1. Feasibility of a computerized clinical decision support system delivered via a socially assistive robot during grand rounds : a pilot studyValentino Šafran, Urška Smrke, Bojan Ilijevec, Samo Horvat, Vojko Flis, Nejc Plohl, Izidor Mlakar, 2025, izvirni znanstveni članek Opis: Aims and Objective: The aim of this study was to explore the feasibility, usability and acceptance of integrating Clinical Decision Support Systems with Socially Assistive Robots into hospital grand rounds. Background: Adopting Clinical Decision Support Systems in healthcare faces challenges such as complexity, poor integration with workflows, and concerns about data privacy and quality. Issues such as too many alerts, confusing errors, and difficulty using the technology in front of patients make adoption challenging and prevent it from fitting into daily workflows. Making Clinical Decision Support System simple, intuitive and user-friendly is essential to enable its use in daily practice to improve patient care and decision-making. Methods: This six-month pilot study had two participant groups, with total of 40 participants: a longitudinal intervention group (n =8) and a single-session evaluation group (n=32). Participants were medical doctors at the University Clinical Center Maribor. The intervention involved implementing a Clinical Decision Support System delivered via a Socially Assistive Robot during hospital grand rounds. We developed a system that employed the HL7 FHIR standard for integrating data from hospital monitors, electronic health records, and patient-reported outcomes into a single dashboard. A Pepper-based SAR provided patient specific recommendations through a voice and SAR tablet enabled interface. Key evaluation metrics were assessed using the System Usability Scale (SUS) and the Unified Theory of Acceptance, Use of Technology (UTAUT2) questionnaire, including Effort Expectancy, Performance Expectancy and open ended questions. The longitudinal group used the system for 6 months and completed the assessments twice, after one week and at the end of the study. The single-session group completed the assessment once, immediately after the experiment. Qualitative data were gathered through open-ended questions. Data analysis included descriptive statistics, paired t-tests, and thematic analysis. Results: System usability was rated highly across both groups, with the longitudinal group reporting consistently excellent scores (M =82.08 at final evaluation) compared to the acceptable scores of the single-session group (M =68.96). Extended exposure improved user engagement, reflected in significant increases in Effort Expectancy and Habit over time. Participants found the system enjoyable to use, and while no significant changes were seen in Performance Expectancy, feedback emphasized its efficiency in saving time and improving access to clinical data, supporting its feasibility and acceptability. Conclusions: This research supports the potential of robotic technologies to transform CDSS into more interactive, efficient, and user-friendly tools for healthcare professionals. The paper also suggests further research directions and technical improvements to maximize the impact of innovative technologies in healthcare. Ključne besede: clinical decision support systems, clinical decision-making, hospital grand rounds, patient data integration, perceived quality of care, socially assistive robots, usability and familiarity, user experience questionnaire, workload reduction Objavljeno v DKUM: 30.05.2025; Ogledov: 0; Prenosov: 3
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2. Facilitating acceptance, trust, and ethical integration of socially assistive robots among nurses : a quasi-experimental studyIzidor Mlakar, Igor Robert Roj, Vojko Flis, Valentino Šafran, Urška Smrke, 2025, izvirni znanstveni članek Opis: Objectives: To evaluate the impact of different types of demonstrations (no demonstration, video demonstration, and face-to-face demonstration) on nurses’ acceptance, trust, and ethical considerations regarding socially assistive robots. Methods: The study employed a quasi-experimental design involving 312 nurses: 201 with no exposure to socially assistive robots, 97 exposed via video demonstrations, and 14 exposed through live face-to-face demonstrations in a hospital room. Participants completed self-report measures assessing their perceptions of ethical acceptability, trust, and acceptance of socially assistive robots. Results: Participants exposed to any kind of demonstration reported significantly higher perceptions of ethical acceptability compared to those with no exposure. Among demonstration types, live face-to-face demonstrations resulted in higher overall ethical acceptability, satisfaction, and acceptance compared to video demonstrations. Conclusions: Demonstrations, particularly face-to-face interactions, play a crucial role in fostering ethical acceptability and overall acceptance of socially assistive robots. These findings highlight the importance of incorporating live demonstrations in strategies to improve healthcare professionals’ trust and acceptance of robotic technology. Ključne besede: ethical acceptability, acceptance, socially assistive robots, nurses, quasi experimental study Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 2
