Variations in achievement of evidence-based, high-impact quality indicators in general practice: An observational study

Abstract

BackgroundThere are widely recognised variations in the delivery and outcomes of healthcare but an incomplete understanding of their causes. There is a growing interest in using routinely collected ‘big data’ in the evaluation of healthcare. We developed a set of evidence-based ‘high impact’ quality indicators (QIs) for primary care and examined variations in achievement of these indicators using routinely collected data in the United Kingdom (UK).MethodsCross-sectional analysis of routinely collected, electronic primary care data from a sample of general practices in West Yorkshire, UK (n = 89). The QIs covered aspects of care (including processes and intermediate clinical outcomes) in relation to diabetes, hypertension, atrial fibrillation, myocardial infarction, chronic kidney disease (CKD) and ‘risky’ prescribing combinations. Regression models explored the impact of practice and patient characteristics. Clustering within practice was accounted for by including a random intercept for practice.ResultsMedian practice achievement of the QIs ranged from 43.2% (diabetes control) to 72.2% (blood pressure control in CKD). Considerable between-practice variation existed for all indicators: the difference between the highest and lowest performing practices was 26.3 percentage points for risky prescribing and 100 percentage points for anticoagulation in atrial fibrillation. Odds ratios associated with the random effects for practices emphasised this; there was a greater than ten-fold difference in the likelihood of achieving the hypertension indicator between the lowest and highest performing practices. Patient characteristics, in particular age, gender and comorbidity, were consistently but modestly associated with indicator achievement. Statistically significant practice characteristics were identified less frequently in adjusted models.ConclusionsDespite various policy and improvement initiatives, there are enduring inappropriate variations in the delivery of evidence-based care. Much of this variation is not explained by routinely collected patient or practice variables, and is likely to be attributable to differences in clinical and organisational behaviour.

Impact of achieving primary care targets in type 2 diabetes on health outcomes and healthcare costs

“…Nevertheless, it has been reported that patient case‐mix and deprivation do not explain the majority of variation in treatment target achievement . Previous studies, which found only modest associations between variations in GP practice and differences in patient and/or GP practice characteristics, suggest that the variation may be related, rather, to differences in local organization and health systems . For example, a meta‐analysis of strategies for quality improvement in diabetes care revealed that strategies targeting the organization of healthcare, such as changes in organization of primary healthcare team and facilitated relay of clinical information to clinicians, were more successful than strategies directed at healthcare professionals or patients .…”

“…1,20 Previous studies, which found only modest associations between variations in GP practice and differences in patient and/or GP practice characteristics, suggest that the variation may be related, rather, to differences in local organization and health systems. [21][22][23] For example, a meta-analysis of strategies for quality improvement in diabetes care revealed that strategies targeting the organization of healthcare, such as changes in organization of primary healthcare team and facilitated relay of clinical information to clinicians, were more successful than strategies directed at healthcare professionals or patients. 13 A qualitative systematic review identified the following barriers to achievement of effective management of T2D in primary care: firstly, uncertainties in professional roles and responsibilities as the result of changes in the structural boundaries between primary and secondary care; and secondly, lack of training in approaches to facilitate behavioural changes in patients.…”

Developing and evaluating packages to support implementation of quality indicators in general practice: the ASPIRE research programme, including two cluster RCTs

“…With seven covariates, and a large effect size (defined by a difference of at least 0.8 SDs), 46 60 practices would provide 94% power. 33 Median achievement of the risky prescribing indicator was 8.7%, although lower scores were indicative of fewer instances of risky prescribing and were, therefore, desirable. Considerable between-practice variation in achievement existed for all indicators: the difference between the highest and the lowest achievers was 26.3% for risky prescribing and 100% for anticoagulation in AF and BP control in CKD.…”

“…We selected seven of the QIs developed in WP1 based on likely scope for improvement and amenability to change (see table 1 of Willis et al 33 ). Four indicators focused on processes of care (e.g.…”

Timings for HbA1c testing in people with diabetes are associated with incentive payments: an analysis of UK primary care data

“…Arguably, monitoring HbA 1c more frequently may be clinically appropriate for some people with diabetes, particularly those who have had a recent medication change [6] or those with uncontrolled diabetes. Care delivery and HbA 1c testing may vary across GP practices [12] or regions of the UK [13], or differ according to an individual's characteristics or comorbidities [14,15].…”