Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.
Backgrounds: Rapid response team or medical emergency team (MET) calls are typically activated by significant alterations of vital signs in inpatients. However, the clinical significance of a specific criterion, blood pressure elevations, is uncertain. Objectives: The aim of this study was to evaluate the likelihood ratios associated with MET-activating vital signs, particularly in-patient hypertension, for predicting in-hospital mortality among general medicine inpatients who met MET criteria at any point during admission in a South Australian metropolitan teaching hospital. Results: Among the 15,734 admissions over a two-year period, 4282 (27.2%) met any MET criteria, with a positive likelihood ratio of 3.05 (95% CI 2.93 to 3.18) for in-hospital mortality. Individual MET criteria were significantly associated with in-hospital mortality, with the highest positive likelihood ratio for respiratory rate ≤ 7 breaths per minute (9.83, 95% CI 6.90 to 13.62), barring systolic pressure ≥ 200 mmHg (LR + 1.26, 95% CI 0.86 to 1.69). Conclusions: Our results show that meeting the MET criteria for hypertension, unlike other criteria, was not significant associated with in-hospital mortality. This observation warrants further research in other patient cohorts to determine whether blood pressure elevations should be routinely included in MET criteria.
OBJECTIVE: To determine the weighting of rural exposure within publicly available standardised curriculum vitae (CV) scoring criteria for trainee medical officer's applying into medical and surgical specialty training programs in Australia and New Zealand. METHODS/DESIGN: An observational analysis of rural exposure point allocations within publicly available standardised CV scoring criteria for entrance into specialty training programs. SETTING: All Australian and New Zealand medical and surgical specialties training programs outlined by the Australian Health Practitioner Regulation Agency (AHPRA) who publish publicly available standardised CV scoring criteria for entrance into specialty training were included. RESULTS: Of the 14 specialty training programs that publish publicly available standardised CV scoring criteria, 8/14 allocate points towards rural exposure. While the allocation of points within this scoring domain varies between the eight training programs, the mean weighting of rural exposure is 13.7%. CONCLUSIONS: The relative weighting of rural exposure varies between the eight specialty training programs who include rural exposure as a CV scoring criteria. The deliberate and strategic construction of CV scoring criteria and inclusion of rural exposure points is important to continue developing the Australian rural specialist workforce. Future development of standardised CV scoring criteria should continue to consider point allocation towards rural exposure and related activities to ensure that the requirements of rural Australian healthcare needs are met across medical and surgical specialties.
BACKGROUND: This scoping review aimed to characterise near-death experiences in the setting of cardiac arrest, a phenomenon that is poorly understood and may have clinical consequences. METHOD: PubMed/MEDLINE was searched to 23 July 2023 for prospective studies describing near-death experiences in cardiac arrest. PRISMA-ScR guidelines were adhered to. Qualitative and quantitative data were synthesised. Meta-analysis was precluded due to data heterogeneity. RESULTS: 60 records were identified, of which 11 studies involving interviews were included from various countries. Sample size ranged from 28-344, and proportion of female patients (when reported) was 0-50%, with mean age (when reported) ranging 54-64 years. Comorbidities and reasons for cardiac arrest were heterogeneously reported. Incidence of near-death experiences in the included studies varied from 6.3% to 39.3%; with variation between in-hospital (6.3-39.3%) versus out-of-hospital (18.9-21.2%) cardiac arrest. Individual variables regarding patient characteristics demonstrated statistically significant association with propensity for near-death experiences. Reported content of near-death experiences tended to reflect the language of the questionnaires used, rather than the true language used by individual study participants. Three studies conducted follow-up, and all suggested a positive life attitude change, however one found significantly higher 30-day all-cause mortality in patients with near-death experiences versus those without, in non-controlled analysis. CONCLUSIONS: From prospective studies that have investigated the phenomenon, near-death experiences may occur in as frequent as over one-third of patients with cardiac arrest. Lasting effects may follow these events, however these could also be confounded by clinical characteristics.
