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Electronic medication management systems (EMMS) have been implemented in most acute care settings in Australia to reduce medication error rates. One of the key challenges related to the introduction of EMMS in hospitals is the uptake of informal "workarounds" by clinicians, including nurses. In this study, we aimed to examine one workaround in depth, nurses not documenting medication administration in the EMMS at the time of administration. We conducted a review of incident reports to identify the factors that contribute to this workaround occurring and the consequences or potential consequences of this workaround on patients. We identified a range of contributing factors, with factors relating to the user (e.g. nurses being time poor) occurring most frequently in incident reports. The most frequently seen consequence of this workaround was the patient receiving an additional dose. This research revealed that strategies to reduce the uptake of this workaround should consider user and organisational factors rather than just EMMS design alone.

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OBJECTIVE: To measure the direct and indirect out-of-pocket (OOP) costs borne by Australians with gout. METHODS: A cross-sectional, Australia-wide, web-based survey was conducted over 12 months between May 2017 and April 2018. Participants were recruited via advertisements in doctors' clinics and healthcare organizations' websites, and social media platforms such as Facebook and Twitter. Survey questions collected information about participants' OOP spending on direct medical and non-medical gout-related healthcare costs. Participant demographics, gout status, healthcare sought, workdays lost to due gout and health-related quality of life were also collected. RESULTS: Seventy-nine patients with gout completed the survey; 70 (89%) were male, and on average were 56 (SD 16) years of age and had gout for 14 (SD 12) years. For this cohort, the median total OOP direct medical cost was AU$200 per year (interquartile range [IQR]: AU$60-AU$570). Sixty (76%) people with gout reported being affected by gout during work; however, only 0.25 (IQR: 0-3) days of work (approximately $60) were lost due to gout in a year. Nine percent (n = 7) of participants experienced cost-related treatment attrition and 33% reported economic hardship (n = 26). Participants who experienced economic hardship or cost-related treatment attrition had higher median total gout-related direct costs than those who did not. CONCLUSION: In Australia, gout has an OOP financial cost and reduces work productivity. The presence of cost-related treatment attrition among people with gout indicates that financial costs may be a significant barrier to seeking treatment for a subset of patients with gout.

OBJECTIVE: To measure the direct and indirect out-of-pocket (OOP) costs borne by Australians with gout. METHODS: A cross-sectional, Australia-wide, web-based survey was conducted over 12 months between May 2017 and April 2018. Participants were recruited via advertisements in doctors' clinics and healthcare organizations' websites, and social media platforms such as Facebook and Twitter. Survey questions collected information about participants' OOP spending on direct medical and non-medical gout-related healthcare costs. Participant demographics, gout status, healthcare sought, workdays lost to due gout and health-related quality of life were also collected. RESULTS: Seventy-nine patients with gout completed the survey; 70 (89%) were male, and on average were 56 (SD 16) years of age and had gout for 14 (SD 12) years. For this cohort, the median total OOP direct medical cost was AU$200 per year (interquartile range [IQR]: AU$60-AU$570). Sixty (76%) people with gout reported being affected by gout during work; however, only 0.25 (IQR: 0-3) days of work (approximately $60) were lost due to gout in a year. Nine percent (n = 7) of participants experienced cost-related treatment attrition and 33% reported economic hardship (n = 26). Participants who experienced economic hardship or cost-related treatment attrition had higher median total gout-related direct costs than those who did not. CONCLUSION: In Australia, gout has an OOP financial cost and reduces work productivity. The presence of cost-related treatment attrition among people with gout indicates that financial costs may be a significant barrier to seeking treatment for a subset of patients with gout.

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Ethics and dissemination

National clinical audits play key roles in improving care and driving system-wide change. However, effects of audit and feedback depend upon both reach (e.g. relevant staff receiving the feedback) and response (e.g. staff regulating their behaviour accordingly). This study aimed to investigate which hospital staff initially receive feedback and formulate a response, how feedback is disseminated within hospitals, and how responses are enacted (including barriers and enablers to enactment). Using a multiple case study approach, we purposively sampled four UK hospitals for variation in infrastructure and resources. We conducted semi-structured interviews with staff from transfusion-related roles and observed Hospital Transfusion Committee meetings. Interviews and analysis were based on the Theoretical Domains Framework of behaviour change. We coded interview transcripts into theoretical domains, then inductively identified themes within each domain to identify barriers and enablers. We also analysed data to identify which staff currently receive feedback and how dissemination is managed within the hospital. Members of the hospital's transfusion team initially received feedback in all cases, and were primarily responsible for disseminating and responding, facilitated through the Hospital Transfusion Committee. At each hospital, key individuals involved in prescribing transfusions reported never having received feedback from a national audit. Whether audits were discussed and actions explicitly agreed in Committee meetings varied between hospitals. Key enablers of action across all cases included clear lines of responsibility and strategies to remind staff about recommendations. Barriers included difficulties disseminating to relevant staff and needing to amend feedback to make it appropriate for local use. Appropriate responses by hospital staff to feedback about blood transfusion practice depend upon supportive infrastructures and role clarity. Hospitals could benefit

© 2017 The Author(s). Background: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. Methods: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. Results: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. Conclusions: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

