ORCID Profile
0000-0002-9893-5546
Current Organisation
Austin Health
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Publisher: Wiley
Date: 29-06-2023
DOI: 10.1002/JPPR.1872
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.
Publisher: Cold Spring Harbor Laboratory
Date: 13-12-2021
DOI: 10.1101/2021.12.11.21267504
Abstract: 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 1.1 million unlabelled clinical documents secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a model finetuned from the ClinicalBERT model, which has demonstrated state-of-the-art performance on other pharmacovigilance tasks. 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 0.955 (95% CI 0.946 - 0.963) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.
Publisher: Therapeutic Guidelines Limited
Date: 22-08-2023
Publisher: Elsevier BV
Date: 2023
Publisher: Wiley
Date: 23-05-2022
DOI: 10.1111/BCP.15394
Abstract: To describe paracetamol dosing and liver function test (LFT) monitoring in older hospital inpatients who are frail or have low body weight. Retrospective observational study, at a 790‐bed metropolitan public health service in Australia. Patients aged ≥70 years, with body weight kg or frailty index based on laboratory data (FI‐Lab) score ≥0.3, who were administered paracetamol during an admission with length‐of‐stay hours, were included. Data were extracted from electronic medical records. Paracetamol doses administered in hospital, and doses prescribed on discharge, were compared against consensus guidelines that recommended ≤60 mg/kg/d for older people weighing kg, and ≤3000 mg/d for frail older people. In total, 240 admissions ( n = 229 patients, mean age 84.7 years) were analysed. During 150 (62.5%) admissions, higher than recommended paracetamol doses were prescribed. On 138 (57.5%) occasions, patients were prescribed paracetamol on discharge, and 112/138 (81.2%) doses were higher than recommended. Most discharge prescriptions (97/138, 70.3%) were for regular administration. The median daily dose on discharge for patients kg was 83.7 mg/kg (interquartile range 73.6–90.9 mg/kg). For frail patients ≥50 kg, the median daily discharge dose was 3990 mg (interquartile range 3000–4000 mg). LFTs were measured in hospital for 151/200 (75.5%) and 93/166 (56.0%) patients who received paracetamol for hours and days, respectively. Majority of paracetamol doses prescribed for frail or low‐weight older patients in hospital and on discharge were higher than recommended in consensus guidelines. LFTs were not measured for 44% patients who received paracetamol regularly for days. Further studies are needed to explore long‐term outcomes of this practice.
No related grants have been discovered for Elizabeth Su.