A cluster randomised stepped wedge trial to evaluate the effectiveness of a multifaceted information technology-based intervention in reducing high-risk prescribing of non-steroidal anti-inflammatory drugs and antiplatelets in primary medical care: the DQIP study protocol.
ABSTRACT: BACKGROUND:High-risk prescribing of non-steroidal anti-inflammatory drugs (NSAIDs) and antiplatelet agents accounts for a significant proportion of hospital admissions due to preventable adverse drug events. The recently completed PINCER trial has demonstrated that a one-off pharmacist-led information technology (IT)-based intervention can significantly reduce high-risk prescribing in primary care, but there is evidence that effects decrease over time and employing additional pharmacists to facilitate change may not be sustainable. METHODS/DESIGN:We will conduct a cluster randomised controlled with a stepped wedge design in 40 volunteer general practices in two Scottish health boards. Eligible practices are those that are using the INPS Vision clinical IT system, and have agreed to have relevant medication-related data to be automatically extracted from their electronic medical records. All practices (clusters) that agree to take part will receive the data-driven quality improvement in primary care (DQIP) intervention, but will be randomised to one of 10 start dates. The DQIP intervention has three components: a web-based informatics tool that provides weekly updated feedback of targeted prescribing at practice level, prompts the review of individual patients affected, and summarises each patient's relevant risk factors and prescribing; an outreach visit providing education on targeted prescribing and training in the use of the informatics tool; and a fixed payment of 350 GBP (560 USD; 403 EUR) up front and a small payment of 15 GBP (24 USD; 17 EUR) for each patient reviewed in the 12 months of the intervention. We hypothesise that the DQIP intervention will reduce a composite of nine previously validated measures of high-risk prescribing. Due to the nature of the intervention, it is not possible to blind practices, the core research team, or the data analyst. However, outcome assessment is entirely objective and automated. There will additionally be a process and economic evaluation alongside the main trial. DISCUSSION:The DQIP intervention is an example of a potentially sustainable safety improvement intervention that builds on the existing National Health Service IT-infrastructure to facilitate systematic management of high-risk prescribing by existing practice staff. Although the focus in this trial is on Non-steroidal anti-inflammatory drugs and antiplatelets, we anticipate that the tested intervention would be generalisable to other types of prescribing if shown to be effective. TRIAL REGISTRATION:ClinicalTrials.gov, dossier number: NCT01425502.
Project description:OBJECTIVES:The cluster randomised trial of the Data-driven Quality Improvement in Primary Care (DQIP) intervention showed that education, informatics and financial incentives for general medical practices to review patients with ongoing high-risk prescribing of non-steroidal anti-inflammatory drugs and antiplatelets reduced the primary end point of high-risk prescribing by 37%, where both ongoing and new high-risk prescribing were significantly reduced. This quantitative process evaluation examined practice factors associated with (1) participation in the DQIP trial, (2) review activity (extent and nature of documented reviews) and (3) practice level effectiveness (relative reductions in the primary end point). SETTING/PARTICIPANTS:Invited practices recruited (n=33) and not recruited (n=32) to the DQIP trial in Scotland, UK. OUTCOME MEASURES:(1) Characteristics of recruited versus non-recruited practices. Associations of (2) practice characteristics and 'adoption' (self-reported implementation work done by practices) with documented review activity and (3) of practice characteristics, DQIP adoption and review activity with effectiveness. RESULTS:(1) Recruited practices had lower performance in the quality and outcomes framework than those declining participation. (2) Not being an approved general practitioner training practice and higher self-reported adoption were significantly associated with higher review activity. (3) Effectiveness ranged from a relative increase in high-risk prescribing of 24.1% to a relative reduction of 77.2%. High-risk prescribing and DQIP adoption (but not documented review activity) were significantly associated with greater effectiveness in the final multivariate model, explaining 64.0% of variation in effectiveness. CONCLUSIONS:Intervention implementation and effectiveness of the DQIP intervention varied substantially between practices. Although the DQIP intervention primarily targeted review of ongoing high-risk prescribing, the finding that self-reported DQIP adoption was a stronger predictor of effectiveness than documented review activity supports that reducing initiation and/or re-initiation of high-risk prescribing is key to its effectiveness. TRIAL REGISTRATION NUMBER:NCT01425502; Post-results.
Project description:OBJECTIVES:The quality and safety of drug therapy in primary care are global concerns. The Pharmacist and Data-Driven Quality Improvement in Primary Care (P-DQIP) intervention aims to improve prescribing safety via an informatics tool, which facilitates proactive management of drug therapy risks (DTRs) by health-board employed pharmacists with established roles in general practices. Study objectives were (1) to identify and prioritise factors that could influence P-DQIP implementation from the perspective of practice pharmacists and (2) to identify potentially effective, acceptable and feasible strategies to support P-DQIP implementation. DESIGN:Semistructured face-to-face interviews using a Theoretical Domains Framework informed topic guide. The framework method was used for data analysis. Identified implementation factors were prioritised for intervention based on research team consensus. Candidate intervention functions, behavioural change techniques (BCTs) and policies targeting these were identified from the behavioural change wheel. The final intervention content and modes of delivery were agreed with local senior pharmacists. SETTING:General practices from three Health and Social Care Partnerships in National Health Service (NHS) Tayside. PARTICIPANTS:14 NHS employed practice pharmacists. RESULTS:Identified implementation factors were linked to thirteen theoretical domains (all except intentions) and six (skill, memory/attention/decision making, behavioural regulation, reinforcement, environmental context/resources, social influences) were prioritised. Three intervention functions (training, enablement and environmental restructuring) were relevant and were served by two policy categories (guidelines, communication/marketing) and eight BCTs (instructions on how to perform a behaviour, problem solving, action planning, prompt/cues, goal setting, self-monitoring, feedback and restructuring the social environment). Intervention components encompass an informatics tool, written educational material, a workshop for pharmacists, promotional activities and small financial incentives. CONCLUSIONS:This study explored pharmacists' perceptions of implementation factors which could influence management of DTRs in general practices to inform implementation of P-DQIP, which will initially be implemented in one Scottish health board with parallel evaluation of effectiveness and implementation.
Project description:(A) To measure the extent to which different candidate outcome measures identified high-risk prescribing that is potentially changeable by the data-driven quality improvement in primary care (DQIP) intervention.(B) To explore the value of reviewing identified high-risk prescribing to clinicians.(C) To optimise the components of the DQIP intervention.Mixed method study.General practices in two Scottish Health boards.4 purposively sampled general practices of varying size and socioeconomic deprivation.Prescribing measures targeting (1) high-risk use of the non-steroidal anti-inflammatory drugs (NSAIDs) and antiplatelets; (2) 'Asthma control' and (3) 'Antithrombotics in atrial fibrillation (AF)'.The prescribing measures were used to identify patients for review by general practices. The ability of the measures to identify potentially changeable high-risk prescribing was measured as the proportion of patients reviewed where practices identified a need for action. Field notes were recorded from meetings between researchers and staff and key staff participated in semistructured interviews exploring their experience of the piloted intervention processes.Practices identified a need for action in 68%, 25% and 18% of patients reviewed for prescribing measures (1), (2) and (3), respectively. General practitioners valued being prompted to review patients, and perceived that (1) 'NSAID and antiplatelet' and (2) 'antithrombotics in AF' were the most important to act on. Barriers to initial and ongoing engagement and to sustaining improvements in prescribing were identified.'NSAIDs and antiplatelets' measures were selected as the most suitable outcome measures for the DQIP trial, based on evidence of this prescribing being more easily changeable. In response to the barriers identified, the intervention was designed to include a financial incentive, additional ongoing feedback on progress and reprompting review of patients, whose high-risk prescribing was restarted after a decision to stop.Clinicaltrials.gov NCT01425502.
