Evaluating the Impact of Accreditation Using Interrupted Time Series Segmented Regression Analysis: A Reflective Analysis

2020 ◽  
Author(s):  
Subashnie Devkaran
2019 ◽  
Vol 70 (691) ◽  
pp. e146-e154
Author(s):  
Sharon L Cadogan ◽  
John P Browne ◽  
Colin P Bradley ◽  
Anthony P Fitzgerald ◽  
Mary R Cahill

BackgroundImplementation science experts recommend that theory-based strategies, developed in collaboration with healthcare professionals, have greater chance of success.AimThis study evaluated the impact of a theory-based strategy for optimising the use of serum immunoglobulin testing in primary care.Design and settingAn interrupted time series with segmented regression analysis in the Cork–Kerry region, Ireland. An intervention was devised comprising a guideline and educational messages-based strategy targeting previously identified GP concerns relevant to testing for serum immunoglobulins.MethodInterrupted time series with segmented regression analysis was conducted to evaluate the intervention, using routine laboratory data from January 2012 to October 2016. Data were organised into fortnightly segments (96 time points pre-intervention and 26 post-intervention) and analysed using incidence rate ratios with their corresponding 95% confidence intervals.ResultsIn the most parsimonious model, the change in trend before and after the introduction of the intervention was statistically significant. In the 1-year period following the implementation of the strategy, test orders were falling at a rate of 0.42% per fortnight (P<0.001), with an absolute reduction of 0.59% per fortnight, corresponding to a reduction of 14.5% over the 12-month study period.ConclusionThe authors’ tailored guideline combined with educational messages reduced serum immunoglobulin test ordering in primary care over a 1-year period. Given the rarity of the conditions for which the test is utilised and the fact that the researchers had only population-level data, further investigation is required to examine the clinical implications of this change in test-ordering patterns.


2018 ◽  
Vol 69 (2) ◽  
pp. 227-232 ◽  
Author(s):  
Violeta Balinskaite ◽  
Alan P Johnson ◽  
Alison Holmes ◽  
Paul Aylin

Abstract Background The Quality Premium was introduced in 2015 to financially reward local commissioners of healthcare in England for targeted reductions in antibiotic prescribing in primary care. Methods We used a national antibiotic prescribing dataset from April 2013 until February 2017 to examine the number of antibiotic items prescribed, the total number of antibiotic items prescribed per STAR-PU (specific therapeutic group age/sex-related prescribing units), the number of broad-spectrum antibiotic items prescribed, and broad-spectrum antibiotic items prescribed, expressed as a percentage of the total number of antibiotic items. To evaluate the impact of the Quality Premium on antibiotic prescribing, we used a segmented regression analysis of interrupted time series data. Results During the study period, over 140 million antibiotic items were prescribed in primary care. Following the introduction of the Quality Premium, antibiotic items prescribed decreased by 8.2%, representing 5933563 fewer antibiotic items prescribed during the 23 post-intervention months, as compared with the expected numbers based on the trend in the pre-intervention period. After adjusting for the age and sex distribution in the population, the segmented regression model also showed a significant relative decrease in antibiotic items prescribed per STAR-PU. A similar effect was found for broad-spectrum antibiotics (comprising 10.1% of total antibiotic prescribing), with an 18.9% reduction in prescribing. Conclusions This study shows that the introduction of financial incentives for local commissioners of healthcare to improve the quality of prescribing was associated with a significant reduction in both total and broad-spectrum antibiotic prescribing in primary care in England.


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