Search

Introduction

Methods

Ethics and dissemination

Introduction

Methods

Ethics and dissemination

Background Despite improvement in health outcomes over the past few decades, China still experiences striking rural-urban health inequalities. There is limited research on the rural-urban differences in health system performance in China. Method We conducted a cross-sectional analysis to compare health system performance between rural and urban areas in five key domains of the health system: effectiveness, cost, access, patient-centredness and equity, using data from the WHO Study on Global AGEing and adult health (SAGE), China. Multiple logistic and linear regression models were used to assess the first four domains, adjusting for individual characteristics, and a relative index of inequality (RII) was used to measure the equity domain. Findings Compared to urban areas, rural areas had poorer performance in the management and control of hypertension and diabetes, with more than 50% lower odds of having breast (AOR = 0.44; 95% CI: 0.30, 0.64) and cervical cancer screening (AOR = 0.49; 95% CI: 0.29, 0.83). There was better performance in rural areas in the patient-centredness domain, with more than twice higher odds of getting prompt attention, respect, clarity of the communication with health provider and involvement in decision making of the treatment in inpatient care (AOR = 2.56, 2.15, 2.28, 2.28). Although rural residents incurred relatively less out-of-pocket expenditures (OOPE) for outpatient and inpatient services than urban residents, they were more likely to incur catastrophic expenditures on health (AOR = 1.30; 95% CI 1.16, 1.44). Wealth inequality was found in many indicators related to the effectiveness, costs and access domains in both rural and urban areas. Rural areas had greater inequalities in the management of hypertension and coverage of cervical cancer (RII = 7.45 vs 1.64). Conclusion Our findings suggest that urban areas have achieved better prevention and management of non-communicable disease than rural areas, but access to healthcare was equi

BACKGROUND: The lack of interoperable IT systems between residential aged care facilities (RACF) and general practitioners (GP) in primary care settings in Australia introduces the potential for medication discrepancies and other medication errors. The aim of the GRACEMED study is to determine the extent and potential severity of medication discrepancies between general practice and RACFs, and identify factors associated with medication discrepancies. METHODS: A cross sectional study of medication discrepancies between RACF medication orders and GP medication lists was conducted in the Sydney North Health Network, Australia. A random sample of RACF residents was included from practice lists provided by the general practices. RACF medication orders and GP medication lists for the included residents were compared, and medication discrepancies between the two sources were identified and characterised in terms of discrepancy type, potential for harm and associated factors. RESULTS: 31 GPs and 203 residents were included in the study. A total of 1777 discrepancies were identified giving an overall discrepancy rate of 72.6 discrepancies for every 100 medications. Omissions were the most common discrepancy type (35.2%,) followed by dose discrepancies (34.4%) and additions (30.4%). 48.5% of residents had a discrepancy with the potential to result in moderate harm and 9.8% had a discrepancy with the potential for severe harm. Number of medications prescribed was the only factor associated with medication discrepancies. CONCLUSION: Increased use of systems that allow information sharing and improved interoperability of clinical information is urgently needed to address medication safety issues experienced by RACF residents.

BACKGROUND: The lack of interoperable IT systems between residential aged care facilities (RACF) and general practitioners (GP) in primary care settings in Australia introduces the potential for medication discrepancies and other medication errors. The aim of the GRACEMED study is to determine the extent and potential severity of medication discrepancies between general practice and RACFs, and identify factors associated with medication discrepancies. METHODS: A cross sectional study of medication discrepancies between RACF medication orders and GP medication lists was conducted in the Sydney North Health Network, Australia. A random sample of RACF residents was included from practice lists provided by the general practices. RACF medication orders and GP medication lists for the included residents were compared, and medication discrepancies between the two sources were identified and characterised in terms of discrepancy type, potential for harm and associated factors. RESULTS: 31 GPs and 203 residents were included in the study. A total of 1777 discrepancies were identified giving an overall discrepancy rate of 72.6 discrepancies for every 100 medications. Omissions were the most common discrepancy type (35.2%,) followed by dose discrepancies (34.4%) and additions (30.4%). 48.5% of residents had a discrepancy with the potential to result in moderate harm and 9.8% had a discrepancy with the potential for severe harm. Number of medications prescribed was the only factor associated with medication discrepancies. CONCLUSION: Increased use of systems that allow information sharing and improved interoperability of clinical information is urgently needed to address medication safety issues experienced by RACF residents.

© 2019 The Society of Hospital Pharmacists of Australia Background: Medication errors in hospitals are common. Although electronic medication management systems (eMMS) have the potential to reduce errors during inpatient care, little is known about their effect in emergency departments (ED). Aim: The aims of this study were to report on the types of prescribing errors in an ED prior to implementation of an eMMS, to assess the risk the errors pose to patient safety and to evaluate whether safety-critical errors were potentially preventable with a newly implemented eMMS. Methods: Medication orders on paper charts were assessed for prescribing errors by a clinical hospital pharmacist. Error severity was rated using a five-point scale. Errors of moderate or higher severity, and all errors involving defined high-risk medicines were considered safety-critical errors. The potential for safety-critical errors to be prevented by the eMMS was rated (likely, possibly or unlikely). Results: Across 239 medication orders, 208 errors (27 clinical, 181 legal or procedural) were identified. The overall prescribing error rate was 0.87 errors/order. There were 67 safety-critical errors (53 legal or procedural, 14 clinical). Overall, 82.1% of errors were likely preventable by eMMS. However, of the clinical safety-critical errors, only 2 (14.3%) were considered likely preventable. Conclusion: Although eMMS has the capacity to prevent a high proportion of safety-critical legal or procedural errors in the ED, clinical prescribing errors in this category were unlikely to be prevented by using eMMS. Further investigation of these more severe error types could guide the design of eMMS decision support to improve system effectiveness.

© 2019 The Society of Hospital Pharmacists of Australia Background: Medication errors in hospitals are common. Although electronic medication management systems (eMMS) have the potential to reduce errors during inpatient care, little is known about their effect in emergency departments (ED). Aim: The aims of this study were to report on the types of prescribing errors in an ED prior to implementation of an eMMS, to assess the risk the errors pose to patient safety and to evaluate whether safety-critical errors were potentially preventable with a newly implemented eMMS. Methods: Medication orders on paper charts were assessed for prescribing errors by a clinical hospital pharmacist. Error severity was rated using a five-point scale. Errors of moderate or higher severity, and all errors involving defined high-risk medicines were considered safety-critical errors. The potential for safety-critical errors to be prevented by the eMMS was rated (likely, possibly or unlikely). Results: Across 239 medication orders, 208 errors (27 clinical, 181 legal or procedural) were identified. The overall prescribing error rate was 0.87 errors/order. There were 67 safety-critical errors (53 legal or procedural, 14 clinical). Overall, 82.1% of errors were likely preventable by eMMS. However, of the clinical safety-critical errors, only 2 (14.3%) were considered likely preventable. Conclusion: Although eMMS has the capacity to prevent a high proportion of safety-critical legal or procedural errors in the ED, clinical prescribing errors in this category were unlikely to be prevented by using eMMS. Further investigation of these more severe error types could guide the design of eMMS decision support to improve system effectiveness.

© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. Objective: The aim of this study was to use routinely collected electronic medicines administration (eMAR) data in residential aged care (RAC) to investigate the quality use of medicines. Design: A cross-sectional analysis of eMAR data. Setting: 71 RAC facilities in New South Wales and the Australian Capital Territory, Australia. Participants: Permanent residents living in a participating facility on 1 October 2015. Intervention: None. Main Outcome Measures: Variation in polypharmacy (?5 medications), hyper-polypharmacy (?10 medications) and antipsychotic use across facilities was examined using funnel plot analysis. Results: The study dataset included 4775 long-term residents. The mean resident age was 85.3 years and 70.6% of residents were female. The median facility size was 60 residents and 74.3% were in metropolitan locations. 84.3% of residents had polypharmacy, 41.2% hyper-polypharmacy and 21.0% were using an antipsychotic. The extent of polypharmacy (69.75-100% of residents), hyper-polypharmacy (38.81-76.19%) and use of antipsychotic medicines (0-75.6%) varied considerably across the 71 facilities. Conclusions: Using eMAR data we found substantial variation in polypharmacy, hyperpolypharmacy and antipsychotic medicine use across 71 RAC facilities. Further investigation into the policies and practices of facilities performing above or below expected levels is warranted to understand variation and drive quality improvement.

© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. Objective: The aim of this study was to use routinely collected electronic medicines administration (eMAR) data in residential aged care (RAC) to investigate the quality use of medicines. Design: A cross-sectional analysis of eMAR data. Setting: 71 RAC facilities in New South Wales and the Australian Capital Territory, Australia. Participants: Permanent residents living in a participating facility on 1 October 2015. Intervention: None. Main Outcome Measures: Variation in polypharmacy (?5 medications), hyper-polypharmacy (?10 medications) and antipsychotic use across facilities was examined using funnel plot analysis. Results: The study dataset included 4775 long-term residents. The mean resident age was 85.3 years and 70.6% of residents were female. The median facility size was 60 residents and 74.3% were in metropolitan locations. 84.3% of residents had polypharmacy, 41.2% hyper-polypharmacy and 21.0% were using an antipsychotic. The extent of polypharmacy (69.75-100% of residents), hyper-polypharmacy (38.81-76.19%) and use of antipsychotic medicines (0-75.6%) varied considerably across the 71 facilities. Conclusions: Using eMAR data we found substantial variation in polypharmacy, hyperpolypharmacy and antipsychotic medicine use across 71 RAC facilities. Further investigation into the policies and practices of facilities performing above or below expected levels is warranted to understand variation and drive quality improvement.

© 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.

© 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