Laura Staetsky

Understanding ethnic and other socio-demographic differences in patient experience of primary care: evidence from the English General Practice Patient Survey

Background Ethnic minorities and some other patient groups consistently report lower scores on patient surveys, but the reasons for this are unclear. This study examined whether low scores of ethnic minority and other socio-demographic groups reflect their concentration in poorly performing primary care practices, and whether any remaining differences are consistent across practices. Methods Using data from the 2009 English General Practice Patient Survey (2 163 456 respondents from 8267 general practices) this study examined associations between patient socio-demographic characteristics and 11 measures of patient-reported experience. Findings South Asian and Chinese patients, younger patients, and those in poor health reported a less positive primary care experience than White patients, older patients and those in better health. For doctor communication, about half of the overall difference associated with South Asian patients (ranging from −6 to −9 percentage points) could be explained by their concentration in practices with low scores, but the other half arose because they reported less positive experiences than White patients in the same practices. Practices varied considerably in the direction and extent of ethnic differences. In some practices ethnic minority patients reported better experience than White patients. Differences associated with gender, Black ethnicity and deprivation were small and inconsistent. Conclusion Substantial ethnic differences in patient experience exist in a national healthcare system providing universal coverage. Improving the experience of patients in low-scoring practices would not only improve the quality of care provided to their White patients but it would also substantially reduce ethnic group differences in patient experience. There were large variations in the experiences reported by ethnic minority patients in different practices: practices with high patient experience scores from ethnic minority patients could be studied as models for quality improvement.

Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey

Objectives Uncertainties exist about when and how best to adjust performance measures for case mix. Our aims are to quantify the impact of case-mix adjustment on practice-level scores in a national survey of patient experience, to identify why and when it may be useful to adjust for case mix, and to discuss unresolved policy issues regarding the use of case-mix adjustment in performance measurement in health care. Design/setting Secondary analysis of the 2009 English General Practice Patient Survey. Responses from 2 163 456 patients registered with 8267 primary care practices. Linear mixed effects models were used with practice included as a random effect and five case-mix variables (gender, age, race/ethnicity, deprivation, and self-reported health) as fixed effects. Main outcome measures Primary outcome was the impact of case-mix adjustment on practice-level means (adjusted minus unadjusted) and changes in practice percentile ranks for questions measuring patient experience in three domains of primary care: access; interpersonal care; anticipatory care planning, and overall satisfaction with primary care services. Results Depending on the survey measure selected, case-mix adjustment changed the rank of between 0.4% and 29.8% of practices by more than 10 percentile points. Adjusting for case-mix resulted in large increases in score for a small number of practices and small decreases in score for a larger number of practices. Practices with younger patients, more ethnic minority patients and patients living in more socio-economically deprived areas were more likely to gain from case-mix adjustment. Age and race/ethnicity were the most influential adjustors. Conclusions While its effect is modest for most practices, case-mix adjustment corrects significant underestimation of scores for a small proportion of practices serving vulnerable patients and may reduce the risk that providers would ‘cream-skim’ by not enrolling patients from vulnerable socio-demographic groups.

How can health care organizations be reliably compared?: Lessons from a national survey of patient experience.

BackgroundPatient experience is increasingly used to assess organizational performance, for example in public reporting or pay-for-performance schemes. Conventional approaches using 95% confidence intervals are commonly used to determine required survey samples or to report performance but these may result in unreliable organizational comparisons. MethodsWe analyzed data from 2.2 million patients who responded to the English 2009 General Practice Patient Survey, which included 45 patient experience questions nested within 6 different care domains (access, continuity of care, communication, anticipatory care planning, out-of-hours care, and overall care satisfaction). For each question, unadjusted and case-mix adjusted (for age, sex, and ethnicity) organization-level reliability, and intraclass correlation coefficients were calculated. ResultsMean responses per organization ranged from 23 to 256 for questions evaluating primary care practices, and from 1454 to 2758 for questions evaluating out-of-hours care organizations. Adjusted and unadjusted reliability values were similar. Twenty-six questions had excellent reliability (≥0.90). Seven nurse communication questions had very good reliability (≥0.85), but 3 anticipatory care planning questions had lower reliability (<0.70). Reliability was typically <0.70 for questions with <100 mean responses per practice, usually indicating questions which only a subset of patients were eligible to answer. Nine questions had both excellent reliability and high intraclass correlation coefficients (≥0.10) indicating both reliable measurement and substantial performance variability. ConclusionsHigh reliability is a necessary property of indicators used to compare health care organizations. Using the English General Practice Patient Survey as a case study, we show how reliability and intraclass correlation coefficients can be used to select measures to support robust organizational comparisons, and to design surveys that will both provide high-quality measurement and optimize survey costs.