Health consumers' ethical concerns towards artificial intelligence in Australian emergency departments.

OBJECTIVES: To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs. METHODS: Qualitative semi-structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022. RESULTS: We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision-making process. There is considerably more approval of AI tools that support clinical decision-making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision-making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI. CONCLUSION: Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited.

Machine learning in clinical practice: Evaluation of an artificial intelligence tool after implementation.

OBJECTIVE: Artificial intelligence (AI) has gradually found its way into healthcare, and its future integration into clinical practice is inevitable. In the present study, we evaluate the accuracy of a novel AI algorithm designed to predict admission based on a triage note after clinical implementation. This is the first of such studies to investigate real-time AI performance in the emergency setting. METHODS: The novel AI algorithm that predicts admission using a triage note was translated into clinical practice and integrated within St Vincent's Hospital Melbourne's electronic emergency patient management system. The data were collected from 1 January 2021 to 17 August 2022 to evaluate the diagnostic accuracy of the AI system after implementation. RESULTS: A total of 77 125 ED presentations were included. The live AI algorithm has a sensitivity of 73.1% (95% confidence interval 72.5-73.8), specificity of 74.3% (73.9-74.7), positive predictive value of 50% (49.6-50.4) and negative predictive value of 88.7% (88.5-89) with a total accuracy of 74% (73.7-74.3). The accuracy of the system was at the lowest for admission to psychiatric units (34%) and at the highest for gastroenterology and medical admission (84% and 80%, respectively). CONCLUSION: Our study showed the diagnostic evaluation of a real-time AI clinical decision-support tool became less accurate than the original. Although real-time sensitivity and specificity of the AI tool was still acceptable as a decision-support tool in the ED, we propose that continuous training and evaluation of AI-enabled clinical support tools in healthcare are conducted to ensure consistent accuracy and performance to prevent inadvertent consequences.

Applications of natural language processing at emergency department triage: A narrative review.

INTRODUCTION: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data. METHODS: All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided. RESULTS: In total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice. CONCLUSION: Unstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.

Use of autologous fat grafting in reconstruction following mastectomy and breast conserving surgery: An updated systematic review protocol.

INTRODUCTION: Use of autologous fat grafting (AFG) for breast reconstructive surgery is gaining acceptance, but concerns regarding its efficacy and safety remain. We present a protocol for a systematic review that aims to update the findings since our previous systematic review on a number of outcomes of AFG. METHODS: The systematic review has been registered a priori (UIN: reviewregistry308). All study designs, including randomised controlled trials, cohort studies, case-controlled studies and case reports/series, reporting original data, on women undergoing AFG for breast reconstruction following mastectomy or breast conserving surgery, will be included. Six categorical outcomes will be assessed: oncological; clinical; aesthetic/functional; patient-reported; process; and radiological.The search strategy will be devised to investigate 'fat grafting and breast reconstruction'. Electronic databases will be searched, 01 April 2014 to 21 August 2017: PubMed, MEDLINE®, EMBASE, SCOPUS, CINAHL, PsychINFO, SciELO, The Cochrane Library, including the Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Reviews of Effect (DARE), the Cochrane Methodology Register, Health Technology Assessment Database, the NHS Economic Evaluation Databases and Cochrane Groups, ClinicalTrials.gov, Current Controlled Trials Database, the World Health Organisation (WHO) International Clinical Trials Registry Platform, UpToDate.com, NHS Evidence and the York Centre for Reviews and Dissemination. Grey literature will be searched. Two trained, independent teams will screen all titles and abstracts, and relevant full texts, for eligibility. Data will be extracted under standardised extraction fields into a preformatted database. ETHICS AND DISSEMINATION: The systematic review will be published in a peer-reviewed journal and presented at national and international meetings within fields of plastic, reconstructive and aesthetic surgery, and surgical oncology. The work will be disseminated electronically and in print. Brief reports of the review and findings will be disseminated to interested parties through email and direct communication. The review aims to guide healthcare practice and policy.

A new system for comparative functional genomics of Saccharomyces yeasts.

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.

Genome stability of Lyme disease spirochetes: comparative genomics of Borrelia burgdorferi plasmids.

Lyme disease is the most common tick-borne human illness in North America. In order to understand the molecular pathogenesis, natural diversity, population structure and epizootic spread of the North American Lyme agent, Borrelia burgdorferi sensu stricto, a much better understanding of the natural diversity of its genome will be required. Towards this end we present a comparative analysis of the nucleotide sequences of the numerous plasmids of B. burgdorferi isolates B31, N40, JD1 and 297. These strains were chosen because they include the three most commonly studied laboratory strains, and because they represent different major genetic lineages and so are informative regarding the genetic diversity and evolution of this organism. A unique feature of Borrelia genomes is that they carry a large number of linear and circular plasmids, and this work shows that strains N40, JD1, 297 and B31 carry related but non-identical sets of 16, 20, 19 and 21 plasmids, respectively, that comprise 33-40% of their genomes. We deduce that there are at least 28 plasmid compatibility types among the four strains. The B. burgdorferi ∼900 Kbp linear chromosomes are evolutionarily exceptionally stable, except for a short ≤20 Kbp plasmid-like section at the right end. A few of the plasmids, including the linear lp54 and circular cp26, are also very stable. We show here that the other plasmids, especially the linear ones, are considerably more variable. Nearly all of the linear plasmids have undergone one or more substantial inter-plasmid rearrangements since their last common ancestor. In spite of these rearrangements and differences in plasmid contents, the overall gene complement of the different isolates has remained relatively constant.

Cytometric profiling in multiple sclerosis uncovers patient population structure and a reduction of CD8low cells.

As part of a biomarker discovery effort in peripheral blood, we acquired an immunological profile of cell-surface markers from healthy control and untreated subjects with relapsing-remitting MS (RRMS). Fresh blood from each subject was screened ex vivo using a panel of 50 fluorescently labelled monoclonal antibodies distributed amongst 56 pools of four antibodies each. From these 56 pools, we derived an immunological profile consisting of 1018 'features' for each subject in our analysis using a systematic gating strategy. These profiles were interrogated in an analysis with a screening phase (23 patients) and an extension phase (15 patients) to identify cell populations in peripheral blood whose frequency is altered in untreated RRMS subjects. A population of CD8(low)CD4(-) cells was identified as being reduced in frequency in untreated RRMS subjects (P = 0.0002), and this observation was confirmed in an independent sample of subjects from the Comprehensive Longitudinal Investigation of MS at the Brigham & Women's Hospital (P = 0.002). This reduction in the frequency of CD8(low)CD4(-) cells is also observed in 38 untreated subjects with a clinically isolated demyelination syndrome (CIS) (P = 0.0006). We also show that these differences may be due to a reduction in the CD8(low)CD56(+)CD3(-)CD4(-) subset of CD8(low) cells, which have a natural killer cell profile. Similarities between untreated CIS and RRMS subjects extend to broader immunological profiles: consensus clustering of our data suggests that there are three distinct populations of untreated RRMS subjects and that these distinct phenotypic categories are already present in our sample of untreated CIS subjects. Thus, our large-scale immunophenotyping approach has yielded robust evidence for a reduction of CD8(low)CD4(-) cells in both CIS and RRMS in the absence of treatment as well as suggestive evidence for the existence of immunologically distinct subsets of subjects with a demyelinating disease.