A Toolkit for Delirium Identification and Promoting Partnerships Between Carers and Nurses: A Pilot Pre-Post Feasibility Study.

BACKGROUND: Delirium is frightening for people experiencing it and their carers, and it is the most common hospital-acquired complication worldwide. Delirium is associated with higher rates of morbidity, mortality, residential care home admission, dementia, and carer stress and burden, yet strategies to embed the prevention and management of delirium as part of standard hospital care remain challenging. Carers are well placed to recognize subtle changes indicative of delirium, and partner with nurses in the prevention and management of delirium. OBJECTIVE: To evaluate a Prevention & Early Delirium Identification Carer Toolkit (PREDICT), to support partnerships between carers and nurses to prevent and manage delirium. DESIGN: A pre-post-test intervention and observation study. MAIN MEASURES: Changes in carer knowledge of delirium; beliefs about their role in partnering with nurses and intended and actual use of PREDICT; carer burden and psychological distress. Secondary measures were rates of delirium. PARTICIPANTS: Participants were carers of Indigenous patients aged 45 years and older and non-Indigenous patients aged 65 years and older. INTERVENTION: Nurses implemented PREDICT, with a view to provide carers with information about delirium and strategies to address caregiving stress and burden. KEY RESULTS: Participants included 25 carers (43% response rate) (n = 17, 68% female) aged 29-88 (M = 65, SD = 17.7 years). Carer delirium knowledge increased significantly from pre-to-post intervention (p = < .001; CI 2.07-4.73). Carers' intent and actual use of PREDICT was (n = 18, 72%; and n = 17, 68%). Carer burden and psychological distress did not significantly change. The incidence of delirium in the intervention ward although not significant, decreased, indicating opportunity for scaling up. CONCLUSION: The prevention and management of delirium are imperative for safe and quality care for patients, carers, and staff. Further comprehensive and in-depth research is required to better understand underlying mechanisms of change and explore facets of nursing practice influenced by this innovative approach.

Healthcare professionals' experiences and perceptions of artificial intelligence in regional and rural health districts in Australia.

INTRODUCTION: A greater understanding of Australian healthcare professionals' perceptions of artificial intelligence (AI) is needed to identify the challenges ahead as this new technology finds its way into healthcare delivery. OBJECTIVE: The aim of this study was to identify healthcare professionals' perceptions of AI, their understanding of this technology, their education needs and barriers they perceived to its implementation. DESIGN: Healthcare professionals in eight local health districts in New South Wales Australia were surveyed using the Shinners Artificial Intelligence Perception (SHAIP) tool. FINDINGS: The study surveyed 176 participants from regional (59.5%), rural (36.4%) and metropolitan (4.0%) healthcare districts in Australia. Only 27% of all participants stated they are currently using AI in the delivery of care. The study found that Age, Discipline, Use of AI and Desire for Education had a significant effect on perceptions of AI, and that overall healthcare professionals believe AI will impact their role and they do not feel prepared for its use. The study showed that understanding of AI is varied and workforce knowledge is seen as the greatest barrier to implementation. More than 75% of healthcare professionals desire education about AI, its application and ethical implications to the delivery of care. CONCLUSION: The development of education is needed urgently to prepare healthcare professionals for the implementation of AI.

Partnering with carers in the management of delirium in general acute care settings: An integrative review.

OBJECTIVES: Delirium is a common, preventable condition. However, delirium is poorly recognised and often missed because symptoms are misinterpreted, and risk factors overlooked by health-care professionals. Carers usually have intimate knowledge about the person they care for. Therefore, they are well placed within care teams to implement delirium prevention strategies, identify symptoms and support the early diagnosis of delirium. The aim of this integrative review was to synthesise findings from the published research reporting on partnering with carers in the management of delirium in general acute care settings. METHODS: Five databases (Medline-EBSCO, PubMed, PsycINFO, ProQuest, CINAHL and SCOPUS) were searched to identify primary research regarding partnering with carers in the management of delirium in acute care settings, and results were synthesised. PRISMA guidelines were adhered to, and quality appraisal was conducted using the Mixed Methods Appraisal Tool. RESULTS: All seven studies reported that partnering with carers was a viable strategy in the management of delirium to maximise outcomes for people at risk of or experiencing delirium and that increasing carers' knowledge of delirium was key. The synthesis of findings also identified two themes: Increasing knowledge and Effective partnerships. CONCLUSIONS: A collaborative approach to increasing carers' and nurses' knowledge about the management of delirium, coupled with education on how to develop therapeutic nurse-carer relationships, is important for ongoing effective partnerships in the management of delirium. Good communication supported effective partnerships, which enabled both nurses and carers the opportunity to express their needs and concerns and negotiate collaborative involvement in the management of delirium.

xPM: Enhancing exogenous data visibility.

Process mining is a well-established discipline with applications in many industry sectors, including healthcare. To date, few publications have considered the context in which processes execute. Little consideration has been given as to how contextual data (exogenous data) can be practically included for process mining analysis, beyond including case or event attributes in a typical event log. We show that the combination of process data (endogenous) and exogenous data can generate insights not possible with standard process mining techniques. Our contributions are a framework for process mining with exogenous data and new analyses, where exogenous data and process behaviour are linked to process outcomes. Our new analyses visualise exogenous data, highlighting the trends and variations, to show where overlaps or distinctions exist between outcomes. We applied our analyses in a healthcare setting and show that clinicians could extract insights about differences in patients' vital signs (exogenous data) relevant to clinical outcomes. We present two evaluations, using a publicly available data set, MIMIC-III, to demonstrate the applicability of our analysis. These evaluations show that process mining can integrate large amounts of physiologic data and interventions, with resulting discrimination and conversion to clinically interpretable information.