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3. Evaluating the benefits and implementation challenges of digital health interventions for improving self-efficacy and patient activation in cancer survivors : single-case experimental prospective studyUmut Arioz, Urška Smrke, Valentino Šafran, Maja Ravnik, Matej Horvat, Vojko Flis, Izidor Mlakar, 2025, izvirni znanstveni članek Opis: Cancer survivors face numerous challenges, and digital health interventions can empower them by enhancing self-efficacy and patient activation. This prospective study aimed to assess the impact of a mHealth app on self-efficacy and patient activation in 166 breast and colorectal cancer survivors. Participants received a smart bracelet and used the app to access personalized care plans. Data were collected at baseline and follow-ups, including patient-reported outcomes and clinician feedback. The study demonstrated positive impacts on self-efficacy and patient activation. The overall trial retention rate was 75.3%. Participants reported high levels of activation (PAM levels 1–3: P = 1.0; level 4: P = 0.65) and expressed a willingness to stay informed about their disease (CASE-Cancer factor 1: P = 0.98; factor 2: P = 0.66; factor 3: P = 0.25). Usability of the app improved, with an increase in participants rating the system as having excellent usability (from 14.82% to 22.22%). Additional qualitative analysis revealed positive experiences from both patients and clinicians. This paper contributes significantly to cancer survivorship care by providing personalized care plans tailored to individual needs. The PERSIST platform shows promise in improving patient outcomes and enhancing self-management abilities in cancer survivors. Further research with larger and more diverse populations is needed to establish its effectiveness. Ključne besede: cancer survivorship, self-efficacy, satisfaction, patient activation, digital health interventions Objavljeno v DKUM: 25.04.2025; Ogledov: 0; Prenosov: 0 |
4. Exploring the feasibility of generative AI in persona research : a omparative analysis of large language model-generated and human-crafted personas in obesity researchUrška Smrke, Ana Rehberger, Nejc Plohl, Izidor Mlakar, 2025, izvirni znanstveni članek Opis: This study investigates the perceptions of Persona descriptions generated using three different large language models (LLMs) and qualitatively developed Personas by an expert panel involved in obesity research. Six different Personas were defined, three from the clinical domain and three from the educational domain. The descriptions of Personas were generated using qualitative methods and the LLMs (i.e., Bard, Llama, and ChatGPT). The perception of the developed Personas was evaluated by experts in the respective fields. The results show that, in general, the perception of Personas did not significantly differ between those generated using LLMs and those qualitatively developed by human experts. This indicates that LLMs have the potential to generate a consistent and valid representation of human stakeholders. The LLM-generated Personas were perceived as believable, relatable, and informative. However, post-hoc comparisons revealed some differences, with descriptions generated using the Bard model being in several Persona descriptions that were evaluated most favorably in terms of empathy, likability, and clarity. This study contributes to the understanding of the potential and challenges of LLM-generated Personas. Although the study focuses on obesity research, it highlights the importance of considering the specific context and the potential issues that researchers should be aware of when using generative AI for generating Personas. Ključne besede: user personas, obesity, large language models, value sensitive design, digital health interventions Objavljeno v DKUM: 14.02.2025; Ogledov: 0; Prenosov: 12
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5. Decoding anxiety : a scoping review of observable cuesUrška Smrke, Izidor Mlakar, Ana Rehberger, Leon Žužek, Nejc Plohl, 2024, pregledni znanstveni članek Opis: Background: While anxiety disorders are one of the most prevalent mental diseases, they are often overlooked due to shortcomings of the existing diagnostic procedures, which predominantly rely on self-reporting. Due to recent technological advances, this source of information could be complemented by the so-called observable cues – indicators that are displayed spontaneously through individuals’ physiological responses or behaviour and can be detected by modern devices. However, while there are several individual studies on such cues, this research area lacks a synthesis. In line with this, our scoping review aimed to identify