BACKGROUND AND AIMS: Accurately documenting mammography use is essential to assess quality of care for early breast cancer detection in underserved populations. Self-reports and medical record reports frequently result in different accounts of whether a mammogram was performed. We hypothesize that electronic medical records (EMRs) provide more accurate documentation of mammography use than paper records, as evidenced by the level of agreement between women's self-reported mammography use and mammography use documented in medical records. METHODS: Black women aged 40-75 were surveyed in six primary care sites in Boston, Massachusetts (n = 411). Survey data assessed self-reported mammography prevalence within 2 years of study entry. Corresponding medical record data were collected at each site. Positive predictive value (PPV) of self-report and kappa statistics compared data agreement among sites with and without EMRs. Logistic regression estimated effects of site and patient characteristics on agreement between data sources. RESULTS: Medical records estimated a lower prevalence of mammography use (58%) than self-report (76%). However, self-report and medical record estimates were more similar in sites with EMRs. PPV of self-report was 88% in sites with continuous access to EMRs and 61% at sites without EMRs. Kappa statistics indicated greater data agreement at sites with EMRs (0.72, 95% CI 0.56-0.88) than without EMRs (0.46, 95% CI 0.29-0.64). Adjusted for covariates, odds of data agreement were greatest in sites where EMRs were available during the entire study period (OR 4.31, 95% CI 1.67-11.13). CONCLUSIONS: Primary care sites with EMRs better document mammography use than those with paper records. Patient self-report of mammography screening is more accurate at sites with EMRs. Broader access to EMRs should be implemented to improve quality of documenting mammography use. At a minimum, quality improvement efforts should confirm the accuracy of paper records with supplemental data.
BACKGROUND AND AIMS: Accurately documenting mammography use is essential to assess quality of care for early breast cancer detection in underserved populations. Self-reports and medical record reports frequently result in different accounts of whether a mammogram was performed. We hypothesize that electronic medical records (EMRs) provide more accurate documentation of mammography use than paper records, as evidenced by the level of agreement between women's self-reported mammography use and mammography use documented in medical records. METHODS: Black women aged 40-75 were surveyed in six primary care sites in Boston, Massachusetts (n = 411). Survey data assessed self-reported mammography prevalence within 2 years of study entry. Corresponding medical record data were collected at each site. Positive predictive value (PPV) of self-report and kappa statistics compared data agreement among sites with and without EMRs. Logistic regression estimated effects of site and patient characteristics on agreement between data sources. RESULTS: Medical records estimated a lower prevalence of mammography use (58%) than self-report (76%). However, self-report and medical record estimates were more similar in sites with EMRs. PPV of self-report was 88% in sites with continuous access to EMRs and 61% at sites without EMRs. Kappa statistics indicated greater data agreement at sites with EMRs (0.72, 95% CI 0.56-0.88) than without EMRs (0.46, 95% CI 0.29-0.64). Adjusted for covariates, odds of data agreement were greatest in sites where EMRs were available during the entire study period (OR 4.31, 95% CI 1.67-11.13). CONCLUSIONS: Primary care sites with EMRs better document mammography use than those with paper records. Patient self-report of mammography screening is more accurate at sites with EMRs. Broader access to EMRs should be implemented to improve quality of documenting mammography use. At a minimum, quality improvement efforts should confirm the accuracy of paper records with supplemental data.
Authors: Kristi L Allgood; Garth H Rauscher; Steven Whitman; Giselle Vasquez-Jones; Ami M Shah Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-05-23 Impact factor: 4.254
Authors: Kyle J Muus; Twyla B Baker-Demaray; T Andy Bogart; Glen E Duncan; Clemma Jacobsen; Dedra S Buchwald; Jeffrey A Henderson Journal: J Rural Health Date: 2011-09-20 Impact factor: 4.333
Authors: Namita L Tundia; Christina M L Kelton; Teresa M Cavanaugh; Jeff J Guo; Dennis J Hanseman; Pamela C Heaton Journal: J Am Med Inform Assoc Date: 2012-10-09 Impact factor: 4.497