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To develop a deep learning-based smart assessment model for pressure injury surface.
Exploratory analysis study.
Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.
From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).
The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.
This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.
The STROBE checklist guided study reporting.
Patients provided image resources for model training.
To identify the principal factors influencing the implementation of high-value healthcare performance evaluation and to examine the interrelationships among these factors.
Value-based health care (VBHC) is gaining momentum as a model that focuses on improving patient outcomes. However, there is still a lack of understanding of the multifaceted factors that contribute to its successful implementation.
Theoretical modelling and mixed research methods.
First, this study constructed a framework of influencing factors on the implementation of VBHC performance evaluation based on the Technology-Organization-Environment model. Second, a representative set of influencing factors for healthcare performance evaluation was identified. The implementation of performance evaluation was identified based on a literature analysis and a case study in China. Finally, experts were invited to assess the relevance of the aforementioned influencing factors, and the collected data were analysed using Interpretative Structural Model. The PRISMA-ScR checklist guided the reporting of this study.
We initially constructed the theory framework with the objective of categorising and summarising the influential factors and potential problems revealed in the implementation of patient VBHC performance evaluation in general hospitals. Subsequently, 15 key factors were identified through interviews with 10 experts. Then, a six-level hierarchy was developed to construct a visual structure diagram, the purpose of which was to clarify the hierarchy of roles of each influencing factor. Finally, we categorise the influencing factors into four clusters based on their driving power and dependency within the system.
The insights from this research will assist hospital managers in identifying and prioritising the key factors that influence high-value healthcare performance.
This study provides a reliable pathway reference for clinical and nursing performance value enhancement and provides important insights into resource allocation and decision-making for clinical practitioners.
No patient or public contribution.
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