OBJECTIVE: To examine variation in culture change to a person-centered care (PCC) model, and the association between culture change and a composite measure of quality in 107 Department of Veterans Affairs nursing homes. METHODS: We examined the relationship between a composite quality measure calculated from 24 quality indicators (QIs) from the Minimum Data Set (that measure unfavorable events), and PCC summary scores calculated from the 6 domains of the Artifact of Culture Change Tool, using 3 different methods of calculating the summary scores. We also use a Bayesian hierarchical model to analyze the relationship between a latent construct measuring extent of culture change and the composite quality measure. RESULTS: Using the original Artifacts scores, the highest performing facility has a 2.9 times higher score than the lowest. There is a statistically significant relationship between the composite quality measure and each of the 3 summary Artifacts scores. Depending on whether original scores, standardized scores, or optimal scores are used, a facility at the 10th percentile in terms of culture change compared with one at the 90th percentile has 8.0%, 8.9%, or 10.3% more QI events. When PCC implementation is considered as a latent construct, 18 low performance PCC facilities have, on an average, 16.3% more QI events than 13 high performance facilities. CONCLUSIONS: Our results indicate that culture change to a PCC model is associated with higher Minimum Data Set-based quality. Longitudinal data are needed to better assess whether there is a causal relationship between the extent of culture change and quality.
OBJECTIVE: To examine variation in culture change to a person-centered care (PCC) model, and the association between culture change and a composite measure of quality in 107 Department of Veterans Affairs nursing homes. METHODS: We examined the relationship between a composite quality measure calculated from 24 quality indicators (QIs) from the Minimum Data Set (that measure unfavorable events), and PCC summary scores calculated from the 6 domains of the Artifact of Culture Change Tool, using 3 different methods of calculating the summary scores. We also use a Bayesian hierarchical model to analyze the relationship between a latent construct measuring extent of culture change and the composite quality measure. RESULTS: Using the original Artifacts scores, the highest performing facility has a 2.9 times higher score than the lowest. There is a statistically significant relationship between the composite quality measure and each of the 3 summary Artifacts scores. Depending on whether original scores, standardized scores, or optimal scores are used, a facility at the 10th percentile in terms of culture change compared with one at the 90th percentile has 8.0%, 8.9%, or 10.3% more QI events. When PCC implementation is considered as a latent construct, 18 low performance PCC facilities have, on an average, 16.3% more QI events than 13 high performance facilities. CONCLUSIONS: Our results indicate that culture change to a PCC model is associated with higher Minimum Data Set-based quality. Longitudinal data are needed to better assess whether there is a causal relationship between the extent of culture change and quality.
Authors: Christine W Hartmann; Michael Shwartz; Shibei Zhao; Jennifer A Palmer; Dan R Berlowitz Journal: J Am Geriatr Soc Date: 2016-01 Impact factor: 5.562
Authors: Jennifer L Sullivan; Michael Shwartz; Kelly Stolzmann; Melissa K Afable; James F Burgess Journal: Health Serv Res Date: 2017-03-28 Impact factor: 3.402
Authors: Karl Swedberg; Desmond Cawley; Inger Ekman; Heather L Rogers; Darijana Antonic; Daiga Behmane; Ida Björkman; Nicky Britten; Sandra C Buttigieg; Vivienne Byers; Mats Börjesson; Kirsten Corazzini; Andreas Fors; Bradi Granger; Boban Joksimoski; Roman Lewandowski; Virgilijus Sakalauskas; Einav Srulovici; Jan Törnell; Sara Wallström; Axel Wolf; Helen M Lloyd Journal: Health Sci Rep Date: 2021-06-06
Authors: Anne E Sales; Mary Ersek; Orna K Intrator; Cari Levy; Joan G Carpenter; Robert Hogikyan; Helen C Kales; Zach Landis-Lewis; Tobie Olsan; Susan C Miller; Marcos Montagnini; Vyjeyanthi S Periyakoil; Sheri Reder Journal: Implement Sci Date: 2016-09-29 Impact factor: 7.327