Exploring Congruence Between Patient and Clinician Expectations of Benefit in the Non-Surgical Management of Common Musculoskeletal Conditions in Tertiary Care.

Background: Patient and clinician expectations of benefit from recommended management approaches may potentially impact the success of managing musculoskeletal conditions.

Methods: This was a multisite study in an advanced practice musculoskeletal service across Queensland, Australia. Relationships between patient and clinician (advanced physiotherapy practitioner) expectations of benefit, patient characteristics, and clinical outcome recorded 6 months later were explored with regression analysis in 619 patients undergoing non-surgical multidisciplinary care for either knee osteoarthritis (n = 286), low back pain (n = 249) or shoulder impingement syndrome (n = 84).

Transcutaneous Auricular Vagus Nerve Stimulation for Visually Induced Motion Sickness: An eLORETA Study.

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive form of electrical brain stimulation, has shown potent therapeutic potential for a wide spectrum of conditions. How taVNS influences the characterization of motion sickness - a long mysterious syndrome with a polysymptomatic onset - remains unclear. Here, to examine taVNS-induced effects on brain function in response to motion-induced nausea, 64-channel electroencephalography (EEG) recordings from 42 healthy participants were analyzed; collected during nauseogenic visual stimulation concurrent with taVNS administration, in a crossover randomized sham-controlled study.

Transcutaneous Auricular Vagus Nerve Stimulation towards Visually Induced Motion Sickness Reduction: A Pilot Study.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel neuromodulation application for vagal afferent stimulation. Owing to its non-invasive nature, taVNS is a potent therapeutic tool for a diverse array of diseases and disorders that ail us. Herein, we investigated taVNS-induced effects on neural activity of participants during visually induced motion sickness.

Heart Rate Variability Responses to Visually Induced Motion Sickness.

Analysis of heart rate variability (HRV) can reveal a range of useful information regarding the dynamics of the autonomic nervous system (ANS). It is considered a robust and reliable tool to understand even some subtle changes in ANS activity. Here, we study the "hidden" characteristic changes in HRV during visually induced motion sickness; using nonlinear analytical methods, supplemented by conventional time- and frequency-domain analyses.

Did You Hear That? Detecting Auditory Events with EEGNet.

The behavioural nature of pure-tone audiometry (PTA) limits those who can participate in the test, and therefore those who can access accurate hearing threshold measurements. Event Related Potentials (ERPs) from brain signals has shown limited utility on adult subjects, and a neural response that can consistently be identified as a result of pure-tone auditory stimulus has yet to be identified. The in doing so challenge is worsened by the nature of PTA, where stimulus amplitude decrease to a patient's lower threshold of hearing.

On the potential of transauricular electrical stimulation to reduce visually induced motion sickness.

Perturbations in the autonomic nervous system occur in individuals experiencing increasing levels of motion sickness. Here, we investigated the effects of transauricular electrical stimulation (tES) on autonomic function during visually induced motion sickness, through the analysis of spectral and time-frequency heart rate variability. To determine the efficacy of tES, we compared sham and tES conditions in a randomized, within-subjects, cross-over design in 14 healthy participants.

Creating conditions for effective knowledge brokering: a qualitative case study.

Background: Process improvement in healthcare is informed by knowledge from the private sector. Skilled individuals may aid the adoption of this knowledge by frontline care delivery workers through knowledge brokering. However, the effectiveness of those who broker knowledge is limited when the context they work within proves unreceptive to their efforts.

An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies.

This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles collected from patients are transformed into time-frequency spectrograms to serve as front-end two-dimensional features. Cropped spectrogram segments are then used to train a range of back-end deep learning networks to classify respiratory cycles into predefined medically-relevant categories.

Vibro-motor Reprocessing Therapy towards Managing Motion Sickness Reduction: Evidence from EEG.

This study examines the neural activities of participants undergoing vibro-motor reprocessing therapy (VRT) while experiencing motion sickness. We evaluated the efficacy of vibro-motor reprocessing therapy, a novel therapeutic technique based on eye movement desensitization and reprocessing (EMDR), in reducing motion sickness. Based on visually induced motion sickness in two sets of performed sessions, eight participants were exposed to VRT stimulation in a VRT/non-VRT setting.

Integrating the complexity of healthcare improvement with implementation science: a longitudinal qualitative case study.

Background: Implementation science seeks to enable change, underpinned by theories and frameworks such as the Consolidated Framework for Implementation Research (CFIR). Yet academia and frontline healthcare improvement remain largely siloed, with limited integration of implementation science methods into frontline improvement where the drivers include pragmatic, rapid change. Using the CIFR lens, we aimed to explore how pragmatic and complex healthcare improvement and implementation science can be integrated.

Inception-Based Network and Multi-Spectrogram Ensemble Applied To Predict Respiratory Anomalies and Lung Diseases.

