BACKGROUND: Self-management support is an important component of improving chronic care delivery. OBJECTIVE: To validate a new measure of self-management support and to characterize performance, including comparisons across chronic conditions. DESIGN, SETTING, PARTICIPANTS: We incorporated a new question module for self-management support within an existing annual statewide patient survey process in 2007. MEASUREMENTS: The survey identified 80,597 patients with a chronic illness on whom the new measure could be evaluated and compared with patients' experiences on four existing measures (quality of clinical interactions, coordination of care, organizational access, and office staff). We calculated Spearman correlation coefficients for self-management support scores for individual chronic conditions within each medical group. We fit multivariable logistic regression models to identify predictors of more favorable performance on self-management support. RESULTS: Composite scores of patient care experiences, including quality of clinical interactions (89.2), coordination of care (77.6), organizational access (76.3), and office staff (85.8) were higher than for the self-management support composite score (69.9). Self-management support scores were highest for patients with cancer (73.0) and lowest for patients with hypertension (67.5). The minimum sample size required for medical groups to provide a reliable estimate of self-management support was 199. There was no consistent correlation between self-management support scores for individual chronic conditions within medical groups. Increased involvement of additional members of the healthcare team was associated with higher self-management support scores across all chronic conditions. CONCLUSION: Measurement of self-management support is feasible and can identify gaps in care not currently included in standard measures of patient care experiences.
BACKGROUND: Self-management support is an important component of improving chronic care delivery. OBJECTIVE: To validate a new measure of self-management support and to characterize performance, including comparisons across chronic conditions. DESIGN, SETTING, PARTICIPANTS: We incorporated a new question module for self-management support within an existing annual statewide patient survey process in 2007. MEASUREMENTS: The survey identified 80,597 patients with a chronic illness on whom the new measure could be evaluated and compared with patients' experiences on four existing measures (quality of clinical interactions, coordination of care, organizational access, and office staff). We calculated Spearman correlation coefficients for self-management support scores for individual chronic conditions within each medical group. We fit multivariable logistic regression models to identify predictors of more favorable performance on self-management support. RESULTS: Composite scores of patient care experiences, including quality of clinical interactions (89.2), coordination of care (77.6), organizational access (76.3), and office staff (85.8) were higher than for the self-management support composite score (69.9). Self-management support scores were highest for patients with cancer (73.0) and lowest for patients with hypertension (67.5). The minimum sample size required for medical groups to provide a reliable estimate of self-management support was 199. There was no consistent correlation between self-management support scores for individual chronic conditions within medical groups. Increased involvement of additional members of the healthcare team was associated with higher self-management support scores across all chronic conditions. CONCLUSION: Measurement of self-management support is feasible and can identify gaps in care not currently included in standard measures of patient care experiences.
Authors: Carol A Brownson; Doriane Miller; Richard Crespo; Sally Neuner; Joan Thompson; Joseph C Wall; Seth Emont; Patricia Fazzone; Edwin B Fisher; Russell E Glasgow Journal: Jt Comm J Qual Patient Saf Date: 2007-07
Authors: Meredith B Rosenthal; Bruce E Landon; Sharon-Lise T Normand; Richard G Frank; Arnold M Epstein Journal: N Engl J Med Date: 2006-11-02 Impact factor: 91.245
Authors: Peter W Harvey; John N Petkov; Gary Misan; Jeffrey Fuller; Malcolm W Battersby; Teofilo N Cayetano; Kate Warren; Paul Holmes Journal: Aust Health Rev Date: 2008-05 Impact factor: 1.990
Authors: Christine Vogeli; Alexandra E Shields; Todd A Lee; Teresa B Gibson; William D Marder; Kevin B Weiss; David Blumenthal Journal: J Gen Intern Med Date: 2007-12 Impact factor: 5.128
Authors: Lucinda B Leung; Arturo Vargas-Bustamante; Ana E Martinez; Xiao Chen; Hector P Rodriguez Journal: Health Serv Res Date: 2016-10-21 Impact factor: 3.402
Authors: Anthony W Russell; Maria Donald; Samantha J Borg; Jianzhen Zhang; Letitia H Burridge; Robert S Ware; Nelufa Begum; H David McIntyre; Claire L Jackson Journal: Diabetologia Date: 2018-10-03 Impact factor: 10.122
Authors: Ellen H Chen; David H Thom; Danielle M Hessler; La Phengrasamy; Hali Hammer; George Saba; Thomas Bodenheimer Journal: J Gen Intern Med Date: 2010-09 Impact factor: 5.128
Authors: Esteban A Cedillo-Couvert; Jesse Y Hsu; Ana C Ricardo; Michael J Fischer; Ben S Gerber; Edward J Horwitz; John W Kusek; Eva Lustigova; Amada Renteria; Sylvia E Rosas; Milda Saunders; Daohang Sha; Anne Slaven; James P Lash Journal: Clin J Am Soc Nephrol Date: 2018-10-18 Impact factor: 8.237
Authors: Jianzhen Zhang; Letitia Burridge; Kimberley A Baxter; Maria Donald; Michele M Foster; Samantha A Hollingworth; Robert S Ware; Anthony W Russell; Claire L Jackson Journal: Trials Date: 2013-11-12 Impact factor: 2.279