The healthcare sector has been confronted with rapidly rising healthcare costs and a shortage of medical staff. At the same time, the field of Artificial Intelligence (AI) has emerged as a promising area of research, offering potential benefits for healthcare. Despite the potential of AI to support healthcare, its widespread implementation, especially in healthcare, remains limited. One possible factor contributing to that is the lack of trust in AI algorithms among healthcare professionals. Previous studies have indicated that explainability plays a crucial role in establishing trust in AI systems. This study aims to explore trust in AI and its connection to explainability in a medical setting. A rapid review was conducted to provide an overview of the existing knowledge and research on trust and explainability. Building upon these insights, a dashboard interface was developed to present the output of an AI-based decision-support tool along with explanatory information, with the aim of enhancing explainability of the AI for healthcare professionals. To investigate the impact of the dashboard and its explanations on healthcare professionals, an exploratory case study was conducted. The study encompassed an assessment of participants’ trust in the AI system, their perception of its explainability, as well as their evaluations of perceived ease of use and perceived usefulness. The initial findings from the case study indicate a positive correlation between perceived explainability and trust in the AI system. Our preliminary findings suggest that enhancing the explainability of AI systems could increase trust among healthcare professionals. This may contribute to an increased acceptance and adoption of AI in healthcare. However, a more elaborate experiment with the dashboard is essential.
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The purpose of this study was to explore the experiences and impact of peer-to-peer shadowing as a technique to develop nurse middle managers’ clinical leadership practices. A qualitative descriptive study was conducted to gain insight into the experiences of nurse middle managers using semi-structured interviews. Data were analysed into codes using constant comparison and similar codes were grouped under sub-themes and then into four broader themes. Peer-to-peer shadowing facilitates collective reflection-in-action and enhances an “investigate stance” while acting. Nurse middle managers begin to curb the caring disposition that unreflectively urges them to act, to answer the call for help in the here and now, focus on ad hoc “doings”, and make quick judgements. Seeing a shadowee act produces, via a process of social comparison, a behavioural repertoire of postponing reactions and refraining from judging. Balancing the act of stepping in and doing something or just observing as well as giving or withholding feedback are important practices that are difficult to develop.
This article describes the relation between mental health and academic performance during the start of college and how AI-enhanced chatbot interventions could prevent both study problems and mental health problems.
Middels een RAAK-impuls aanvraag wordt beoogd de vertraging van het RAAK-mkb project Praktische Predictie t.g.v. corona in te halen. In het project Praktische Predictie wordt een prototype app ontwikkeld waarmee fysiotherapeuten in een vroeg stadium het chronisch worden van lage rugpijn kunnen voorspellen. Om chronische rugpijn te voorkomen is het belangrijk om in een vroeg stadium de kans hierop in te schatten door psychosociale en mogelijk andere risicofactoren op chronische pijnklachten te herkennen en hierop te interveniëren. Fysiotherapeuten zijn met deze vraag naar het lectoraat Werkzame factoren in Fysiotherapie en Paramedisch Handelen van de Hogeschool van Arnhem en Nijmegen gegaan en dit heeft aanleiding gegeven een onderzoek op te zetten waarin een dergelijke methodiek ontwikkeld wordt. De voorgestelde methodiek betreft een Clinical Decision Support Tool waarmee een geïndividualiseerde kans op chronische rugpijn kan worden bepaald gekoppeld aan een behandeladvies conform de lage rugpijn richtlijn. Hiervoor is eerst geïnventariseerd welke methoden fysiotherapeuten reeds gebruiken en welke in de literatuur worden genoemd. Op basis hiervan is een keuze gemaakt ten aanzien van data die digitaal verzameld worden in minimaal 16 fysiotherapiepraktijken waarbij patiënten gedurende 12 weken gevolgd worden. Met de verzamelde data worden met machine learning algoritmes ontwikkeld voor het berekenen van de kans op chroniciteit. De algoritmes worden ingebouwd in de Clinical Decision Support Tool: een gebruiksvriendelijke prototype app. Bij het ontwikkelen van de tool worden eindgebruikers (fysiotherapeuten en patiënten) intensief betrokken. Op deze manier wordt gegarandeerd dat de tool aansluit bij de wensen en behoeften van de doelgroep. De tool berekent de kans op chroniciteit en geeft een behandeladvies. Daarnaast kan de tool gebruikt worden om patiënten te informeren en te betrekken bij de besluitvorming. Vanwege de coronacrisis is er een aanzienlijke vertraging in de patiënten-instroom (doel n= 300) ontstaan die we met ondersteuning van een RAAK-impuls subsidie willen inlopen.
The main objective is to write a scientific paper in a peer-reviewed Open Access journal on the results of our feasibility study on increasing physical activity in home dwelling adults with chronic stroke. We feel this is important as this article aims to close a gap in the existing literature on behavioral interventions in physical therapy practice. Though our main target audience are other researchers, we feel clinical practice and current education on patients with stroke will benefit as well.
Alcohol use disorder (AUD) is a major problem. In the USA alone there are 15 million people with an AUD and more than 950,000 Dutch people drink excessively. Worldwide, 3-8% of all deaths and 5% of all illnesses and injuries are attributable to AUD. Care faces challenges. For example, more than half of AUD patients relapse within a year of treatment. A solution for this is the use of Cue-Exposure-Therapy (CET). Clients are exposed to triggers through objects, people and environments that arouse craving. Virtual Reality (VRET) is used to experience these triggers in a realistic, safe, and personalized way. In this way, coping skills are trained to counteract alcohol cravings. The effectiveness of VRET has been (clinically) proven. However, the advent of AR technologies raises the question of exploring possibilities of Augmented-Reality-Exposure-Therapy (ARET). ARET enjoys the same benefits as VRET (such as a realistic safe experience). But because AR integrates virtual components into the real environment, with the body visible, it presumably evokes a different type of experience. This may increase the ecological validity of CET in treatment. In addition, ARET is cheaper to develop (fewer virtual elements) and clients/clinics have easier access to AR (via smartphone/tablet). In addition, new AR glasses are being developed, which solve disadvantages such as a smartphone screen that is too small. Despite the demand from practitioners, ARET has never been developed and researched around addiction. In this project, the first ARET prototype is developed around AUD in the treatment of alcohol addiction. The prototype is being developed based on Volumetric-Captured-Digital-Humans and made accessible for AR glasses, tablets and smartphones. The prototype will be based on RECOVRY, a VRET around AUD developed by the consortium. A prototype test among (ex)AUD clients will provide insight into needs and points for improvement from patient and care provider and into the effect of ARET compared to VRET.
