ABSTRACT
The COVID-19 pandemic response has accelerated adoption of digital health technologies to support social distancing. In part this involves repurposing technologies that were not originally developed for healthcare application. In the hospital setting this includes the appropriation of 'Off the Shelf ' (OTS) digital products that facilitate video-based clinical consultations diagnostics and communication during ward rounds and multi-disciplinary team meetings. Such technologies were implemented within weeks at GOSH where video consultations were implemented for >90% of outpatient appointments. Methods Here we present the findings from a Debrief-After Action Review (AAR) lasting two hours supplemented with speculative questions on how lessons learned from the rapid deployment of one technology could inform the adoption practices of other emerging technologies. Results Fifteen participants who were members of a Transforming Care Links working group and interested in the impact of digital systems on patient care contributed to the AAR. Five themes were identified from thematic analysis of this bottom-up approach: (i) Clinical: Planning and redesigning workflows with clear purpose intent and communication with clinical teams (ii) Technology: Infrastructure and equipment available across the team based on the needs of the workspace, with security and governance processes (iii) Capability mapping: Building core capability in a structured way across the entire team with space and time to trial out technologies as part of a progressive learning path and supported by clinical champions (iv) Benefits: Demonstrable benefits with new technology enabled ways of working based on preliminary small-scale deployments that deliver measurable value (v) Environment and Context: Context-specific workflow redesign for technology enabled interactions that consider optimum conditions of the physical environment. Conclusion This user-centred approach identified routine training pathways equity of access to training opportunities and equipment a period of trialability and demonstrable benefits as enablers for the successful adoption of emerging technologies.
ABSTRACT
Background: Adolescent youngsters' adherence to COVID-19 Appropriate Behaviour (CAB) is essential to forestall and oversee Covid illness 19. The review planned to decide the pervasiveness and related variables of adherence to CAB in juvenile youngsters by applying Health Benefit Model (HBM) and summing up friendly convictions. Techniques: This is a hospital-based, cross-sectional review done between January 202 to June 2022. A pre-tied, organized, questioner-regulated device was utilized to gather information from 384 members. Information was gathered from Outpatients after acquiring informed assent. Results: The mean (SD) period of members was 16.3 (2.84) years. Around 56% of members were male. Adherence to all CAB means was 23%, it was not viewed as associated with age and orientation. The most widely recognized rehearsed CAB measure was face cover (64.2%) trailed by handwashing (56.71%). Conclusion: The adherence to CAB was low among young adolescents. It is critical to consider the wellbeing training, parenteral help, social maxims, seriousness, benefit, boundary, prompt to activity, pessimism and compensation for application to work on the adherence towards CAB.
ABSTRACT
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans. Our system utilises a novel two-stage approach to generate robust and efficient diagnoses across heterogeneous patient level inputs. We use a powerful backbone network as a feature extractor to capture discriminative slice-level features. These features are aggregated by a lightweight network to obtain a patient level diagnosis. The aggregation network is carefully designed to have a small number of trainable parameters while also possessing sufficient capacity to generalise to diverse variations within different CT volumes and to adapt to noise introduced during the data acquisition. We achieve a significant performance increase over the baselines when benchmarked on the SPGC COVID-19 Radiomics Dataset, despite having only 2.5 million trainable parameters and requiring only 0.623 seconds on average to process a single patient's CT volume using an Nvidia-GeForce RTX 2080 GPU. © 2021 IEEE.