Project description:INTRODUCTION:In light of the prospective Prenatal Assessment of Genomes and Exomes (PAGE) study, this paper aimed to determine the additional costs of using exome sequencing (ES) alongside or in place of chromosomal microarray (CMA) in a fetus with an identified congenital anomaly. METHODS:A decision tree was populated using data from a prospective cohort of women undergoing invasive diagnostic testing. Four testing strategies were evaluated: CMA, ES, CMA followed by ES ("stepwise"); CMA and ES combined. RESULTS:When ES is priced at GBP 2,100 (EUR 2,407/USD 2,694), performing ES alone prenatally would cost a further GBP 31,410 (EUR 36,001/USD 40,289) per additional genetic diagnosis, whereas the stepwise would cost a further GBP 24,657 (EUR 28,261/USD 31,627) per additional genetic diagnosis. When ES is priced at GBP 966 (EUR 1,107/USD 1,239), performing ES alone prenatally would cost a further GBP 11,532 (EUR 13,217/USD 14,792) per additional genetic diagnosis, whereas the stepwise would cost a further additional GBP 11,639 (EUR 13,340/USD 14,929) per additional genetic diagnosis. The sub-group analysis suggests that performing stepwise on cases indicative of multiple anomalies at ultrasound scan (USS) compared to cases indicative of a single anomaly, is more cost-effective compared to using ES alone. DISCUSSION/CONCLUSION:Performing ES alongside CMA is more cost-effective than ES alone, which can potentially lead to improvements in pregnancy management. The direct effects of test results on pregnancy outcomes were not examined; therefore, further research is recommended to examine changes on the projected incremental cost-effectiveness ratios.
Project description:Two to 4% of emergency hospital admissions are caused by preventable adverse drug events. The estimated costs of such avoidable admissions in England were £530 million in 2015. The data-driven quality improvement in primary care (DQIP) intervention was designed to prompt review of patients vulnerable from currently prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and anti-platelets and was found to be effective at reducing this prescribing. A process evaluation was conducted parallel to the trial, and this paper reports the analysis which aimed to explore response to the intervention delivered to clusters in relation to participants' perceptions about which intervention elements were active in changing their practice.Data generation was by in-depth interview with key staff exploring participant's perceptions of the intervention components. Analysis was iterative using the framework technique and drawing on normalisation process theory.All the primary components of the intervention were perceived as active, but at different stages of implementation: financial incentives primarily supported recruitment; education motivated the GPs to initiate implementation; the informatics tool facilitated sustained implementation. Participants perceived the primary components as interdependent. Intervention subcomponents also varied in whether and when they were active. For example, run charts providing feedback of change in prescribing over time were ignored in the informatics tool, but were motivating in some practices in the regular e-mailed newsletter. The high-risk NSAID and anti-platelet prescribing targeted was accepted as important by all interviewees, and this shared understanding was a key wider context underlying intervention effectiveness.This was a novel use of process evaluation data which examined whether and how the individual intervention components were effective from the perspective of the professionals delivering changed care to patients. These findings are important for reproducibility and roll-out of the intervention.ClinicalTrials.gov, NCT01425502 .
Project description:Trials of complex interventions are criticized for being 'black box', so the UK Medical Research Council recommends carrying out a process evaluation to explain the trial findings. We believe it is good practice to pre-specify and publish process evaluation protocols to set standards and minimize bias. Unlike protocols for trials, little guidance or standards exist for the reporting of process evaluations. This paper presents the mixed-method process evaluation protocol of a cluster randomized trial, drawing on a framework designed by the authors.This mixed-method evaluation is based on four research questions and maps data collection to a logic model of how the data-driven quality improvement in primary care (DQIP) intervention is expected to work. Data collection will be predominately by qualitative case studies in eight to ten of the trial practices, focus groups with patients affected by the intervention and quantitative analysis of routine practice data, trial outcome and questionnaire data and data from the DQIP intervention.We believe that pre-specifying the intentions of a process evaluation can help to minimize bias arising from potentially misleading post-hoc analysis. We recognize it is also important to retain flexibility to examine the unexpected and the unintended. From that perspective, a mixed-methods evaluation allows the combination of exploratory and flexible qualitative work, and more pre-specified quantitative analysis, with each method contributing to the design, implementation and interpretation of the other.As well as strengthening the study the authors hope to stimulate discussion among their academic colleagues about publishing protocols for evaluations of randomized trials of complex interventions. DATA-DRIVEN QUALITY IMPROVEMENT IN PRIMARY CARE TRIAL REGISTRATION: ClinicalTrials.gov: NCT01425502.
