No abstract

Opioid prescribing requires careful planning to minimise the risk of serious adverse outcomes. However, documentation of discharge opioid plans for patients and their general practitioners (GPs) is inconsistent, particularly when opioids are commenced in the emergency department or after surgery. We describe an initiative to promote consistent discharge opioid plan communication by implementing an opioid management plan (OMP) in our hospital's electronic medical record. Completion of an electronic form by the prescriber generates an OMP note in the medical history, which is used by the pharmacist to provide tailored opioid patient education. The OMP also populates the discharge summary that is sent to the GP and the Australian national digital health record platform, My Health Record. Preliminary evaluation shows incorporating OMP documentation into routine workflows has assisted prescribers to consistently document the plan for supplied opioids, supporting continuity of care. Workflow optimisation is ongoing to further improve discharge summary documentation and provision of patient‐friendly written information. This study was conducted as a quality improvement project and audits conducted as part of the project were approved by Austin Health's Office for Research (Project No: LNR/18/Austin/155). Informed patient consent was not required by Austin Health.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events.We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.