INTRODUCTION: In February 2018 the Remote Medical Practitioner (RMP)-led telehealth model for providing both primary care advice and aeromedical retrievals in Central Australia was replaced by the Medical Retrieval and Consultation Centre (MRaCC) and Remote Outreach Consultation Centre (ROCC). In this new model, specialists with advanced critical care skills provide telehealth consultations for emergencies 24/7 and afterhours primary care advice (MRaCC) while RMPs (general practitioners) provide primary care telehealth advice in business hours via the separate ROCC. OBJECTIVE: To evaluate changes in clinicians' perceptions of efficiency and timeliness of the new (MRaCC) and (ROCC) model in Central Australia. DESIGN: There were 103 and 72 respondents, respectively, to pre- and post-implementation surveys of remote clinicians and specialist staff. FINDINGS: Both emergency and primary care aspects of telehealth support were perceived as being significantly more timely and efficient under the newly introduced MRaCC/ROCC model. Importantly, health professionals in remote community were more likely to feel that their access to clinical support during emergencies was consistent and immediately available. DISCUSSION: Respondents consistently perceived the new MRaCC/ROCC model more favourably than the previous RMP-led model, suggesting that there are benefits to having separate referral streams for telehealth advice for primary health care and emergencies, and staffing the emergency stream with specialists with advanced critical care skills. CONCLUSION: Given the paucity of literature about optimal models for providing pre-hospital medical care to remote residents, the findings have substantial local, national and international relevance and implications, particularly in similar geographically large countries, with low population density.

INTRODUCTION: It is well-established that heatwaves increase demand for emergency transport in metropolitan areas; however, little is known about the impact of heat events on demand for prehospital retrieval services in rural and remote areas, or how heatwaves are defined in this context. INCLUSION CRITERIA: Papers were eligible for inclusion if they reported on the impact of a heat event on the activity of a prehospital and retrieval service in a rural or remote area. METHODS: A search of PubMed, Cochrane, Science Direct, CINAHL, and Google Scholar databases was undertaken on August 18, 2020 using search terms related to emergency medical transport, extreme heat, and rural or remote. Data relevant to the impact of heat on retrieval service activity were extracted, as well as definitions of extreme heat. RESULTS: Two papers were identified, both from Australia. Both found that heat events increased the number of road ambulance call-outs. Both studies used the Excess Heat Factor (EHF) to define heatwave periods of interest. CONCLUSIONS: This review found almost no primary literature on demand for prehospital retrieval services in rural and remote areas, and no data specifically related to aeromedical transport. The research did recognize the disproportionate impact of heat-related increase in service demand on Australian rural and regional health services. With the effects of climate change already being felt, there is an urgent need for more research and action in this area.

BACKGROUND: Remote Australian women in labour often rely on retrieval services to allow birthing in specialist obstetric centres. However, there is currently debate over when not to transfer a woman in labour, for risk of an in-transit birth, associated with worse neonatal outcomes. METHODS: A scoping review methodology was undertaken, to define the scope of published literature on the topic and identify gaps in the current knowledge. RESULTS: A total of seven full texts were deemed suitable for synthesis, which were all retrospective observational studies. Four themes from the studies' findings were identified: population features, predicting time-to-birth, use of tocolysis and birth during medical evacuation. CONCLUSION: The evidence identified in this review was of low methodological quality and heterogenous. The key findings were that births in-flight are rare, despite geographical distances and long transport times, with a knowledge gap on predictors of time-to-birth.

OBJECTIVE: The aim of this study was to describe the characteristics and outcomes of remote-dwelling pregnant women with threatened labor referred for air medical retrieval to a regional birthing center as well as factors associated with birth within 48 hours. METHODS: This was a retrospective observational study of all pregnant women in the remote Central Australian region referred to the Medical Retrieval Consultation and Coordination Centre for labor > 23 weeks' gestation between February 12, 2018, and February 12, 2020. Univariate and multivariate statistical analyses were performed. RESULTS: There were 116 women referred for retrieval for labor. There were no births during transport, and less than half of the cases resulted in birth within 48 hours of retrieval. Tocolysis was frequently used. Predictors of birth within 48 hours were cervical dilatation ≥ 5 cm, preterm gestational age, and ruptured membranes in the univariate analysis. Nearly one third of this cohort required intervention or had complications during birth. CONCLUSION: Birth during transport for threatened labor did not occur in this cohort, and more than half of the retrievals did not result in birth within 48 hours; however, the high risk of birth complications may offset any benefit of avoiding air medical transport from remote regions. Retrieval clinicians should consider urgent transfer in cases of ruptured membranes, cervical dilatation of 5 cm or more, or gestational age less than 37 weeks.

OBJECTIVE: To assess timeliness, efficiency, health outcomes and cost-effectiveness of the 2018 redesigned Central Australian aeromedical retrieval model. DESIGN: Pre- and postimplementation observational study of all patients receiving telehealth consultations from remote medical practitioners (RMPs) or Medical Retrieval and Consultation Centre (MRaCC) physicians between 1/1/2015 and 29/2/2020. Descriptive and inferential statistics measuring system efficiency, timeliness, health outcomes and incremental cost-effectiveness. FINDINGS: There were 9%-10% reductions in rates of total aeromedical retrievals, emergency department admissions and hospitalisations postimplementation, all p-values < 0.001. Usage rates for total hospital bed days and ICU hours were 17% lower (both p < 0.001). After adjusting for periodicity (12% fewer retrievals on weekends), each postimplementation year, there were 0.7 fewer retrievals/day (p = 0.002). The mean time from initial consultation to aeromedical departure declined by 18 minutes post-implementation (115 vs. 97 min, p = 0.007). The hazard of death within 365 days was nonsignificant (0.912, 95% CI 0.743-1.120). Postimplementation, it cost $302 more per hospital admission and $3051 more per year of life saved, with a 75% probability of cost-effectiveness. These costs excluded estimated savings of $744,528/year in reduced hospitalisations and the substantial social and out-of-pocket costs to patients and their families associated with temporary relocation to Alice Springs. CONCLUSION: Central Australia's new critical care consultant-led aeromedical retrieval model is more efficient, is dispatched faster and is more cost-effective. These findings are highly relevant to other remote regions in Australia and internationally that have comparable GP-led retrieval services.

Classifying free-text from historical databases into research-compatible formats is a barrier for clinicians undertaking audit and research projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed 'label-train-evaluate' loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. This machine-learning training method was able to classify free-text clinical notes quickly and accurately from two different health datasets into categories of relevance to clinicians undertaking health service research.