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. 2008 Nov 18:8:77.
doi: 10.1186/1471-2288-8-77.

Improving quality indicator report cards through Bayesian modeling

Byron J Gajewski  1 , Jonathan D Mahnken, Nancy Dunton
Affiliations

Improving quality indicator report cards through Bayesian modeling

Byron J Gajewski et al. BMC Med Res Methodol. .

Abstract

Background: The National Database for Nursing Quality Indicators (NDNQI) was established in 1998 to assist hospitals in monitoring indicators of nursing quality (eg, falls and pressure ulcers). Hospitals participating in NDNQI transmit data from nursing units to an NDNQI data repository. Data are summarized and published in reports that allow participating facilities to compare the results for their units with those from other units across the nation. A disadvantage of this reporting scheme is that the sampling variability is not explicit. For example, suppose a small nursing unit that has 2 out of 10 (rate of 20%) patients with pressure ulcers. Should the nursing unit immediately undertake a quality improvement plan because of the rate difference from the national average (7%)?

Methods: In this paper, we propose approximating 95% credible intervals (CrIs) for unit-level data using statistical models that account for the variability in unit rates for report cards.

Results: Bayesian CrIs communicate the level of uncertainty of estimates more clearly to decision makers than other significance tests.

Conclusion: A benefit of this approach is that nursing units would be better able to distinguish problematic or beneficial trends from fluctuations likely due to chance.

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Figures

Figure 1
Figure 1
Prior distributions for three indicators using the Full and Approximate Bayesian Models.
Figure 2
Figure 2
Posterior distribution for four units' fall rates.
Figure 3
Figure 3
Posterior distribution for four units' PrU rates.
Figure 4
Figure 4
Posterior distribution for four units' for JE.
Figure 5
Figure 5
Comparison of methods for assessing significant units.
Figure 6
Figure 6
Q-Q plots of 10,000 simulations comparing "full" Bayesian approach to the "approx" Bayesian approach for sample sizes varying from 5 to 95.
See this image and copyright information in PMC

References

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