Medical practitioner perspectives on AI in Emergency Triage

Background: A proposed Diagnostic AI System for Robot-Assisted Triage (‘DAISY’) is under development to support Emergency Department (‘ED’) triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England (‘NHS’) but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. Objective: The objective of this study was to explore NHS health practitioners’ perspectives and attitudes towards the future use of AI-supported technologies in ED triage. Methods: Between July and August 2022 a qualitative-exploratory research study was conducted to collect and capture the perceptions and attitudes of nine NHS healthcare practitioners to better understand the challenges and benefits of a DAISY deployment. The study was based on a thematic analysis of semi-structured interviews. The study involved qualitative data analysis of the interviewees’ responses. Audio-recordings were transcribed verbatim, and notes included into data documents. The transcripts were coded line-by-line, and data were organised into themes and sub-themes. Both inductive and deductive approaches to thematic analysis were used to analyse such data. Results: Based on a qualitative analysis of coded interviews with the practitioners, responses were categorised into broad main thematic-types, namely: trust; current practice; social, legal, ethical, and cultural concerns; and empathetic practice. Sub-themes were identified for each main theme. Further quantitative analyses explored the vocabulary and sentiments of the participants when talking generally about NHS ED practices compared to discussing DAISY. Limitations include a small sample size and the requirement that research participants imagine a prototype AI-supported system still under development. The expectation is that such a system would work alongside the practitioner. Findings can be generalisable to other healthcare AI-supported systems and to other domains. Conclusions: This study highlights the benefits and challenges for an AI-supported triage healthcare solution. The study shows that most NHS ED practitioners interviewed were positive about such adoption. Benefits cited were a reduction in patient wait times in the ED, assistance in the streamlining of the triage process, support in calling for appropriate diagnostics and for further patient examination, and identification of those very unwell and requiring more immediate and urgent attention. Words used to describe the system were that DAISY is a ‘good idea’, ‘help’, helpful, ‘easier’, ‘value’, and ‘accurate’. Our study demonstrates that trust in the system is a significant driver of use and a potential barrier to adoption. Participants emphasised social, legal, ethical, and cultural considerations and barriers to DAISY adoption and the importance of empathy and non-verbal cues in patient interactions. Findings demonstrate how DAISY might support and augment human medical performance in ED care, and provide an understanding of attitudinal barriers and considerations for the development and implementation of future triage AI-supported systems

Medical practitioner perspectives on AI in emergency triage

IntroductionA proposed Diagnostic AI System for Robot-Assisted Triage (“DAISY”) is under development to support Emergency Department (“ED”) triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England (“NHS”) but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. The objective of this study was to explore NHS health practitioners' perspectives and attitudes towards the future use of AI-supported technologies in ED triage.MethodsBetween July and August 2022 a qualitative-exploratory research study was conducted to collect and capture the perceptions and attitudes of nine NHS healthcare practitioners to better understand the challenges and benefits of a DAISY deployment. The study was based on a thematic analysis of semi-structured interviews. The study involved qualitative data analysis of the interviewees' responses. Audio-recordings were transcribed verbatim, and notes included into data documents. The transcripts were coded line-by-line, and data were organised into themes and sub-themes. Both inductive and deductive approaches to thematic analysis were used to analyse such data.ResultsBased on a qualitative analysis of coded interviews with the practitioners, responses were categorised into broad main thematic-types, namely: trust; current practice; social, legal, ethical, and cultural concerns; and empathetic practice. Sub-themes were identified for each main theme. Further quantitative analyses explored the vocabulary and sentiments of the participants when talking generally about NHS ED practices compared to discussing DAISY. Limitations include a small sample size and the requirement that research participants imagine a prototype AI-supported system still under development. The expectation is that such a system would work alongside the practitioner. Findings can be generalisable to other healthcare AI-supported systems and to other domains.DiscussionThis study highlights the benefits and challenges for an AI-supported triage healthcare solution. The study shows that most NHS ED practitioners interviewed were positive about such adoption. Benefits cited were a reduction in patient wait times in the ED, assistance in the streamlining of the triage process, support in calling for appropriate diagnostics and for further patient examination, and identification of those very unwell and requiring more immediate and urgent attention. Words used to describe the system were that DAISY is a “good idea”, “help”, helpful, “easier”, “value”, and “accurate”. Our study demonstrates that trust in the system is a significant driver of use and a potential barrier to adoption. Participants emphasised social, legal, ethical, and cultural considerations and barriers to DAISY adoption and the importance of empathy and non-verbal cues in patient interactions. Findings demonstrate how DAISY might support and augment human medical performance in ED care, and provide an understanding of attitudinal barriers and considerations for the development and implementation of future triage AI-supported systems

The virtual landing pad: facilitating rotary-wing landing operations in degraded visual environments

The safety of rotary-wing operations are significantly affected by the local weather conditions, especially during key phases of flight including hover and landing. Despite the operational flexibility of rotary-wing craft, such craft account for a significantly greater proportion of accidents than their fixed-wing counterparts. A key period of risk when operating rotary-wing aircraft is during operations that occur in degraded visual environments, for example as a result of thick fog. During such conditions, pilots’ workload significantly increases and their situation awareness can be greatly impeded. The current study examines the extent to which providing information to pilots via the use of a Head-Up display (HUD) influenced perceived workload and situation awareness, when operating in both clear and degraded visual environments. Results suggest that whilst the HUD did not benefit pilots during clear conditions, workload was reduced when operating in degraded visual conditions. Overall results demonstrate that access to the HUD reduces the difficulties associated with flying in degraded visual environments

