ABSTRACT
Background: While the UK's Royal College of General Practitioners developed guidance concerning the delivery of essential services during the COVID pandemic, no such guidance was available in Australia and little is known about the experiences or approaches taken by general practitioners (GPs) for the delivery of care in Australia. Aims/Objectives: To describe GPs' experiences and approaches to delivering essential clinical services (ECS) during COVID lockdowns. Method(s): A survey of GPs who had practiced during lockdowns in Melbourne and Sydney. Questions focused on changes made to care delivery including what services were continued: (1) regardless of outbreak scale, (2) if capacity allowed, or (3) postponed. Finding(s): Of 274 completed surveys, 95% of participants reported increased use of telehealth for diagnosis, investigation, and management of clinical conditions, and 97% for follow-up. Time-sensitive services such as provision of care for symptoms consistent with cancer or those with immediate health impact (e.g., immunisations, prolia injections) were generally continued even if requiring face-to face delivery. Consultations involving screening or health assessments or those necessitating face-to-face care but not clinically urgent (e.g., low risk cervical cancer screening and IUD insertions), were more likely to be postponed, as were visits to homebound and nursing home patients. Implications: The almost universal uptake of telehealth by GPs supported continuity of service provision during lockdown. Australian GPs acted autonomously to triage and provide ECS face to face through the lockdowns. To optimise future preparedness, local guidance for safe delivery of ECS must be developed considering contextual factors relevant to the Australian primary healthcare system.
ABSTRACT
Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide range of medical conditions can indicate interstitial lung diseases. Respiratory diseases impose an immense worldwide health burden. It is even more deadly when considering COVID-19 in present times. Auscultation is the most common and primary method of respiratory disease diagnosis. It is known to be non-expensive, non-invasive, safe, and takes less time for diagnosis. However, diagnosis accuracy using auscultation is subjective to the experience and knowledge of the physician, and it requires extensive training. This study proposes a solution developed for respiratory disease diagnosis. 'smart Stethoscope' is an intelligent platform for providing assistance in respiratory disease diagnosis and training of novice physicians, which is powered by state-of-the-art artificial intelligence. This system performs 3 main functions(modes). These 3 modes are a unique aspect of this study. The real-time prediction mode provides real-time respiratory diagnosis predictions for lung sounds collected via auscultation. The offline training mode is for trainee doctors and medical students. Finally, the expert mode is used to continuously improve the system's prediction performance by getting validations and evaluations from pulmonologists. The smart stethoscope's respiratory disease diagnosis prediction model is developed by combining a state-of-the-art neural network with an ensembling convolutional recurrent neural network. The proposed convolutional Bi-directional Long Short-Term Memory (C- Bi LSTM) model achieved an accuracy of 98% on 6 class classification of breathing cycles for ICBHF17 scientific challenge respiratory sound database. The novelty of the project lies on the whole platform which provides different functionalities for a diverse hierarchy of medical professionals which supported by a state-of-the-art prediction model based on Deep Learning. © 2022 IEEE.