Exploring healthcare professionals' perceptions of artificial intelligence: Piloting the Shinners Artificial Intelligence Perception tool.

OBJECTIVE: There is an urgent need to prepare the healthcare workforce for the implementation of artificial intelligence (AI) into the healthcare setting. Insights into workforce perception of AI could identify potential challenges that an organisation may face when implementing this new technology. The aim of this study was to psychometrically evaluate and pilot the Shinners Artificial Intelligence Perception (SHAIP) questionnaire that is designed to explore healthcare professionals' perceptions of AI. Instrument validation was achieved through a cross-sectional study of healthcare professionals (n = 252) from a regional health district in Australia. METHODS AND RESULTS: Exploratory factor analysis was conducted and analysis yielded a two-factor solution consisting of 10 items and explained 51.7% of the total variance. Factor one represented perceptions of 'Professional impact of AI' (α = .832) and Factor two represented 'Preparedness for AI' (α = .632). An analysis of variance indicated that 'use of AI' had a significant effect on healthcare professionals' perceptions of both factors. 'Discipline' had a significant effect on Allied Health professionals' perception of Factor one and low mean scale score across all disciplines suggests that all disciplines perceive that they are not prepared for AI. CONCLUSIONS: The results of this study provide preliminary support for the SHAIP tool and a two-factor solution that measures healthcare professionals' perceptions of AI. Further testing is needed to establish the reliability or re-modelling of Factor 2 and the overall performance of the SHAIP tool as a global instrument.

Evaluation of a digital application to support internationally qualified nurses' communication and leadership skills.

BACKGROUND: Internationally qualified nurses enrolled in Australian bridging programs to support professional registration lack confidence, and require support and time to develop communication and leadership skills in the clinical setting. Strategies that strengthen professional self-concept have been demonstrated to improve the nursing performance of internationally qualified nurses. OBJECTIVE: To evaluate an interactive mobile application called mPreceptor, specifically designed to support internationally qualified nurses' communication and leadership skills during a 6 week clinical placement. The application facilitated weekly learning content and activities in the areas of clinical communication and leadership, including self-assessment, goal-setting, case studies, and weekly detailed reflections while on placement. DESIGN: A quasi-experimental pre and post-test design with a non-equivalent comparison group was used to explore the effectiveness of mPreceptor on internationally qualified nurses' self-appraisal of professional self-concept, including leadership and communication skills, compared with standard clinical placement. The psychometrically tested Nurse Self-Concept Questionnaire, measured changes to perceived professional self-concept. RESULTS: Overall, there was a significant increase in Nurse Self-Concept following the clinical placement, confirming that the bridging program for internationally qualified nurses in Australia improves leadership and communication skills. Leadership skills were significantly greater for those internationally qualified nurses who engaged with mPreceptor. CONCLUSION: Further research is required to investigate the application of interactive mobile applications, as effective education resources to facilitate internationally qualified nurses' transition of skills and knowledge to the Australian healthcare context.

Exploring healthcare professionals' perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method.

OBJECTIVE: The aim of this study was to draw upon the collective knowledge of experts in the fields of health and technology to develop a questionnaire that measured healthcare professionals' perceptions of Artificial Intelligence (AI). METHODS: The panel for this study were carefully selected participants who demonstrated an interest and/or involvement in AI from the fields of health or information technology. Recruitment was accomplished via email which invited the panel member to participate and included study and consent information. Data were collected from three rounds in the form of an online survey, an online group meeting and email communication. A 75% median threshold was used to define consensus. RESULTS: Between January and March 2019, five healthcare professionals and three IT experts participated in three rounds of study to reach consensus on the structure and content of the questionnaire. In Round 1 panel members identified issues about general understanding of AI and achieved consensus on nine draft questionnaire items. In Round 2 the panel achieved consensus on demographic questions and comprehensive group discussion resulted in the development of two further questionnaire items for inclusion. In a final e-Delphi round, a draft of the final questionnaire was distributed via email to the panel members for comment. No further amendments were put forward and 100% consensus was achieved. CONCLUSION: A modified e-Delphi method was used to validate and develop a questionnaire to explore healthcare professionals' perceptions of AI. The e-Delphi method was successful in achieving consensus from an interdisciplinary panel of experts from health and IT. Further research is recommended to test the reliability of this questionnaire.

Exploring healthcare professionals' understanding and experiences of artificial intelligence technology use in the delivery of healthcare: An integrative review.

BACKGROUND: The integration of artificial intelligence (AI) into our digital healthcare system is seen as a significant strategy to contain Australia's rising healthcare costs, support clinical decision making, manage chronic disease burden and support our ageing population. With the increasing roll-out of 'digital hospitals', electronic medical records, new data capture and analysis technologies, as well as a digitally enabled health consumer, the Australian healthcare workforce is required to become digitally literate to manage the significant changes in the healthcare landscape. To ensure that new innovations such as AI are inclusive of clinicians, an understanding of how the technology will impact the healthcare professions is imperative. METHOD: In order to explore the complex phenomenon of healthcare professionals' understanding and experiences of AI use in the delivery of healthcare, an integrative review inclusive of quantitative and qualitative studies was undertaken in June 2018. RESULTS: One study met all inclusion criteria. This study was an observational study which used a questionnaire to measure healthcare professional's intrinsic motivation in adoption behaviour when using an artificially intelligent medical diagnosis support system (AIMDSS). DISCUSSION: The study found that healthcare professionals were less likely to use AI in the delivery of healthcare if they did not trust the technology or understand how it was used to improve patient outcomes or the delivery of care which is specific to the healthcare setting. The perception that AI would replace them in the healthcare setting was not evident. This may be due to the fact that AI is not yet at the forefront of technology use in healthcare setting. More research is needed to examine the experiences and perceptions of healthcare professionals using AI in the delivery of healthcare.