Introduction Gout is increasing despite effective therapies to lower serum urate concentrations to 0.36 mmol/L or less, which, if sustained, significantly reduces acute attacks of gout. Adherence to urate-lowering therapy (ULT) is poor, with rates of less than 50% 1 year after initiation of ULT. Attempts to increase adherence in gout patients have been disappointing. We aim to evaluate the effectiveness of use of a personal, self-management, a'smartphone' application (app) to achieve target serum urate concentrations in people with gout. We hypothesise that personalised feedback of serum urate concentrations will improve adherence to ULT. Methods and analysisSetting and design Primary care. A prospective, cluster randomised (by general practitioner (GP) practices), controlled trial. Participants GP practices will be randomised to either intervention or control clusters with their patients allocated to the same cluster. Intervention The intervention group will have access to the Healthy.me app tailored for the self-management of gout. The control group patients will have access to the same app modified to remove all functions except the Gout Attack Diary. Primary and secondary outcomes The proportion of patients whose serum urate concentrations are less than or equal to 0.36 mmol/L after 6 months. Secondary outcomes will be proportions of patients achieving target urate concentrations at 12 months, ULT adherence rates, serum urate concentrations at 6 and 12 months, rates of attacks of gout, quality of life estimations and process and economic evaluations. The study is designed to detect a ≥30% improvement in the intervention group above the expected 50% achievement of target serum urate at 6 months in the control group: power 0.80, significance level 0.05, assumed a'dropout' rate 20%. Ethics and dissemination This study has been approved by the University of New South Wales Human Research Ethics Committee. Study findings will be disseminated in international conferen

© © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. Introduction Gout is increasing despite effective therapies to lower serum urate concentrations to 0.36 mmol/L or less, which, if sustained, significantly reduces acute attacks of gout. Adherence to urate-lowering therapy (ULT) is poor, with rates of less than 50% 1 year after initiation of ULT. Attempts to increase adherence in gout patients have been disappointing. We aim to evaluate the effectiveness of use of a personal, self-management, a'smartphone' application (app) to achieve target serum urate concentrations in people with gout. We hypothesise that personalised feedback of serum urate concentrations will improve adherence to ULT. Methods and analysisSetting and design Primary care. A prospective, cluster randomised (by general practitioner (GP) practices), controlled trial. Participants GP practices will be randomised to either intervention or control clusters with their patients allocated to the same cluster. Intervention The intervention group will have access to the Healthy.me app tailored for the self-management of gout. The control group patients will have access to the same app modified to remove all functions except the Gout Attack Diary. Primary and secondary outcomes The proportion of patients whose serum urate concentrations are less than or equal to 0.36 mmol/L after 6 months. Secondary outcomes will be proportions of patients achieving target urate concentrations at 12 months, ULT adherence rates, serum urate concentrations at 6 and 12 months, rates of attacks of gout, quality of life estimations and process and economic evaluations. The study is designed to detect a ≥30% improvement in the intervention group above the expected 50% achievement of target serum urate at 6 months in the control group: power 0.80, significance level 0.05, assumed a'dropout' rate

© © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. Introduction Gout is increasing despite effective therapies to lower serum urate concentrations to 0.36 mmol/L or less, which, if sustained, significantly reduces acute attacks of gout. Adherence to urate-lowering therapy (ULT) is poor, with rates of less than 50% 1 year after initiation of ULT. Attempts to increase adherence in gout patients have been disappointing. We aim to evaluate the effectiveness of use of a personal, self-management, a'smartphone' application (app) to achieve target serum urate concentrations in people with gout. We hypothesise that personalised feedback of serum urate concentrations will improve adherence to ULT. Methods and analysisSetting and design Primary care. A prospective, cluster randomised (by general practitioner (GP) practices), controlled trial. Participants GP practices will be randomised to either intervention or control clusters with their patients allocated to the same cluster. Intervention The intervention group will have access to the Healthy.me app tailored for the self-management of gout. The control group patients will have access to the same app modified to remove all functions except the Gout Attack Diary. Primary and secondary outcomes The proportion of patients whose serum urate concentrations are less than or equal to 0.36 mmol/L after 6 months. Secondary outcomes will be proportions of patients achieving target urate concentrations at 12 months, ULT adherence rates, serum urate concentrations at 6 and 12 months, rates of attacks of gout, quality of life estimations and process and economic evaluations. The study is designed to detect a ≥30% improvement in the intervention group above the expected 50% achievement of target serum urate at 6 months in the control group: power 0.80, significance level 0.05, assumed a'dropout' rate

© 2017 The Author(s). Background: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. Methods: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. Results: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. Conclusions: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

© 2017 The Author(s). Background: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. Methods: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. Results: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. Conclusions: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

Introduction: Medication errors are the most frequent cause of preventable harm in hospitals. Medication management in paediatric patients is particularly complex and consequently potential for harms are greater than in adults. Electronic medication management (eMM) systems are heralded as a highly effective intervention to reduce adverse drug events (ADEs), yet internationally evidence of their effectiveness in paediatric populations is limited. This study will assess the effectiveness of an eMM system to reduce medication errors, ADEs and length of stay (LOS). The study will also investigate system impact on clinical work processes. Methods and analysis: A stepped-wedge cluster randomised controlled trial (SWCRCT) will measure changes pre-eMM and post-eMM system implementation in prescribing and medication administration error (MAE) rates, potential and actual ADEs, and average LOS. In stage 1, 8 wards within the first paediatric hospital will be randomised to receive the eMM system 1 week apart. In stage 2, the second paediatric hospital will randomise implementation of a modified eMM and outcomes will be assessed. Prescribing errors will be identified through record reviews, and MAEs through direct observation of nurses and record reviews. Actual and potential severity will be assigned. Outcomes will be assessed at the patient-level using mixed models, taking into account correlation of admissions within wards and multiple admissions for the same patient, with adjustment for potential confounders. Interviews and direct observation of clinicians will investigate the effects of the system on workflow. Data from site 1 will be used to develop improvements in the eMM and implemented at site 2, where the SWCRCT design will be repeated (stage 2). Ethics and dissemination: The research has been approved by the Human Research Ethics Committee of the Sydney Children's Hospitals Network and Macquarie University. Results will be reported through academic journals and semin