***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background — Despite effective treatments to reduce cardiovascular disease risk , their translation into practice is limited . Methods and Results — Using a parallel arm cluster-r and omized controlled trial in 60 Australian primary healthcare centers , we tested whether a multifaceted quality improvement intervention comprising computerized decision support , audit/feedback tools , and staff training improved ( 1 ) guideline -indicated risk factor measurements and ( 2 ) guideline -indicated medications for those at high cardiovascular disease risk . Centers had to use a compatible software system , and eligible patients were regular attendees ( Aboriginal and Torres Strait Isl and er people aged ≥35 years and others aged ≥45 years ) . Patient-level analyses were conducted using generalized estimating equations to account for clustering . Median follow-up for 38 725 patients ( mean age , 61.0 years ; 42 % men ) was 17.5 months . Mean monthly staff support was < 1 hour/site . For the co primary outcomes , the intervention was associated with improved overall risk factor measurements ( 62.8 % versus 53.4 % risk ratio ; 1.25 ; 95 % confidence interval , 1.04–1.50 ; P=0.02 ) , but there was no significant differences in recommended prescriptions for the high-risk cohort ( n=10 308 ; 56.8 % versus 51.2 % ; P=0.12 ) . There were significant treatment escalations ( new prescriptions or increased numbers of medicines ) for antiplatelet ( 4.3 % versus 2.7 % ; P=0.01 ) , and BP lowering ( 18.2 % versus 11.0 % ; P=0.02 ) but not lipid-lowering medications . Conclusions — In Australian primary healthcare setting s , a computer-guided quality improvement intervention , requiring minimal support , improved cardiovascular disease risk measurement but did not increase prescription rates in the high-risk group . Computerized quality improvement tools offer an important , albeit partial , solution to improving primary healthcare system capacity for cardiovascular disease risk management . Clinical Trial Registration — URL : https://www.anzctr.org.au/Trial/ Registration /Trial Review .aspx?id=336630 . Australian New Zeal and Clinical Trials Registry No. 12611000478910 BACKGROUND The adherence to evidence -based treatment guidelines for acute myocardial infa rct ion ( AMI ) is still suboptimal . Therefore , we design ed a study to evaluate the effects of a collaborative quality improvement ( QI ) intervention on the adherence to AMI guidelines . The intervention used a national web-based quality registry to generate local and regular real-time performance feedback . METHODS A 12-month baseline measurement of the adherence rates was retrospectively collected , comprising the period July 1 , 2001 , through June 30 , 2002 . During the intervention period of November 1 , 2002 , through April 30 , 2003 , multidisciplinary teams from 19 nonr and omized intervention hospitals were subjected to a multifaceted QI-oriented intervention .  Another 19 hospitals , unaware of their status as controls , were matched to the intervention hospitals . During the postintervention measurement period of May 1 , 2003 , through April 30 , 2004 , a total of 6726 consecutive patients were included at the intervention ( n = 3786 ) and control ( n = 2940 ) hospitals . The outcome measures comprised 5 Swedish national guideline -derived quality indicators , compared between baseline and postintervention levels in the control and QUICC intervention hospitals . RESULTS In the control and QI intervention hospitals , the mean absolute increase of patients receiving angiotensin-converting enzyme inhibitors was 1.4 % vs 12.6 % ( P = .002 ) , lipid-lowering therapy 2.3 % vs 7.2 % ( P = .065 ) , clopidogrel 26.3 % vs 41.2 % ( P = .010 ) , heparin/low-molecular weight heparin 5.3 % vs 16.3 % ( P = .010 ) , and coronary angiography 6.2 % vs 16.8 % ( P = .027 ) , respectively . The number of QI intervention hospitals reaching a treatment level of at least 70 % in 4 or 5 of the 5 indicators was 15 and 5 , respectively . In the control group , no hospital reached 70 % or more in just 4 of the 5 indicators . CONCLUSIONS By combining a systematic and multidisciplinary QI collaborative with a web-based national quality registry with functionality allowing real-time performance feedback , major improvements in the adherence to national AMI guidelines can be achieved OBJECTIVE To examine whether the Acute Respiratory Infection ( ARI )  Quality Dashboard , an electronic health record (EHR)-based feedback system , changed antibiotic prescribing . STUDY DESIGN Cluster r and omized , controlled trial . METHODS We r and omly assigned 27 primary care practice s to receive the ARI Quality Dashboard or usual care .  