Clinician perspectives on rapid transition to telehealth during COVID-19 in Australia - a qualitative study.

Objective The coronavirus disease 2019 (COVID-19) pandemic precipitated a major shift in the use of telehealth in Australia. The changes highlighted gaps in our knowledge regarding the efficacy of, and clinician attitudes to, the use of telehealth. The current study expands and deepens the available evidence as a result of being collected in unique circumstances that removed one of the major barriers (lack of Medicare rebates) and also one major enablers (willingness) of telehealth uptake. Methods Using a semi-structured interview, we invited clinicians (N = 39) to share their perspectives, attitudes and experiences of using telehealth. Topics covered included perceptions of the strengths and challenges of telehealth, and how experience of using telehealth during the COVID-19 pandemic had influenced clinicians' views and intentions regarding their future practice. Participants included clinicians from five disciplines across public and private practice: paediatrics, neurology, immunology, rural general practice, and orthopaedics. Results We found three key dimensions for consideration when assessing the suitability of telehealth for ongoing practice: the attributes of the patient population, the attributes of the clinical context and environment, and the risks and benefits of a telehealth approach. These findings map to the existing literature and allow us to infer that the experiences of clinicians who previously would have chosen telehealth did not differ significantly from those of our 'pandemic-conscripted' clinicians. Conclusions Our findings map clearly to the existing literature and allow us to infer that the experiences of the clinicians who have chosen telehealth (and are already represented in the literature) did not differ significantly from those trying out telehealth under the unique circumstances of the removal of the Medicare Benefits Scheme barrier and external pressure that over-rides the 'willingness' enabling factor in uptake decisions.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

BACKGROUND: Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. METHODS: Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. RESULTS: Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.