Project description:BACKGROUND:Dentists prescribe approximately 10% of antibiotics dispensed in UK community pharmacies. Despite clear clinical guidance, dentists often prescribe antibiotics inappropriately. This cluster-randomised controlled trial used routinely collected National Health Service (NHS) dental prescribing and treatment claim data to compare the impact of individualised audit and feedback (A&F) interventions on dentists' antibiotic prescribing rates. METHODS AND FINDINGS:All 795 antibiotic prescribing NHS general dental practices in Scotland were included. Practices were randomised to the control (practices = 163; dentists = 567) or A&F intervention group (practices = 632; dentists = 1,999). A&F intervention practices were allocated to one of two A&F groups: (1) individualised graphical A&F comprising a line graph plotting an individual dentist's monthly antibiotic prescribing rate (practices = 316; dentists = 1,001); or (2) individualised graphical A&F plus a written behaviour change message synthesising and reiterating national guidance recommendations for dental antibiotic prescribing (practices = 316; dentists = 998). Intervention practices were also simultaneously randomised to receive A&F: (i) with or without a health board comparator comprising the addition of a line to the graphical A&F plotting the monthly antibiotic prescribing rate of all dentists in the health board; and (ii) delivered at 0 and 6 mo or at 0, 6, and 9 mo, giving a total of eight intervention groups. The primary outcome, measured by the trial statistician who was blinded to allocation, was the total number of antibiotic items dispensed per 100 NHS treatment claims over the 12 mo post-delivery of the baseline A&F. Primary outcome data was available for 152 control practices (dentists = 438) and 609 intervention practices (dentists = 1,550). At baseline, the number of antibiotic items prescribed per 100 NHS treatment claims was 8.3 in the control group and 8.5 in the intervention group. At follow-up, antibiotic prescribing had decreased by 0.4 antibiotic items per 100 NHS treatment claims in control practices and by 1.0 in intervention practices. This represents a significant reduction (-5.7%; 95% CI -10.2% to -1.1%; p = 0.01) in dentists' prescribing rate in the intervention group relative to the control group. Intervention subgroup analyses found a 6.1% reduction in the antibiotic prescribing rate of dentists who had received the written behaviour change message relative to dentists who had not (95% CI -10.4% to -1.9%; p = 0.01). There was no significant between-group difference in the prescribing rate of dentists who received a health board comparator relative to those who did not (-4.3%; 95% CI -8.6% to 0.1%; p = 0.06), nor between dentists who received A&F at 0 and 6 mo relative to those who received A&F at 0, 6, and 9 mo (0.02%; 95% CI -4.2% to 4.2%; p = 0.99). The key limitations relate to the use of routinely collected datasets which did not allow evaluation of any effects on inappropriate prescribing. CONCLUSIONS:A&F derived from routinely collected datasets led to a significant reduction in the antibiotic prescribing rate of dentists. TRIAL REGISTRATION:Current Controlled Trials ISRCTN49204710.
Project description:BACKGROUND: Antibiotic prescribing in dentistry accounts for 9% of total antibiotic prescriptions in Scottish primary care. The Scottish Dental Clinical Effectiveness Programme (SDCEP) published guidance in April 2008 (2nd edition, August 2011) for Drug Prescribing in Dentistry, which aims to assist dentists to make evidence-based antibiotic prescribing decisions. However, wide variation in prescribing persists and the overall use of antibiotics is increasing. METHODS: RAPiD is a 12-month partial factorial cluster randomised trial conducted in NHS General Dental Practices across Scotland. Its aim is to compare the effectiveness of individualised audit and feedback (A&F) strategies for the translation into practice of SDCEP recommendations on antibiotic prescribing. The trial uses routinely collected electronic healthcare data in five aspects of its design in order to: identify the study population; apply eligibility criteria; carry out stratified randomisation; generate the trial intervention; analyse trial outcomes. Eligibility was determined on contract status and a minimum level of recent NHS treatment provision. All eligible dental practices in Scotland were simultaneously randomised at baseline either to current audit practice or to an intervention group. Randomisation was stratified by single-handed/multi-handed practices. General dental practitioners (GDPs) working at intervention practices will receive individualised graphical representations of their antibiotic prescribing rate from the previous 14 months at baseline and an update at six months. GDPs could not be blinded to their practice allocation. Intervention practices were further randomised using a factorial design to receive feedback with or without: a health board comparator; a supplementary text-based intervention; additional feedback at nine months. The primary outcome is the total antibiotic prescribing rate per 100 courses of treatment over the year following delivery of the baseline intervention. A concurrent qualitative process evaluation will apply theory-based approaches using the Consolidated Framework for Implementation Research to explore the acceptability of the interventions and the Theoretical Domains Framework to identify barriers and enablers to evidence-based antibiotic prescribing behaviour by GDPs. DISCUSSION: RAPiD will provide a robust evaluation of A&F in dentistry in Scotland. It also demonstrates that linked administrative datasets have the potential to be used efficiently and effectively across all stages of an randomised controlled trial. TRIAL REGISTRATION: Current Controlled Trials ISRCTN49204710.