The primary outcome was the intent-to-intervene antibiotic prescribing rate for ARI visits . We also compared antibiotic prescribing between ARI Quality Dashboard users and nonusers . RESULTS During the 9-month intervention , there was no difference between intervention and control practice s in antibiotic prescribing for all ARI visits ( 47 % vs 47 % ; P = .87 ) , antibiotic-appropriate ARI visits ( 65 % vs 64 % ; P = .68 ) , or non – antibiotic-appropriate ARI visits ( 38 % vs 40 % ; P = .70 ) . Among the 258 intervention clinicians , 72 ( 28 % ) used the ARI Quality Dashboard at least once . These clinicians had a lower overall ARI antibiotic prescribing rate ( 42 % vs 50 % for nonusers ; P = .02 ) . This difference was due to less antibiotic prescribing for non-antibiotic-appropriate ARIs ( 32 % vs 43 % ; P = .004 ) , including nonstreptococcal pharyngitis ( 31 % vs 41 % ; P = .01 ) and nonspecific upper respiratory infections ( 19 % vs 34 % ; P = .01 ) . CONCLUSIONS The ARI Quality Dashboard was not associated with an overall change in antibiotic prescribing for ARIs , although when used , it was associated with improved antibiotic prescribing . EHR-based quality reporting , as part of " meaningful use , " may not improve care in the absence of other changes to primary care practice RATIONALE AND OBJECTIVES The aim of this study was to describe the impact of a tailored Web-based educational program design ed to reduce excessive screening mammography recall . MATERIAL S AND METHODS Radiologists enrolled in one of four mammography registries in the United States were invited to take part and were r and omly assigned to receive the intervention or to serve as controls . The controls were offered the intervention at the end of the study , and data collection included an assessment of their clinical practice as well . The intervention provided each radiologist with individual audit data for his or her sensitivity , specificity , recall rate , positive predictive value , and cancer detection rate compared to national benchmarks and peer comparisons for the same measures ; profiled breast cancer risk in each radiologist 's respective patient population s to illustrate how low breast cancer risk is in population -based setting s ; and evaluated the possible impact of medical mal practice concerns on recall rates . Participants ' recall rates from actual practice were evaluated for three time periods : the 9 months before the intervention was delivered to the intervention group ( baseline period ) , the 9 months between the intervention and control groups ( T1 ) , and the 9 months after completion of the intervention by the controls ( T2 ) . Logistic regression models examining the probability that a mammogram was recalled included indication of intervention versus control and time period ( baseline , T1 , and T2 ) . Interactions between the groups and time period were also included to determine if the association between time period and the probability of a positive result differed across groups . RESULTS Thirty-one radiologists who completed the continuing medical education intervention were included in the adjusted model comparing radiologists in the  intervention  group ( n = 22 ) to radiologists who completed the intervention in the control group ( n = 9 ) . At T1 , the intervention group had 12 % higher odds of positive mammographic results compared to the controls , after controlling for baseline ( odds ratio , 1.12 ; 95 % confidence interval , 1.00 - 1.27 ; P = .0569 ) . At T2 , a similar association was found , but it was not statistically significant ( odds ratio , 1.10 ; 95 % confidence interval , 0.96 to 1.25 ) . No associations were found among radiologists in the control group when comparing those who completed the continuing medical education intervention ( n = 9 ) to those who did not ( n = 10 ) . In addition , no associations were found between time period and recall rate among radiologists who set realistic goals . CONCLUSIONS This study result ed in a null effect , which may indicate that a single 1-hour intervention is not adequate to change excessive recall among radiologists who undertook the intervention being tested Background Health professionals strive to deliver high- quality care in an inherently complex and error-prone environment . Underreporting of medical errors challenges attempts to underst and causative factors and impedes efforts to implement preventive strategies . Audit with feedback is a knowledge translation strategy that has potential to modify health professionals ’ medical error reporting behaviour . However , evidence regarding which aspects of this complex , multi-dimensional intervention work best is lacking . The aims of the Safe Medication Audit Reporting Translation ( SMART ) study are to:1 . Implement and refine a reporting mechanism to feed audit data on medication errors back to nurses2 . Test the feedback reporting mechanism to determine its utility and effect3 . Identify characteristics of organisational context associated with error reporting in response to feedback Methods / design A quasi-experimental design , incorporating two pairs of matched wards at an acute care hospital , is used . R and omisation occurs at the ward level ; one ward from each pair is r and omised to receive the intervention . A key stakeholder reference group informs the design and delivery of the feedback intervention . Nurses on the intervention wards receive the feedback intervention ( feedback of analysed audit data ) on a quarterly basis for 12 months . Data for the feedback intervention come from medication documentation point-prevalence audits and weekly reports on routinely collected medication error data . Weekly reports on these data are obtained for the control wards . A controlled interrupted time series analysis is used to evaluate the effect of the feedback intervention .  Self-report data are also collected from nurses on all four wards at baseline and at completion of the intervention to elicit their perceptions of the work context . Additionally , following each feedback cycle , nurses on the intervention wards are invited to complete a survey to evaluate the feedback and to establish their intentions to change their reporting behaviour . To assess sustainability of the intervention , at 6 months following completion of the intervention a point-prevalence chart audit is undertaken and a report of routinely collected medication errors for the previous 6 months is obtained . This intervention will have wider application for delivery of feedback to promote behaviour change for other areas of preventable error and adverse events Background Large gaps exist in the implementation of guideline recommendations for cardiovascular disease ( CVD ) risk management .  Electronic decision support ( EDS ) systems are promising interventions to close these gaps but few have undergone clinical trial evaluation in Australia . We have developed HealthTracker , a multifaceted EDS and quality improvement intervention to improve the management of CVD risk . Methods / design It is hypothesised that the use of HealthTracker over a 12-month period will result in : ( 1 ) an increased proportion of patients receiving guideline -indicated measurements of CVD risk factors and ( 2 ) an increased proportion of patients at high risk will receive guideline -indicated prescriptions for lowering their CVD risk . Sixty health services ( 40 general practice s and 20 Aboriginal Community Controlled Health Services ( ACCHSs ) will be r and omised in a 1:1 allocation to receive either the intervention package or continue with usual care , stratified by service type , size and participation in existing quality improvement initiatives . The intervention consists of point-of-care decision support ; a risk communication interface ; a clinical audit tool to assess performance on CVD-related indicators ; a quality improvement component comprising peer-ranked data feedback and support to develop strategies to improve performance .  The control arm will continue with usual care without access to these intervention components . Quantitative data will be derived from cross-sectional sample s at baseline and end of study via automated data extraction . Detailed process and economic evaluations will also be conducted . Ethics and dissemination The general practice component of the study is approved by the University of Sydney Human Research Ethics Committee ( HREC ) and the ACCHS component is approved by the Aboriginal Health and Medical Research Council HREC . Formal agreements with each of the participating sites have been signed . In addition to the usual scientific forums , results will be disseminated via newsletters , study websites , face-to-face feedback forums and workshops . Trial registration The trial is registered with the Australian Clinical Trials Registry ACTRN 12611000478910 Background Behavioural theory can be used to better underst and the effects of behaviour change interventions targeting healthcare professional behaviour to improve quality of care . However , the explicit use of theory is rarely reported despite interventions inevitably involving at least an implicit idea of what factors to target to implement change . There is a quality of care gap in the post-fracture investigation ( bone 
    Output:
    
Analyses identified the number of different domains coded in control arm to have the biggest role in heterogeneity in  e-A&F effect size .
Conclusions Given the high heterogeneity of identified studies , the effects of e-A&F were found to be highly variable .