PURPOSE: To investigate the potential of an Optical Coherence Tomography (OCT) based Deep-Learning (DL) model in the prediction of Vitreomacular Traction (VMT) syndrome outcomes. DESIGN: A single-centre retrospective review. METHODS: Records of consecutive adult patients attending the Royal Adelaide Hospital vitreoretinal clinic with evidence of spontaneous VMT were reviewed from January 2019 until May 2022. All patients with evidence of causes of cystoid macular oedema or secondary causes of VMT were excluded. OCT scans and outcome data obtained from patient records was used to train, test and then validate the models. RESULTS: For the deep learning model, ninety-five patient files were identified from the OCT (SPECTRALIS system; Heidelberg Engineering, Heidelberg, Germany) records. 25% of the patients spontaneously improved, 48% remained stable and 27% had progression of their disease, approximately. The final longitudinal model was able to predict 'improved' or 'stable' disease with a positive predictive value of 0.72 and 0.79, respectively. The accuracy of the model was greater than 50%. CONCLUSIONS: Deep-learning models may be utilised in real-world settings to predict outcomes of VMT. This approach requires further investigation as it may improve patient outcomes by aiding ophthalmologists in cross-checking management decisions and reduce the need for unnecessary interventions or delays.
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data and transfer learning. AIMS: The aims of this study were to investigate the use of additional training data and novel machine learning strategies, namely synthetic data and transfer learning, to improve the performance of penicillin adverse drug reaction (ADR) machine learning classification. METHODS: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system electronic health record (EHR). The models were developed by training on various labelled data sets. ADR entries were split into training and testing data sets and used to develop and test a variety of machine learning models. The effect of training on additional data and synthetic data versus the use of transfer learning was analysed. RESULTS: Following the application of these techniques, the area under the receiver operator curve of best-performing models for the classification of penicillin allergy (vs intolerance) and high-risk allergy (vs low-risk allergy) improved to 0.984 (using the artificial neural network model) and 0.995 (with the transfer learning approach) respectively. CONCLUSIONS: Machine learning models demonstrate high levels of accuracy in the classification and risk stratification of penicillin ADR labels using the reaction documented in the EHR. The model can be further optimised by incorporating additional training data and using transfer learning. Practical applications include automating case detection for penicillin allergy delabelling programmes.
Large language models like ChatGPT are a type of machine learning model that can offer a positive paradigm shift in case-based/problem-based learning (CBL/PBL). ChatGPT may be able to augment the existing paradigm to work in conjunction with the clinical-teacher in PBL/CBL case generation. It can develop realistic patient cases that could be revised by clinical teachers to ensure accuracy and relevance. Further, it can be directed to include specific case content in order to facilitate the constructive alignment of the case with the broader learning objectives of the curriculum. There is also the possibility of improving engagement by 'gamifying' CBL/PBL. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-023-01934-5.
INTRODUCTION: Student-led Objective Structured Clinical Examinations (OSCEs) provide formative learning opportunities prior to Faculty-led OSCEs. It is important to undertake quality assurance measurements of peer-led assessments because, if they are found to be unreliable and invalid, they may have detrimental impacts. The objectives of this study were to explore and evaluate Student-led OSCEs hosted by fifth-year medical students. METHODS: Student-led OSCE results were analysed to examine reliability (Cronbach's alpha). The relationship between Student-led and Faculty-led OSCEs was evaluated using linear regression. Qualitative data were acquired by survey and semi-structured interviews and were analysed using an inductive content analysis approach. RESULTS: In total, 85 (94%) of 91 eligible students consented to study participation. Student-led OSCEs had a low-moderate reliability [Cronbach alpha = 0.47 (primary care) and 0.61 (human reproduction/paediatrics) (HRH)]. A statistically significant, positive relationship between Student-led and Faculty-led OSCE results was observed. Faculty-led OSCE grades increased by 0.49 (95% CI: 0.18, 0.80) to 1.09 (95% CI: 0.67, 1.52), for each percentage increase in Student-led OSCE result. Student-led OSCE participants highly valued the authentic peer-assessed experience. Reported benefits included a reduction of perceived stress and anxiety prior to Faculty-led OSCEs, recognition of learning gaps, contribution to overall clinical competency and facilitation of collaboration between peers. DISCUSSION: Student-led OSCEs are moderately reliable and can predict Faculty-led OSCE performance. This form of near-peer assessment encourages the metacognitive process of reflective practice and can be effectively implemented to direct further study. Faculties should collaborate with their student bodies to facilitate Student-led OSCEs and offer assistance to improve the quality, and benefits, of these endeavours.