observable cues that offer meaningful insight into individuals’ anxiety and to determine how these cues can be measured. Methods: We followed the PRISMA guidelines for scoping reviews. The search string containing terms related to anxiety and observable cues was entered into four databases (Web of Science, MEDLINE, ERIC, IEEE). While the search – limited to English peer-reviewed records published from 2012 onwards – initially yielded 2311 records, only 33 articles fit our selection criteria and were included in the final synthesis. Results: The scoping review unravelled various categories of observable cues of anxiety, specifically those related to facial expressions, speech and language, breathing, skin, heart, cognitive control, sleep, activity and motion, location data and smartphone use. Moreover, we identified various approaches for measuring these cues, including wearable devices, and analysing smartphone usage and social media activity. Conclusions: Our scoping review points to several physiological and behavioural cues associated with anxiety and highlights how these can be measured. These novel insights may be helpful for healthcare practitioners and fuel future research and technology development. However, as many cues were investigated only in a single study, more evidence is needed to generalise these findings and implement them into practice with greater confidence. Ključne besede: anxiety, observable cues, digital biomarkers, scoping review, physiological cues, behavioural cues Objavljeno v DKUM: 07.02.2025; Ogledov: 0; Prenosov: 7
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6. An end-to-end framework for extracting observable cues of depression from diary recordingsIzidor Mlakar, Umut Arioz, Urška Smrke, Nejc Plohl, Valentino Šafran, Matej Rojc, 2024, izvirni znanstveni članek Opis: Because of the prevalence of depression, its often-chronic course, relapse and associated disability, early detection and non-intrusive monitoring is a crucial tool for timely diagnosis and treatment, remission of depression and prevention of relapse. In this way, its impact on quality of life and well-being can be limited. Current attempts to use artificial intelligence for the early classification of depression are mostly data-driven and thus non-transparent and lack effective means to deal with uncertainties. Therefore, in this paper, we propose an end-to-end framework for extracting observable depression cues from diary recordings. Furthermore, we also explore its feasibility for automatic detection of depression symptoms using observable behavioural cues. The proposed end-to-end framework for extracting depression was used to evaluate 28 video recordings from the Symptom Media dataset and 27 recordings from the DAIC-WOZ dataset. We compared the presence of the extracted features between recordings of individuals with and without a depressive disorder. We identified several cues consistent with previous studies in terms of their differentiation between individuals with and without depressive disorder across both datasets among language (i.e., use of negatively valanced words, use of first-person singular pronouns, some features of language complexity, explicit mentions of treatment for depression), speech (i.e., monotonous speech, voiced speech and pauses, speaking rate, low articulation rate), and facial cues (i.e., rotational energy of head movements). The nature/context of the discourse, the impact of other disorders and physical/psychological stress, and the quality and resolution of the recordings all play an important role in matching the digital features to the relevant background. In this way, the work presented in this paper provides a novel approach to extracting a wide range of cues relevant to the classification of depression and opens up new opportunities for further research. Ključne besede: digital biomarkers of depression, facial cues, speech cues, language cues, deep learning, end-to-end pipeline, artificial intelligence Objavljeno v DKUM: 17.01.2025; Ogledov: 0; Prenosov: 13
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7. Quality of life of colorectal cancer survivors : mapping the key indicators by expert consensus and measures for their assessmentUrška Smrke, Sara Abalde-Cela, Catherine Loly, Jean-Paul Calbimonte, Liliana Pires, Simon Lin, Alberto Sánchez, Sara Tement, Izidor Mlakar, 2024, izvirni znanstveni članek Ključne besede: quality of life, surveys and questionnaires, adult oncology, colorectal cancer survivors, Delphi study, scoping review, expert consensus Objavljeno v DKUM: 15.01.2025; Ogledov: 0; Prenosov: 9