Project description:BACKGROUND:Overuse of antimicrobial therapy in the community adds to the global spread of antimicrobial resistance, which is jeopardizing the treatment of common infections. METHODS:We designed a cluster randomized complex intervention to improve antimicrobial prescribing for urinary tract infection in Irish general practice. During a 3-month baseline period, all practices received a workshop to promote consultation coding for urinary tract infections. Practices in intervention arms A and B received a second workshop with information on antimicrobial prescribing guidelines and a practice audit report (baseline data). Practices in intervention arm B received additional evidence on delayed prescribing of antimicrobials for suspected urinary tract infection. A reminder integrated into the patient management software suggested first-line treatment and, for practices in arm B, delayed prescribing. Over the 6-month intervention, practices in arms A and B received monthly audit reports of antimicrobial prescribing. RESULTS:The proportion of antimicrobial prescribing according to guidelines for urinary tract infection increased in arms A and B relative to control (adjusted overall odds ratio [OR] 2.3, 95% confidence interval [CI] 1.7 to 3.2; arm A adjusted OR 2.7, 95% CI 1.8 to 4.1; arm B adjusted OR 2.0, 95% CI 1.3 to 3.0). An unintended increase in antimicrobial prescribing was observed in the intervention arms relative to control (arm A adjusted OR 2.2, 95% CI 1.2 to 4.0; arm B adjusted OR 1.4, 95% CI 0.9 to 2.1). Improvements in guideline-based prescribing were sustained at 5 months after the intervention. INTERPRETATION:A complex intervention, including audit reports and reminders, improved the quality of prescribing for urinary tract infection in Irish general practice. TRIAL REGISTRATION:ClinicalTrials.gov, no. NCT01913860.
Project description:<h4>Background</h4>Research literature consistently documents that scientifically based therapeutic recommendations are not always followed in the hospital or in the primary care setting. Currently, there is evidence that some general practitioners in Australia are not prescribing appropriately for patients diagnosed with 1) hypertension (HT) and 2) chronic heart failure (CHF). The objectives of this study were to improve general practitioner's drug treatment management of these patients through feedback on their own prescribing and small group discussions with peers and a trained group facilitator. The impact evaluation includes quantitative assessment of prescribing changes at 6, 9, 12 and 18 months after the intervention.<h4>Methods</h4>A pragmatic multi site cluster RCT began recruiting practices in October 2009 to evaluate the effects of a multi-faceted quality improvement (QI) intervention on prescribing practice among Australian general practitioners (GP) in relation to patients with CHF and HT. General practices were recruited nationally through General Practice Networks across Australia. Participating practices were randomly allocated to one of three groups: two groups received the QI intervention (the prescribing indicator feedback reports and small group discussion) with each group undertaking the clinical topics (CHF and HT) in reverse order to the other. The third group was waitlisted to receive the intervention 6 months later and acted as a "control" for the other two groups.De-identified data on practice, doctor and patient characteristics and their treatment for CHF and HT are extracted at six-monthly intervals before and after the intervention. Post-test comparisons will be conducted between the intervention and control arms using intention to treat analysis and models that account for clustering of practices in a Network and clustering of patients within practices and GPs.<h4>Discussion</h4>This paper describes the study protocol for a project that will contribute to the development of acceptable and sustainable methods to promote QI activities within routine general practice, enhance prescribing practices and improve patient outcomes in the context of CHF and HT.<h4>Trial registration</h4>Australian New Zealand Clinical Trials Registry (ANZCTR), Trial # 320870.