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Objectives Adverse medication events are associated with a significant number of hospital admissions, and the appropriate recording of these events plays a vital role in medication safety. We set out to analyse the time and extrapolated cost in reporting adverse drug reactions (ADRs). Methods A time and motion study of the tasks involved in reviewing, assessing, reporting and communicating ADRs was done over a period of 2 months. Key findings We found a median of 69 min was needed in background work per ADR report. Conclusion The commitment involved in the support of this program is considerable and will encourage further refinement to streamline the process.

COVID-19 brings with it unprecedented challenges in clinical management. An important component of care is the provision of safe and effective symptom control. Given the emerging literature reporting on the risk of QT prolongation and arrhythmias associated with COVID-19 disease and experimental therapies, we highlight some considerations for the prescribing of palliative care medications in this context. Based on the experience gained from palliative care referrals at our institution prior to and during the COVID-19 pandemic, and in collaboration with our clinical pharmacology colleagues, we outline some general prescribing principles which may assist with weighing the risks and benefits of prescribing symptomatic medications in and beyond the current pandemic.

Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10th Revision (ICD-10) codes have been used in epidemiological studies of ADRs and offer the potential for automated ADR detection systems. The aim of this study was to develop an automated ADR detection system based on ICD-10 codes, using machine-learning algorithms to improve accuracy and efficiency. For a 12-month period from December 2016 to November 2017, every inpatient episode receiving an ICD-10 code in the range Y40.0–Y59.9 (ADR code) was flagged for review as a potential ADR. Each flagged admission was assessed by an expert pharmacist and, if needed, reviewed at regular ADR committee meetings. For each report, a determination was made about ADR probability and severity. The dataset was randomly split into training and test sets. A machine-learning model using the random forest algorithm was developed on the training set to discriminate between true and false ADR reports. The model was then applied to the test set to assess accuracy using the area under the receiver operating characteristic (AUC). In the study period, 2917 Y40.0–Y59.9 codes were applied to admissions, resulting in 245 ADR reports after review. These 245 reports accounted for 44.5% of all ADR reporting in our hospital in the study period. A random forest model built on the training set was able to discriminate between true and false reports on the test set with an AUC of 0.803. Automated ADR detection using ICD-10 coding significantly improved ADR detection in the study period, with improved discrimination between true and false reports by applying a machine-learning model.

CK: Principal Investigator. CM and CK were responsible for the study design and conception; all authors were responsible for acquisition and validation of the data; CM was responsible for analysis and interpretation of the data; and all authors contributed to reviewing drafts of the manuscript and approved the final version for publication.

The recent article ‘Pharmacovigilance and expedited drug approvals’ by Matthew Linger and Jennifer Martin,1 provided...

A 59-year-old female underwent a renal transplant in early 2015. Her initial immunosuppression regimen was mycophenolate mofetil, prednisolone and tacrolimus.