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8. Development and validation of the perceived deepfake trustworthiness questionnaire (PDTQ) in three languagesNejc Plohl, Izidor Mlakar, Letizia Aquilino, Piercosma Bisconti, Urška Smrke, 2025, izvirni znanstveni članek Opis: Exposure to false information is becoming a common occurrence in our daily lives. New developments in artificial intelligence are now used to produce increasingly sophisticated multimedia false content, such as deepfakes, making false information even more challenging to detect and combat. This creates expansive opportunities to mislead individuals into believing fabricated claims and negatively influence their attitudes and behavior. Therefore, a better understanding of how individuals perceive such content and the variables related to the perceived trustworthiness of deepfakes is needed. In the present study, we developed and validated the Perceived Deepfake Trustworthiness Questionnaire (PDTQ) in English, Italian, and Slovene. This was done in three phases. First, we developed the initial pool of items by reviewing previous studies, generating items via interviews and surveys, and employing artificial intelligence. Second, we shortened and adapted the questionnaire according to experts’ evaluation of content validity and translated the questionnaire into Italian and Slovene. Lastly, we evaluated the psychometric characteristics via a cross-sectional study in three languages (N ¼ 733). The exploratory factor analyses suggested a two-factor solution, with the first factor measuring the perceived trustworthiness of the content and the second measuring the perceived trustworthiness of the presentation. This factorial structure was replicated in confirmatory factor analyses. Moreover, our analyses provided support for PDTQ’s reliability, measurement invariance across all three languages, and its construct and incremental validity. As such, the PDTQ is a reliable, measurement invariant, and valid tool for comprehensive exploration of individuals’ perception of deepfake videos. Ključne besede: deepfakes, misinformation, perception, questionnaire validation, trust Objavljeno v DKUM: 03.09.2024; Ogledov: 30; Prenosov: 42
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9. Multilingual framework for risk assessment and symptom tracking (MRAST)Valentino Šafran, Simon Lin, Jama Nateqi, Alistair G. Martin, Urška Smrke, Umut Arioz, Nejc Plohl, Matej Rojc, Dina Běma, Marcela Chavez, Matej Horvat, Izidor Mlakar, 2024, izvirni znanstveni članek Opis: The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app’s usability as above satisfactory (i.e., 7.9 on 1–10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1–10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care. Ključne besede: multilingual framework, risk assessment, symptom tracking, chronic diseases, patient-centered care, real-world data Objavljeno v DKUM: 12.08.2024; Ogledov: 74; Prenosov: 34
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10. Using structural equation modeling to explore patients’ and healthcare professionals’ expectations and attitudes towards socially assistive humanoid robots in nursing and care routineIzidor Mlakar, Urška Smrke, Vojko Flis, Nina Kobilica, Samo Horvat, Bojan Ilijevec, Bojan Musil, Nejc Plohl, 2023, izvirni znanstveni članek Opis: Healthcare systems around the world are currently witnessing various challenges, including population aging and workforce shortages. As a result, the existing, overworked staff are struggling to meet the ever-increasing demands and provide the desired quality of care. One of the promising technological solutions that could complement the human workforce and alleviate some of their workload, are socially assistive humanoid robots. However, despite their potential, the implementation of socially assistive humanoid robots is often challenging due to low acceptance among key stakeholders, namely, patients and healthcare professionals. Hence, the present study first investigated the extent to which these stakeholders accept the use of socially assistive humanoid robots in nursing and care routine, and second, explored the characteristics that contribute to higher/lower acceptance within these groups, with a particular emphasis on demographic variables, technology expectations, ethical acceptability, and negative attitudes. In study 1, conducted on a sample of 490 healthcare professionals, the results of structural equation modeling showed that acceptance is driven primarily by aspects of ethical acceptability, although education and technology expectations also exert an indirect effect. In study 2, conducted on a sample of 371 patients, expectations regarding capabilities and attitudes towards the social influence of robots emerged as important predictors of acceptance. Moreover, although acceptance rates differed between tasks, both studies show a relatively high acceptance of socially assistive humanoid robots. Despite certain limitations, the study findings provide essential knowledge that enhances our understanding of stakeholders’ perceptions and acceptance of socially assistive humanoid robots in hospital environments, and may guide their deployment. Ključne besede: socially assistive humanoid robots, attitudes, expectations, patients, healthcare professionals Objavljeno v DKUM: 12.06.2024; Ogledov: 166; Prenosov: 15
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