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are first transformed into spectrograms where both spectral and temporal information are well represented, in a process referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, in a process referred to as back-end classification, for detecting whether patients suffer from lung-related diseases.

Engaging Healthcare Staff and Stakeholders in Healthcare Simulation Modeling to Better Translate Research Into Health Impact: A Systematic Review.

To identify processes to engage stakeholders in healthcare Simulation Modeling (SM), and the impacts of this engagement on model design, model implementation, and stakeholder participants. To investigate how engagement process may lead to specific impacts. English-language articles on health SM engaging stakeholders in the MEDLINE, EMBASE, Scopus, Web of Science and Business Source Complete databases published from inception to February 2020.

Using institutional entrepreneurship to understand the role of innovation teams in healthcare: a longitudinal qualitative study.

Objectives: We draw on institutional theory to explore the roles and actions of innovation teams and how this influences their behaviour and capabilities as 'institutional entrepreneurs (IEs)', in particular the extent to which they are both 'willing' and 'able' to facilitate transformational change in healthcare through service redesign.

Design: A longitudinal qualitative study that applied a 'researcher in residence' as an ethnographic approach.

Setting: The development and implementation of two innovation projects within a single public hospital setting in an Australian state jurisdiction.

D-MONA: A dilated mixed-order non-local attention network for speaker and language recognition.

Attention-based convolutional neural network (CNN) models are increasingly being adopted for speaker and language recognition (SR/LR) tasks. These include time, frequency, spatial and channel attention, which can focus on useful time frames, frequency bands, regions or channels while extracting features. However, these traditional attention methods lack the exploration of complex information and multi-scale long-range speech feature interactions, which can benefit SR/LR tasks.

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation.

Academic Health Science Centres as Vehicles for Knowledge Mobilisation in Australia? A Qualitative Study.

Background: Despite increasing investments in academic health science centres (AHSCs) in Australia and an expectation that they will serve as vehicles for knowledge translation and exchange, there is limited empirical evidence on whether and how they deliver impact. The aim of this study was to examine and compare the early development of four Australian AHSCs to explore how they are enacting their impact-focused role.

Methods: A descriptive qualitative methodology was employed across four AHSCs located in diverse health system settings in urban and regional locations across Australia.

Patient characteristics associated with a poor response to non-surgical multidisciplinary management of knee osteoarthritis: a multisite prospective longitudinal study in an advanced practice physiotherapist-led tertiary service.

Objectives: To explore patient characteristics recorded at the initial consultation associated with a poor response to non-surgical multidisciplinary management of knee osteoarthritis (KOA) in tertiary care.

Design: Prospective multisite longitudinal study.

Setting: Advanced practice physiotherapist-led multidisciplinary orthopaedic service within eight tertiary hospitals.

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases.

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly.

Automated Assessment of Glottal Dysfunction Through Unified Acoustic Voice Analysis.

This paper uses the recent glottal flow model for iterative adaptive inverse filtering to analyze recordings from dysfunctional speakers, namely those with larynx-related impairment such as laryngectomy. The analytical model allows extraction of the voice source spectrum, described by a compact set of parameters. This single model is used to visualize and better understand speech production characteristics across impaired and nonimpaired voices.

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging.

Methods: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning.

An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system.

Given the pace of technological advancement and government mandates for healthcare and system transformation, there is an imperative for change. Health systems are highly complex in their design, networks and interacting components, and experience demonstrates that change is very challenging to enact, sustain and scale. Policy-makers, academics and clinicians all need better insight into the nature of this complexity and an understanding of the evidence-base that can support healthcare improvement (HCI), or quality improvement, interventions and make them more effective in driving change.

Agreement between telehealth and in-person assessment of patients with chronic musculoskeletal conditions presenting to an advanced-practice physiotherapy screening clinic.

Objective: To determine the level of agreement between a telehealth and in-person assessment of a representative sample of patients with chronic musculoskeletal conditions referred to an advanced-practice physiotherapy screening clinic.

Design: Repeated-measures study design.

Participants: 42 patients referred to the Neurosurgical & Orthopaedic Physiotherapy Screening Clinic (Queensland, Australia) for assessment of their chronic lumbar spine, knee or shoulder condition.

Examining 'institutional entrepreneurship' in healthcare redesign and improvement through comparative case study research: a study protocol.

Introduction: Healthcare service redesign and improvement has become an important activity that health system leaders and clinicians realise must be nurtured and mastered, if the capacity issues that constrain healthcare delivery are to be solved. However, little is known about the critical success factors that are essential for sustaining and scaling up improvement initiatives. This situation limits the impact of these initiatives and undermines the general standing of redesign and improvement activity within healthcare systems.

Continuous robust sound event classification using time-frequency features and deep learning.

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds.