Nurses' use of an advisory decision support system in ambulance services: A qualitative study.

AIM: To illuminate from the perspective of nurses in ambulance services the experiences of using a web-based advisory decision support system to assess care needs and refer patients. DESIGN: Inductive and descriptive approaches. METHOD: Thirteen semi-structured interviews were conducted in the spring of 2020. The data were analysed through the reflexive thematic analysis. RESULTS: The Swedish web-based advisory decision support system (ADSS) was found to strengthen nurses' feelings of security when they assess patients' care needs, promote their competence and professional pride, and help them manage stress. However, the system also generated difficulties for nurses to adjust to the dynamic ambulance team and revealed a discrepancy between their professional roles and responsibilities to refer patients and provide self-care advice. The nurses thought that the support system facilitated their increased participation and helped them understand patients and significant others by offering transparency in assessment and decision making. Thus, the support system provides nurses with an opportunity to strengthen patients' independence through information and education. However, in the care relationship, nurses worked to overcome patients' expectations. CONCLUSION: Nurses using the ADSS increased their security while performing assessments and referrals and found new opportunities to provide information and promote understanding of their decisions. However, nursing care values can be threatened when new support systems are introduced, especially as ambulance services become increasingly protocol-driven. IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE: These findings have implications for nurses' work environments and help them maintain consistency in making medical assessments and in providing equivalent self-care advice when referring patients to the different levels of care. The findings will also impact researchers and policymakers who formulate decision support systems. REPORTING METHOD: Consolidated criteria for reporting qualitative research (COREQ). PATIENT OR PUBLIC CONTRIBUTION: None.

Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release.

Phase-separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water-soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high-resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB-SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB-SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale-space features. Finally, we facilitate further development of FIB-SEM segmentation methods by making the data and software used open access.

Drug release from pharmaceutical tablets or pellets is often controlled by applying a phase-separated polymer film coating. Ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends are commonly used. The HPC phase leaches out when in contact with water and the result is a porous EC matrix coating, with mass transport properties that can be controlled by tailoring the structure of the porous network. High-resolution 3D imaging is necessary to characterize such materials, and the resolution of e.g. X-ray computed tomography is simply insufficient. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography on the other hand is a suitable technique, but segmentation of FIB-SEM data, in this case to separate the solid matrix and the porous network, is challenging. In this work, we develop a method for segmentation of FIB-SEM image data acquired from three different EC porous films where the HPC phases have been leached out. The segmentation is based on convolutional neural networks (CNNs). CNNs is a well-established machine learning paradigm and has demonstrated state-of-the-art performance in many image analysis and segmentation tasks. CNNs are inspired from biological processes in the visual cortex and act similarly, at least conceptually. In contrast to most conventional machine learning algorithms, CNNs learn by themselves which features to extract from the images. The features are extracted at different spatial scales and may constitute e.g. edge and contrast detectors. These features are subsequently used for classification. In this work, CNNs are used for image segmentation. The goal is to identify which regions in the images that contain either pore (empty space) or solid (material), hence a binary classification task. For the CNN to learn how to perform such a task, a ground truth is needed. This is achieved by letting an expert manually segment parts of the data. This is a very time-consuming endeavor, hence only a small random subset of the full dataset is manually segmented. The CNN is trained for the task using the manually segmented data, after which automatic segmentation of the full dataset is performed. We obtain very good agreement with manual segmentations in terms of accuracy and porosity, and a clear improvement in comparison to an earlier developed random forest classifier trained on Gaussian scale-space features on the same data. The development of accurate segmentation methods is a crucial step toward better understanding and tailoring of coatings for controlled drug release.

New Characterization Measures of Pore Shape and Connectivity Applied to Coatings used for Controlled Drug Release.

Pore geometry characterization-methods are important tools for understanding how pore structure influences properties such as transport through a porous material. Bottlenecks can have a large influence on transport and related properties. However, existing methods only catch certain types of bottleneck effects caused by variations in pore size. We here introduce a new measure, geodesic channel strength, which captures a different type of bottleneck effect caused by many paths coinciding in the same pore. We further develop new variants of pore size measures and propose a new way of visualizing 3-D characterization results using layered images. The new measures together with existing measures were used to characterize and visualize properties of 3-D FIB-SEM images of three leached ethyl-cellulose/hydroxypropyl-cellulose films. All films were shown to be anisotropic, and the strongest anisotropy was found in the film with lowest porosity. This film had very tortuous paths and strong geodesic channel-bottlenecks, while the paths through the other two films were relatively straight with well-connected pore networks. The geodesic channel strength was shown to give important new visual and quantitative insights about connectivity, and the new pore size measures provided useful information about anisotropies and inhomogeneities in the pore structures. The methods have been implemented in the freely available software MIST.

Optimization of FIB-SEM Tomography and Reconstruction for Soft, Porous, and Poorly Conducting Materials.

Tomography using a focused ion beam (FIB) combined with a scanning electron microscope (SEM) is well-established for a wide range of conducting materials. However, performing FIB­SEM tomography on ion- and electron-beam-sensitive materials as well as poorly conducting soft materials remains challenging. Some common challenges include cross-sectioning artifacts, shadowing effects, and charging. Fully dense materials provide a planar cross section, whereas pores also expose subsurface areas of the planar cross-section surface. The image intensity of the subsurface areas gives rise to overlap between the grayscale intensity levels of the solid and pore areas, which complicates image processing and segmentation for three-dimensional (3D) reconstruction. To avoid the introduction of artifacts, the goal is to examine porous and poorly conducting soft materials as close as possible to their original state. This work presents a protocol for the optimization of FIB­SEM tomography parameters for porous and poorly conducting soft materials. The protocol reduces cross-sectioning artifacts, charging, and eliminates shadowing effects. In addition, it handles the subsurface and grayscale intensity overlap problems in image segmentation. The protocol was evaluated on porous polymer films which have both poor conductivity and pores. 3D reconstructions, with automated data segmentation, from three films with different porosities were successfully obtained.

Fabrication and Characterization of Plasmonic Nanopores with Cavities in the Solid Support.

Plasmonic nanostructures are widely used for various sensing applications by monitoring changes in refractive index through optical spectroscopy or as substrates for surface enhanced Raman spectroscopy. However, in most practical situations conventional surface plasmon resonance is preferred for biomolecular interaction analysis because of its high resolution in surface coverage and the simple single-material planar interface. Still, plasmonic nanostructures may find unique sensing applications, for instance when the nanoscale geometry itself is of interest. This calls for new methods to prepare nanoscale particles and cavities with controllable dimensions and curvature. In this work, we present two types of plasmonic nanopores where the solid support underneath a nanohole array has been etched, thereby creating cavities denoted as 'nanowells' or 'nanocaves' depending on the degree of anisotropy (dry or wet etch). The refractometric sensitivity is shown to be enhanced upon removing the solid support because of an increased probing volume and a shift of the asymmetric plasmonic field towards the liquid side of the finite gold film. Furthermore, the structures exhibit different spectral changes upon binding inside the cavities compared to the gold surface, which means that the structures can be used for location-specific detection. Other sensing applications are also suggested.