Ian Cleland’s research while affiliated with University of Wollongong and other places

Objective To develop and optimise an app (iMPAKT) for improving implementation and measurement of person-centred practice in healthcare settings. Methods Two iterative rounds of testing were carried out based on cognitive task analysis and qualitative interview methods. The System Usability Scale (SUS) was also used to evaluate the app. Quantitative data on task completion and SUS scores were evaluated descriptively, with thematic analysis performed on qualitative data. The MoSCoW prioritisation system was used to identify key modifications to improve the app. Results Twelve participants took part (eight health professionals and four patient and public involvement representatives). Views on design and structure of the app were positive. The majority of the 16 tasks undertaken during the cognitive task analysis were easy to complete. Mean SUS scores were 73.5/100 (SD: 7.9; range = 60–92.5), suggesting good overall usability. For one section of the app that transcribes patients speaking about their experience of care, a non-intuitive user interface and lack of transcription accuracy were identified as key issues influencing usability and acceptability. Conclusions Findings from the evaluation were used to inform iterative modifications to further develop and optimise the iMPAKT App. These included improved navigational flow, and implementation of an updated artificial intelligence (AI) based Speech-To-Text software; allowing for more accurate, real-time transcription. Use of such AI-based software represents an interesting area that requires further evaluation. This is particularly apparent in relation to potential for large-scale collection of data on person-centred measures using the iMPAKT App, and for assessing initiatives designed to improve patient experience.

Background Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user’s likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. Objective This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. Methods First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts’ knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. Results We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree ( M 3 =2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). Conclusions The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.

The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development.

This study tested the acceptability of the iMPAKT App with end users. Cognitive task analysis and semi-structured interviews were used. Twelve participants took part. The majority of tasks were found to be easy to complete but issues were identified with a section of the app that provides Speech-To-Text transcription of patients speaking about their experience of care. Artificial Intelligence (AI) based systems may be needed to address these limitations. Overall views on acceptability of the app were positive and participants valued how it could be used to support practice improvement initiatives and large scale collection of person-centred measures.

Objective To develop and optimise an app (iMPAKT) for improving implementation and measurement of person-centred practice in healthcare settings. Methods Two iterative rounds of testing were carried out based on cognitive task analysis and qualitative interview methods. The System Usability Scale (SUS) was also used to evaluate the app. Quantitative data on task completion and SUS scores were evaluated descriptively, with thematic analysis performed on qualitative data. The MoSCoW prioritisation system was used to identify key modifications to improve the app. Results Twelve participants took part (eight health professionals and four patient and public involvement representatives). Views on design and structure of the app were positive. The majority of the 16 tasks undertaken during the cognitive task analysis were easy to complete. Mean SUS scores were 73.5/100 (SD:7.9; range=60-92.5), suggesting good overall usability. For one section of the app that transcribes patients speaking about their experience of care, a non-intuitive user interface and lack of transcription accuracy were identified as key issues influencing usability and acceptability. Conclusions Findings from the evaluation were used to inform iterative modifications to further develop and optimise the iMPAKT App. These included improved navigational flow, and implementation of an updated artificial intelligence (AI) based Speech-To-Text software; allowing for more accurate, real-time transcription. Use of such AI-based software represents an interesting area that requires further evaluation. This is particularly apparent in relation to potential for large-scale collection of data on person-centred measures using the iMPAKT App, and for assessing initiatives designed to improve patient experience.

The iMPAKT App has been developed as a digital tool for implementing and measuring person- centredness in nursing and midwifery practice. Despite its potential usefulness for the collection of person-centred measures, appropriate strategies are required to enhance the implementation of the app. To better understand the factors affecting adoption and maintenance, this protocol describes a multi-methods study to examine the experience of using the iMPAKT App in different contexts and settings. A convergent, multiple-methods approach will be used. Nurses and midwifes working in teams at different study sites in the UK and Australia will use the app during two, six-week cycles of data collection. Qualitative interviews and focus groups, guided by the Consolidated Framework for Implementation Research (CFIR) will be used to explore individual responses, views and experiences around acceptability and engagement with the app, and to examine variations in contexts. Quantitative data will be gathered on the number of person-centred measures recorded during the data collection cycles and using the System Usability Scale. Results will help to develop an understanding of the determinants and processes underpinning successful implementation, and inform further research to develop tailored implementation strategies, aimed at facilitating large scale collection of data on person- centred measures using the iMPAKT App.