Michael Barras

Background: Transition of care (TOC) is a high-risk period for medication errors as patients move between multiple health care professionals, settings, and changing medication regimens. Ineffective communication systems further increase the likelihood of medication harm and hospital readmission. A TOC service with tailored, patient-specific support may help reduce these risks. Cardiovascular medicines significantly contribute to medication harm, and patients recovering from acute myocardial infarction (AMI) or cardiothoracic surgery (CS) are vulnerable due to complex regimens and frequent medication changes. Pharmacists, as medication experts embedded within cardiology teams, are well positioned to support these high-risk patients, facilitate safer transitions from hospital to home, and provide coordinated postdischarge follow-up. Objective: This study aims to evaluate the impact of a pharmacist-led interdisciplinary TOC service on reducing medication harm for cardiology patients post-AMI or CS. Methods: The study will be conducted in two phases: phase 1—intervention development will combine scoping review findings with stakeholder input to design an evidence-based, cocreated, culturally relevant intervention, underpinned by implementation science. A risk prediction model will be developed and validated to identify AMI/CS patients at high risk of medication-related unplanned readmission within 30 days. Phase 2: the RECARD (Reduce Hospital Readmissions for High-Risk Cardiology Patients) trial will use a pre-post design across 3 Australian tertiary hospitals (2 metropolitan and 1 regional) to compare a retrospective usual care patient cohort (referred to as “pre”) with a different prospective patient cohort (referred to as “post”) that includes both usual care and intervention AMI/CS patients receiving the TOC service. Only high-risk poststudy patients will receive the intervention TOC service, consisting of an individualized bundle of TOC activities to optimize medication management, while the remaining poststudy patients will receive usual care. A unique feature of the service is its capacity for hospital clinicians to conduct postdischarge home visits for patients needing additional support. The primary outcome for this study is medication-related 30-day hospital readmission. The analysis will consist of multivariable logistic regression. An economic analysis will be conducted to evaluate the costs associated with the intervention and medication-related readmissions. A comprehensive process evaluation will be conducted using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to enable learnings from the trial to inform sustainable improvements in this field. A patient survey will be administered at the end of the trial to assess patient experience and elicit patient preferences using a discrete choice experiment. Results: The predata are currently being collected for patients discharged from the 3 hospital sites during the 12-month prestudy period. The postdata intervention began on December 10, 2025. As of May 27, 2026, a total of 343 patients have been recruited at the 3 study sites. Conclusions: The findings of this study will inform future implementation of a sustainable cardiac TOC model within the Australian health care setting.

Introduction: Risk prediction tools (RPTs) use clinical data to estimate the likelihood of adverse events such as medication harm and institute preventive strategies. However, their adoption in hospitals can be limited by implementation challenges, workflow integration barriers, and technical constraints. Objectives: This study aimed to determine clinicians' and informaticians' perspectives on the requirements, factors influencing adoption of digital RPTs for medication harm in hospitals, and to identify effective implementation strategies. Methods: Semi‐structured interviews were conducted with clinicians and informaticians in three Australian digital hospitals within the Metro South Hospital and Health Service in 2025. Themes were inductively identified and deductively mapped to the Integrated Promoting Action on Research Implementation in Health Services (i‐PARIHS) framework. Results: Twenty‐two interviews were conducted with six doctors, eleven pharmacists, and five informaticians. Five themes were identified, reflecting key influences on the implementation of digital RPTs in hospitals, including clinician trust, perceived utility, and organisational readiness. Desired features such as automation, feedback functionality, and workflow compatibility were seen as essential to adoption. While enablers included comprehensive feedback mechanisms and adequate resource planning, barriers included poor integration with existing systems and uncertainty around tool reliability. Conclusion: Stakeholders recognised the value of digital RPTs in supporting medication safety. Understanding their perceptions and experiences is necessary for addressing barriers and enhancing tool acceptability. This study highlighted desirable tool features and organisational factors that influence successful implementation. These findings can inform strategies for effective implementation, adoption, and sustained use of RPTs in hospital settings.

Background: Medication harm is a significant healthcare challenge in hospitalised adult patients. Machine learning (ML) approaches offer the potential to improve prediction accuracy for medication harm by capturing complex relationships among clinical risk factors that traditional statistical models may not detect. Objective: To develop and evaluate ML models for predicting medication harm in hospitalised adult patients. Design: ML study involving secondary use of a prospectively collected hospital cohort dataset. Methods: This study used data from 279 adult patients admitted to general medical and geriatric wards of a tertiary hospital, among whom 40 experienced 51 medication harm events. Eight ML models were trained and evaluated for identifying patients at risk of medication harm. Medication harm cases were identified through detailed chart reviews, trigger tools, voluntary incident reporting, and International Classification of Diseases version 10 discharge coding. Data were pre-processed with missing values imputed using median imputation. Ten predictive features were selected using recursive feature elimination and clinical expert opinion. Models were trained using stratified 10-fold cross-validation with an 80/20 train-test split. Class imbalance was addressed using an oversampling approach. Results: A random forest model demonstrated the highest performance, achieving an area under the receiver operating characteristic curve of 0.76, precision of 0.50, recall of 0.62, F1 score of 0.54, accuracy of 0.86, specificity of 0.90, and an area under the precision-recall curve of 0.47. Predictive features of importance included length of stay, depression, dementia, insulin use, number of medications (⩾15), age (⩾65), opioid use, and antibiotic use. Conclusion: This study highlights the potential of ML models to predict medication harm, enabling early identification of high-risk patients for preventive interventions. Interdisciplinary collaboration is essential in developing robust, clinically relevant models that can be used to improve patient safety.

Aim: To identify and appraise studies of machine learning (ML)‐derived prediction models for determining the optimal dose of oral anticoagulants (OACs). Data Sources: PubMed, Embase, International Pharmaceutical Abstracts (IPA), IEEE Xplore, and Web of Science were searched from inception to 31 May 2024 using key terms synonymous with 'artificial intelligence' or 'machine learning', 'prediction', 'dose', and 'oral anticoagulants'. OACs included vitamin K antagonists (VKAs) — warfarin, acenocoumarol, phenprocoumon — and direct oral anticoagulants (DOACs) — apixaban, rivaroxaban, dabigatran. Study Selection: Studies published in English that used ML methods to develop and/or validate models to predict optimal OAC doses in adults, in any healthcare setting were included. A modified checklist, using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), guided data extraction and study appraisal was performed independently by two researchers. This review was conducted and reported according to the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) 2020 statement and was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (Study registration: PROSPERO CRD42021281150). Results: Of 7791 abstracts, 164 underwent full‐text review and 53 studies met the inclusion criteria. All used supervised learning methods; all but one evaluated VKAs. Of the 53 studies, 77% ( n = 41) targeted a ‘stable therapeutic dose’; and only one DOAC study evaluated patient outcomes using apixaban, rivaroxaban, edoxaban, and dabigatran. Nine studies (17%) were prospective and two (4%) reported external validation. Of 44 retrospective analyses, 15 (34%) studies used the International Warfarin Pharmacogenetics Consortium (IWPC) dataset, with 12 (23%) reporting external validation. Only four (8%) studies satisfied all pre‐determined criteria considered essential for building ML models for OACs. While comparison across studies was challenging, two of the better performing, externally validated models were by Li et al. (2020) and Gu et al. (2022) with an accuracy of 63% and 75% and retrospectively. Conclusion: Future research should train models on large datasets, including key genetic and non‐genetic predictors, undertake external validation, and prospectively assess performance using clinical data.

Aspirin resistance (AR) is linked to increased morbidity and mortality after cardiovascular and neurovascular procedures but has not been investigated after total hip arthroplasty (THA) and total knee arthroplasty (TKA). To investigate the incidence of AR in high‐risk patients after elective THA/TKA, we conducted a prospective cohort study of elective THA/TKA in patients with thromboembolic risk factors: obesity (body mass index [BMI] ≥30 kg/m ² ), diabetes and/or advanced age (≥65 years) who were administered aspirin for venous thromboembolism prophylaxis. AR was confirmed using a platelet function analyser. Forty patients were included with a mean±standard deviation age of 66.1±10.4 years and BMI of 32.2±6.0 kg/m ² and eight (20.0%) had diabetes. Overall, 52.5% of patients were aspirin resistant, with no statistically significant relationships between patient variables and AR ( P > 0.05). AR after THA/TKA was an observed phenomenon; however, larger trials are required to determine the clinical consequences of AR and to guide prophylaxis strategies in this population.

Background: Patients transitioning from secondary to primary healthcare are at increased risk of medication errors, adverse drug events and readmission to hospital. Incorporating a post-discharge follow-up by a hospital pharmacist has been proposed as a potential strategy to reduce readmissions. Aim: To determine the impact of a hospital-based pharmacist-led post-discharge medication review clinic on 30-day hospital readmissions in adult patients. Method: A single-site, retrospective cohort study compared the medical records of patients who attended the Pharmacist Review and EValuation of Existing and New Therapies (PREVENT) clinic between 1 January 2018 and 31 December 2019 to a group of case-matched control patients who did not attend the clinic. Patient inclusion criteria comprised those 18 years and older and attended the PREVENT clinic within 30 days of discharge. The matched group was based on gender, age and hospital metrics. The primary outcome measure is unplanned, all-cause 30-day hospital readmission. Results: There were 170 patients per group, with similar baseline characteristics. There were significantly less unplanned all-cause 30-day hospital readmissions in the PREVENT clinic group (n = 12 (7.1%)) compared to the control group (n = 40 (23.5%), χ² = 17.799, p < 0.001). Conclusion: This study demonstrates that a hospital-based pharmacist-led post-discharge medication review clinic reduced 30-day hospital readmissions in adult patients compared to a group of case-matched controls. This study provides evidence to support extending pharmaceutical care beyond the inpatient hospital setting into the early post-discharge period, particularly in hospitals providing comprehensive clinical pharmacy services.