Conectar
A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.
A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.
The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.
The study's main objective was to use a fuzzy set qualitative comparative analysis to identify the configuration of recipes that predict nurses' safety compliance behaviour.
A cross-sectional design.
A survey was used where questionnaires were collected from 285 nurses across four primary healthcare hospitals within the Ashanti Region, Ghana. The data collection happened between June 1 to August 2, 2022. A fuzzy set qualitative comparative analysis was used to identify the recipes of psychological factors that determine nurses' safety compliance behaviour.
Results from the study suggest that the necessary configurations that explained nurses' safety compliance behaviour came from the presence of subjective norm, attitude, perceived behavioural control, perceived organizational support and negation of intention. The result highlights the need for safety protocols to be conscious of the interplay between nurses' assessment of self, social clues and perception of management care and support since such psychological factors must be considered concurrently to achieve the optimal safety compliance behaviour among nurses.
A health and safety protocol that fails to recognize the importance of psychological antecedents on subordinates' safety compliance behaviour could limit the safety policy's usefulness in bringing the appropriate behavioural change in nurses.
To date, no study has combined the antecedents of theory planned behaviour with perceived organizational support and cue to action to assess how they collectively predict nurses' safety compliance behaviour. Findings from the study suggest that nurses in primary health facilities inform their safety compliance behaviour by assessing self-capabilities, social signals from superiors and colleagues and perception of management support. Hospital administrators and nursing managers in sub-Saharan Africa may rely on these psychological forces to persuade nurses to develop positive safety compliance behaviour at the health facility.
No Patient or Public Contribution.
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