Objectives

To compare medication errors identified at audit and via direct observation with medication errors reported to an incident reporting system at paediatric hospitals and to investigate differences in types and severity of errors detected and reported by staff.

Methods

This is a comparison study at two tertiary referral paediatric hospitals between 2016 and 2020 in Australia. Prescribing errors were identified from a medication chart audit of 7785 patient records. Medication administration errors were identified from a prospective direct observational study of 5137 medication administration doses to 1530 patients. Medication errors reported to the hospitals’ incident reporting system were identified and matched with errors identified at audit and observation.

Results

Of 11 302 clinical prescribing errors identified at audit, 3.2 per 1000 errors (95% CI 2.3 to 4.4, n=36) had an incident report. Of 2224 potentially serious prescribing errors from audit, 26.1% (95% CI 24.3 to 27.9, n=580) were detected by staff and 11.2 per 1000 errors (95% CI 7.6 to 16.5, n=25) were reported to the incident system. Although the prescribing error detection rates varied between the two hospitals, there was no difference in incident reporting rates regardless of error severity. Of 40 errors associated with actual patient harm, only 7 (17.5%; 95% CI 8.7% to 31.9%) were detected by staff and 4 (10.0%; 95% CI 4.0% to 23.1%) had an incident report. None of the 2883 clinical medication administration errors observed, including 903 potentially serious errors and 144 errors associated with actual patient harm, had incident reports.

Conclusion

Incident reporting data do not provide an accurate reflection of medication errors and related harm to children in hospitals. Failure to detect medication errors is likely to be a significant contributor to low error reporting rates. In an era of electronic health records, new automated approaches to monitor medication safety should be pursued to provide real-time monitoring.

Background

Overuse of medical care is a pervasive problem. Studies using hypothetical scenarios suggest that physicians’ risk literacy influences medical decisions; real-world correlations, however, are lacking. We sought to determine the association between physicians’ risk literacy and their real-world prescriptions of potentially hazardous drugs, accounting for conflicts of interest and perceptions of benefit–harm ratios in low-value prescribing scenarios.

Setting and sample

Cross-sectional study—conducted online between June and October 2023 via field panels of Sermo (Hamburg, Germany)—with a convenience sample of 304 English general practitioners (GPs).

Methods

GPs’ survey responses on their treatment-related risk literacy, conflicts of interest and perceptions of the benefit–harm ratio in low-value prescribing scenarios were matched to their UK National Health Service records of prescribing volumes for antibiotics, opioids, gabapentin and benzodiazepines and analysed for differences.

Results

204 GPs (67.1%) worked in practices with ≥6 practising GPs and 226 (76.0%) reported 10–39 years of experience. Compared with GPs demonstrating low risk literacy, GPs with high literacy prescribed fewer opioids (mean (M): 60.60 vs 43.88 prescribed volumes/1000 patients/6 months, p=0.016), less gabapentin (M: 23.84 vs 18.34 prescribed volumes/1000 patients/6 months, p=0.023), and fewer benzodiazepines (M: 17.23 vs 13.58 prescribed volumes/1000 patients/6 months, p=0.037), but comparable volumes of antibiotics (M: 48.84 vs 40.61 prescribed volumes/1000 patients/6 months, p=0.076). High-risk literacy was associated with lower conflicts of interest ( = 0.12, p=0.031) and higher perception of harms outweighing benefits in low-value prescribing scenarios (p=0.007). Conflicts of interest and benefit–harm perceptions were not independently associated with prescribing behaviour (all ps >0.05).

Conclusions and relevance

The observed association between GPs with higher risk literacy and the prescription of fewer hazardous drugs suggests the importance of risk literacy in enhancing patient safety and quality of care.

Introduction

Although diagnostic errors have gained renewed focus within the patient safety domain, measuring them remains a challenge. They are often measured using methods that lack information on decision-making processes given by involved physicians (eg, record reviews). The current study analyses serious adverse event (SAE) reports from Dutch hospitals to identify common contributing factors of diagnostic errors in hospital medicine. These reports are the results of thorough investigations by highly trained, independent hospital committees into the causes of SAEs. The reports include information from involved healthcare professionals and patients or family obtained through interviews.

Methods

All 71 Dutch hospitals were invited to participate in this study. Participating hospitals were asked to send four diagnostic SAE reports of their hospital. Researchers applied the Safer Dx Instrument, a Generic Analysis Framework, the Diagnostic Error Evaluation and Research (DEER) taxonomy and the Eindhoven Classification Model (ECM) to analyse reports.

Results

Thirty-one hospitals submitted 109 eligible reports. Diagnostic errors most often occurred in the diagnostic testing, assessment and follow-up phases according to the DEER taxonomy. The ECM showed human errors as the most common contributing factor, especially relating to communication of results, task planning and execution, and knowledge. Combining the most common DEER subcategories and the most common ECM classes showed that clinical reasoning errors resulted from failures in knowledge, and task planning and execution. Follow-up errors and errors with communication of test results resulted from failures in coordination and monitoring, often accompanied by usability issues in electronic health record design and missing protocols.

