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Qual Saf Health Care. 2004 February; 13(1): 26–31.
PMCID: PMC1758048

Data feedback efforts in quality improvement: lessons learned from US hospitals

Abstract

Background: Data feedback is a fundamental component of quality improvement efforts, but previous studies provide mixed results on its effectiveness. This study illustrates the diversity of hospital based efforts at data feedback and highlights successful strategies and common pitfalls in designing and implementing data feedback to support performance improvement.

Methods: Open ended interviews with 45 clinical and administrative staff in eight US hospitals in 2000 concerning their perceptions about the effectiveness of data feedback in supporting performance improvement efforts were analysed. The hospitals were chosen to represent a range of sizes, geographical regions, and ß blocker improvement rates over a 3 year period. Data were organized and analyzed in NUD-IST 4 using the constant comparative method of qualitative data analysis.

Results: Although the data feedback efforts at the hospitals were diverse, the interviews suggested that seven key themes may be important: (1) data must be perceived by physicians as valid to motivate change; (2) it takes time to develop the credibility of data within a hospital; (3) the source and timeliness of data are critical to perceived validity; (4) benchmarking improves the meaningfulness of data feedback; (5) physician leaders can enhance the effectiveness of data feedback; (6) data feedback that profiles an individual physician's practices can be effective but may be perceived as punitive; (7) data feedback must persist to sustain improved performance. Embedded in several themes was the view that the effectiveness of data feedback depends not only on the quality and timeliness of the data, but also on the organizational context in which such efforts are implemented.

Conclusions: Data feedback is a complex and textured concept. Data feedback strategies that might be most effective are suggested, as well as potential pitfalls in using data to promote performance improvement.

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Selected References

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