Setting the surgical wound care agenda across two healthcare districts: A priority setting approach

Patient participation in surgical wound care in acute care settings: An integrative review

BACKGROUND

Surgical site infections can significantly impact postoperative recovery. Patient participation, which involves patients actively engaging in wound care, has been linked to improved healing and reduced wound complications. However, there is limited synthesis of the literature that explores the patient's role and participation in the context of surgical wound care.

OBJECTIVE

To explore patients' perceptions of how they participate in surgical wound care, within 30 days post-operation.

DESIGN

An integrative review guided by Whittemore and Knafl's methodology. This review was registered with PROSPERO (CRD42022363669).

DATA SOURCES

Searches were conducted in Medline (Ovid), CINAHL (Complete), and EMBASE (Elsevier) databases in October 2023, supplemented by forward and backward citation searching.

REVIEW METHODS

Based on a priori eligibility criteria, two authors independently screened articles to select relevant studies. The quality of the included research articles was critically appraised using the Mixed Methods Appraisal Tool. A descriptive and thematic synthesis was used to synthesise the findings.

RESULTS

Of the 4701 records screened for titles and abstracts, 25 studies using qualitative, quantitative, and mixed-methods designs were included. Three key themes were identified. In theme 1, 'I am healing: how my wound shapes me and my journey,' physical symptoms, psychological factors and previous experiences significantly influenced patients' engagement in wound care. Theme 2, 'Taking charge of my healing: my active engagement in wound care' described how patient participation in surgical wound care goes beyond clinical procedures and can include the use of technology and holistic self-care. Finally, theme 3, 'Navigating the path to recovery: How others shape my experience' showed that effective communication is crucial for promoting participation, yet issues like inadequate information can leave patients unprepared for wound management.

CONCLUSIONS

This review highlights opportunities to personalise and prioritise a patient-oriented approach to surgical wound care. Clinicians and educators should adopt an individualised approach by tailoring patient participation based on patient factors (i.e. physical symptoms) and adopt patient-centred communication approaches. Researchers should focus on exploring approaches to self-care and technology, as these approaches may enhance patient participation in wound care.

Integrated image and location analysis for wound classification: a deep learning approach

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Establishing critical care nursing research priorities for three Australian regional public hospitals: A mixed method priority setting study

OBJECTIVE

To determine key priorities for critical care nursing research in three Australian regional public hospitals, representing the shared priorities of healthcare professionals and patient representatives.

METHODS

A three phase priority setting study, including consensus methods (nominal group), survey, qualitative interviews and focus groups were conducted between May 2021 and March 2022. Healthcare professionals and patient representatives from critical care units in regional public hospitals in Australia participated. A patient representative contributed to research design and co-authored this paper.

RESULTS

In phase one, 29 research topics were generated. In phase two, during a nominal group ranking process, the top 5 priority areas for each site were identified. In the final phase, three themes from focus groups and interviews included patient flow through intensive care, patient care through intensive care journey and intensive care patient recovery.

CONCLUSION

Identifying context specific research priorities through a priority setting exercise provides insight into the topics that are important to healthcare professionals and to patients in critical care. The top research priorities for nursing research in critical care in regional Australian hospitals include patient flow, patient recovery, and evidence based patient care through the intensive care journey, such as delirium management, pain and sedation, and mobilisation. These shared priorities will be used to guide future nursing research in critical care over the next 3-5 years.

IMPLICATIONS FOR CLINICAL PRACTICE

The method we used in identifying the research priorities can be used by other researchers and clinicians; close collaboration among researchers and clinicians will be beneficial for practice improvement; and how we can be reassured that our practice is evidence based is worthy of attention.

Surgical wound care preferences and priorities from the perspectives of patients: a qualitative analysis

OBJECTIVE

To explore patients' priorities and preferences for optimal care of their acute or hard-to-heal surgical wound(s).

METHOD

This qualitative study involved semi-structured individual interviews with patients receiving wound care in Queensland, Australia. Convenience and snowball sampling were used to recruit patients from inpatient and outpatient settings between November 2019 and January 2020. Interviews were audio recorded, transcribed verbatim and analysed using thematic analysis. Emergent themes were discussed by all investigators to ensure consensus.

RESULTS

A total of eight patients were interviewed, five of whom were male (average median age: 70.5 years; interquartile range (IQR): 45-80 years). Four interrelated themes emerged from the data that describe the patients' surgical wound journey: experiencing psychological and psychosocial challenges; taking back control by actively engaging in care; seeking out essential clinician attributes; and collaborating with clinicians to enable an individualised approach to their wound care.

CONCLUSION

Findings from this study indicate that patients want to actively collaborate with clinicians who have caring qualities, professional skills and knowledge, and be involved in decision-making to ensure care meets their individual needs.

Multi-modal wound classification using wound image and location by deep neural network

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.