Discussion

Diagnostic errors occurred in every hospital type, in different specialties and with different care teams. While clinical reasoning errors remain a common problem, often caused by knowledge and skill gaps, other frequent errors in communication of test results and follow-up require different improvement measures (eg, improving technological systems).

Introduction

Current international standards in consent to surgery practices are usually derived from health systems in Western countries, while little attention has been given to other contexts such as sub-Saharan Africa (SSA), despite this region facing the highest burdens of disease amenable to surgery globally. The aim of this study was to examine how the concept of informed consent for surgery is interpreted and applied in the context of SSA, and factors affecting current practices.

Methods

A systematic search of Medline, Embase and African Journal OnLine databases as well as grey sources was executed in May 2023 to retrieve relevant literature published since 2010 in English language against a set of given criteria. The socioecological framework for health was used for organising and summarising the identified evidence.

Results

A total of 27 papers were included in the review. Findings revealed that consent to surgery practices is generally substandard across SSA and the process is not adequate. Patients’ understanding of informed consent is limited, likewise awareness of their rights to decision-making. A range of factors at the individual, interpersonal, institutional and system/societal levels affect the informed consent process.

Conclusion

There is a need to find more culturally acceptable and ethical ways to include the participation of patients in the decision-making process for surgical treatment in the SSA and define standards more closely aligned with the local context.

Diagnostic errors are associated with patient harm and suboptimal outcomes. Despite national scientific efforts to advance definition, measurement and interventions for diagnostic error, diagnosis in mental health is not well represented in this ongoing work. We aimed to summarise the current state of research on diagnostic errors in mental health and identify opportunities to align future research with the emerging science of diagnostic safety. We review conceptual considerations for defining and measuring diagnostic error, the application of these concepts to mental health settings, and the methods and subject matter focus of recent studies of diagnostic error in mental health. We found that diagnostic error is well understood to be a problem in mental healthcare. Although few studies used clear definitions or frameworks for understanding diagnostic error in mental health, several studies of missed, wrong, delayed and disparate diagnosis of common mental disorders have identified various avenues for future research and development. Nevertheless, a lack of clear consensus on how to conceptualise, define and measure errors in diagnosis will pose a barrier to advancement. Further research should focus on identifying preventable missed opportunities in the diagnosis of mental disorders, which may uncover generalisable opportunities for improvement.

Checklists are a type of cognitive aid used to guide task performance; they have been adopted as an important safety intervention throughout many high-risk industries. They have become an ubiquitous tool in many medical settings due to being easily accessible and perceived as easy to design and implement. However, there is a lack of understanding for when to use checklists and how to design them, leading to substandard use and suboptimal effectiveness of this intervention in medical settings. The design of a checklist must consider many factors including what types of errors it is intended to address, the experience and technical competencies of the targeted users, and the specific tools or equipment that will be used. Although several taxonomies have been proposed for classifying checklist types, there is, however, little guidance on selecting the most appropriate checklist type, nor how differences in user expertise can influence the design of the checklist. Therefore, we developed an algorithm to provide guidance on checklist use and design. The algorithm, intended to support conception and content/design decisions, was created based on the synthesis of the literature on checklists and our experience developing and observing the use of checklists in clinical environments. We then refined the algorithm iteratively based on subject matter experts’ feedback provided at each iteration. The final algorithm included two parts: the first part provided guidance on the system safety issues for which a checklist is best suited, and the second part provided guidance on which type of checklist should be developed with considerations of the end users’ expertise.

Barcode medication administration (BCMA) technology can improve patient safety by using scanning technology to ensure the right drug and dose are given to the right patient. Implementation can be challenging, requiring adoption of different workflows by nursing staff. In one London National Health Service trust scanning rates were lower than desired at around 0–20% of doses per ward. Our objective was to encourage patient safety behaviours in the form of medication scanning through implementation of a feedback intervention. This was informed by behavioural science, codesigned with nurses and informed by known barriers to use. Five wards were selected to trial the intervention over an 18-week period beginning August 2021. The remaining 14 hospital wards acted as controls. Intervention wards had varying uptake of BCMA at baseline and represented a range of specialties. A bespoke feedback intervention comprising three behavioural science constructs (gamification, the messenger effect and framing) was delivered to each intervention ward each week. A linear difference-in-difference analysis was used to evaluate the impact of our intervention on scan rates, both for the overall 18-week period and at two weekly intervals within this timeframe. We identified a 23.1 percentage point increase in medication scan rates (from an average baseline of 15.0% to 38.1%) on the intervention wards compared with control (p<0.001) following implementation of the intervention. Feedback had most impact in the first 6 weeks, with an initial percentage point increase of 26.3 (p<0.001), which subsequently plateaued. Neither clinical specialty nor number of beds on each ward were significant factors in our models. Our study demonstrated that a feedback intervention, codesigned with end users and incorporating behavioural science constructs, can lead to a significant increase in the adoption of BCMA scanning.

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