a supplement to 30D may be useful for identifying suboptimal care in the clinical pathway, and thus informing design of quality improvement projects.
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Kristoffersen et al. (2015) studied this question.
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