Students as Academic Partners or Co-Researchers: An Action Research Involving Pre-Qualifying Nursing Students

This essay presents a case study outlining how an academic team engaged undergraduate Nursing students as co-researchers in an education research project. The student co-researchers were tasked by the team to lead on the second phase of the research project that looked to capture the learning experience of first-year BSc (Hons) Nursing degree students (N = 437) who had recently taken an Objective Structured Clinical Examination as an assessment. This case study primarily focuses on the recruitment process of students as appropriate co-researchers and how these were subsequently “up-skilled” to lead and undertake the second phase of the research. Hence, no emphasis is placed on the methodological detail of the broader research project. A key facet of the research project was to further develop a range of academic and research skills of the student co-researchers. Encouraging the student co-researchers to write a reflective diary allowed them to consider the impact of the experience on their personal and professional development, skills acquisition, and employability. The benefits and drawbacks of involving students as co-researchers will also be discussed, along with implications for future research practice

Applying the AcciMap methodology to investigate the tragic Mirsharai road accident in Bangladesh

A serious road accident happened on Monday, 11 July, 2011 afternoon in Mirsharai sub-district of Bangladesh's Chittagong district, that killed at least 44 schoolboys injured many more when the truck in which they were traveling skidded off a highway and plunged into a canal. In this study, the AcciMap methodology is applied, an extension to Rasmussen’s Risk Management Framework. This sociotechnical framework provides an insight into the interactions between different levels of a system, and supports the development of recommendations that could mitigate or prevent the outcomes of such an incident in the future. In the case of Mirsharai accident, the police and social media put the blame on the truck driver alone; however, this study, based on popular media reports, accident investigation reports and local interviews revealed mismanagement and an apparent disregard for safety across different levels of the socio-technical system. By integrating the different levels of the system, from international committees to end users, the road accident scenario of Bangladesh can be improved through developing a systematic approach to accident analysis

Developing a reduced consumer-led lexicon to measure emotional response to beer

Previous researchers have recently recommended and utilised consumer-led lexicons to measure emotional response. This study further advances this approach by 1) making the lexicon generation process more efficient by using consumer focus groups as opposed to individual consumer interviews and 2) decreasing the number of responses required from each consumer by reducing the lexicon to categories of similar terms. In response to 10 lager samples which were manipulated in order to control selected sensory properties, focus groups generated a lexicon of 44 emotion terms. This lexicon was reduced to 12 distinct emotion categories using linguistic checks and cluster analysis. Naïve beer consumers (n = 113) used these 12 emotion categories to rate their emotional response to the 10 samples. The reduced consumer-led lexicon was validated through its ability to discriminate across samples as well as show differences in emotional response between genders and age groups. The 12 emotion categories were found to discriminate well between samples, although a number of categories grouped samples similarly. However, differences in responses to otherwise comparable emotion categories were identified between genders and age groups, highlighting the importance of including all emotion categories so as to not over-reduce the lexicon and risk missing out on valuable emotion data

Estrogen/Estrogen Receptor Alpha Signaling in Mouse Posterofrontal Cranial Suture Fusion

BACKGROUND: While premature suture fusion, or craniosynostosis, is a relatively common condition, the cause is often unknown. Estrogens are associated with growth plate fusion of endochondral bones. In the following study, we explore the previously unknown significance of estrogen/estrogen receptor signaling in cranial suture biology. METHODOLOGY/PRINCIPAL FINDINGS: Firstly, estrogen receptor (ER) expression was examined in physiologically fusing (posterofrontal) and patent (sagittal) mouse cranial sutures by quantitative RT-PCR. Next, the cranial suture phenotype of ER alpha and ER beta knockout (alphaERKO, betaERKO) mice was studied. Subsequently, mouse suture-derived mesenchymal cells (SMCs) were isolated; the effects of 17-beta estradiol or the estrogen antagonist Fulvestrant on gene expression, osteogenic and chondrogenic differentiation were examined in vitro. Finally, in vivo experiments were performed in which Fulvestrant was administered subcutaneously to the mouse calvaria. Results showed that increased ERalpha but not ERbeta transcript abundance temporally coincided with posterofrontal suture fusion. The alphaERKO but not betaERKO mouse exhibited delayed posterofrontal suture fusion. In vitro, addition of 17-beta estradiol enhanced both osteogenic and chondrogenic differentiation in suture-derived mesenchymal cells, effects reversible by Fulvestrant. Finally, in vivo application of Fulvestrant significantly diminished calvarial osteogenesis, inhibiting suture fusion. CONCLUSIONS/SIGNIFICANCE: Estrogen signaling through ERalpha but not ERbeta is associated with and necessary for normal mouse posterofrontal suture fusion. In vitro studies suggest that estrogens may play a role in osteoblast and/or chondrocyte differentiation within the cranial suture complex

Reliably predicting pollinator abundance: challenges of calibrating process-based ecological models

1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. 2. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process. 3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. 4. Our results highlight a key universal challenge of calibrating spatially-explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that is often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data