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, original scientific article Abstract: 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.
Keywords: 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
Published in DKUM: 30.05.2025; Views: 0; Downloads: 2
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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, original scientific article Abstract: 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.
Keywords: ethical acceptability, acceptance, socially assistive robots, nurses, quasi experimental study
Published in DKUM: 29.05.2025; Views: 0; Downloads: 1
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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, original scientific article Abstract: 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.
Keywords: cancer survivorship, self-efficacy, satisfaction, patient activation, digital health interventions
Published in DKUM: 25.04.2025; Views: 0; Downloads: 0 |
4. Radiotherapy department supported by an optimization algorithm for scheduling patient appointmentsMarcela Chavez, Silvia Gonzalez, Ruiz Alvaro, Duflot Patrick, Nicolas Jansen, Izidor Mlakar, Umut Arioz, Valentino Šafran, Philippe Kolh, Van Gasteren Marteyn, 2025, original scientific article Abstract: Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Eachday, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, con-sidering the availability of human and material resources, such as healthcare professionals and linearaccelerators. With the increasing number of patients suffering from different types of cancers, manuallyestablishing schedules following each patient’s treatment protocols has become an extremely difficultand time-consuming task. We propose an optimization algorithm that automatically schedules andgenerates patient appointments. The model can rearrange fixed appointments to accommodate urgentcases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Eachday, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, con-sidering the availability of human and material resources, such as healthcare professionals and linearaccelerators. With the increasing number of patients suffering from different types of cancers, manuallyestablishing schedules following each patient’s treatment protocols has become an extremely difficultand time-consuming task. We propose an optimization algorithm that automatically schedules andgenerates patient appointments. The model can rearrange fixed appointments to accommodate urgentcases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment.
Keywords: appointments, hospital management, optimization algorithm, patient satisfaction, planning, radiotherapy
Published in DKUM: 25.02.2025; Views: 0; Downloads: 10
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5. Unlocking the power of socially assistive robotic nurses in hospitals through innovative living lab methodologyUmut Arioz, Božidar Bratina, Izidor Mlakar, Nejc Plohl, Suzana Uran, Igor Robert Roj, Riko Šafarič, Valentino Šafran, 2024, original scientific article Keywords: co-creation, stakeholder engagement, socially assistive humanoid robots, living lab, hospital care
Published in DKUM: 03.02.2025; Views: 0; Downloads: 8
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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, original scientific article Abstract: 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.
Keywords: digital biomarkers of depression, facial cues, speech cues, language cues, deep learning, end-to-end pipeline, artificial intelligence
Published in DKUM: 17.01.2025; Views: 0; Downloads: 9
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7. 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, original scientific article Abstract: 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.
Keywords: multilingual framework, risk assessment, symptom tracking, chronic diseases, patient-centered care, real-world data
Published in DKUM: 12.08.2024; Views: 74; Downloads: 18
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8. PALANTIR : An NFV-Based Security-as-a-Service Approach for Automating Threat MitigationMaxime Compastié, Antonio López Martínez, Carolina Fernandez, Manuel Gil Pérez, Stylianos Tsarsitalidis, George Xylouris, Izidor Mlakar, Michail Alexandros Kourtis, Valentino Šafran, 2023, original scientific article Abstract: Small and medium enterprises are significantly hampered by cyber-threats as they have inherently limited skills and financial capacities to anticipate, prevent, and handle security incidents. The EU-funded PALANTIR project aims at facilitating the outsourcing of the security supervision to external providers to relieve SMEs/MEs from this burden. However, good practices for the operation of SME/ME assets involve avoiding their exposure to external parties, which requires a tightly defined and timely enforced security policy when resources span across the cloud continuum and need interactions. This paper proposes an innovative architecture extending Network Function Virtualisation to externalise and automate threat mitigation and remediation in cloud, edge, and on-premises environments. Our contributions include an ontology for the decision-making process, a Fault-and-Breach-Management-based remediation policy model, a framework conducting remediation actions, and a set of deployment models adapted to the constraints of cloud, edge, and on-premises environment(s). Finally, we also detail an implementation prototype of the framework serving as evaluation material.
Keywords: Security-as-a-Service, security orchestration, policy-driven management, virtual network functions, finite state machines, constraints programming
Published in DKUM: 06.02.2024; Views: 334; Downloads: 18
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9. Razvoj večmodalnega zaznavnega omrežja za zajemanje občutkov in ocene kakovosti življenja : magistrsko deloValentino Šafran, 2021, master's thesis Abstract: V magistrskem delu predstavljamo razvoj večmodalnega zaznavnega omrežja za namene evropskega projekta H2020 PERSIST, ki zajema podatke oziroma občutke, z namenom ocenjevanja kakovosti življenja pacientov. Pred razvojem sistema, smo preučili in podali pregled procesa zajema podatkov že obstoječih sistemov, ter pregled izbranih gradnikov za naš sistem. Predstavljamo tudi uporabljene standarde in protokole, ki se uporabljajo znotraj predlaganega sistema. Za arhitekturo večmodalnega zaznavnega omrežja smo izbrali tri temeljne gradnike za katere smo ocenili, da lahko tvorijo zmogljivo omrežje za prenos in obdelavo podatkov, ter ponujajo možnost nadgrajevanja v primeru zvišanja zahtev projekta. Ti trije gradniki so Apache Camel, Apache ActiveMQ Artemis in Apache Kafka. Vse gradnike smo postavili na fizičnem strežniku PERSIST_CAMEL, in sicer vsakega na svojem virtualnem stroju. Mikroservisi strojnega učenja, razen vprašalnikov, ki jih izvaja Rasa Chatbot, ki so v procesu razvoja, pa predstavljajo odjemalce tega sistema. Večmodalno zaznavno omrežje je ustrezno zavarovano z varnimi protokoli in z uporabo drugih varnostnih elementov. Na koncu podamo tudi rezultate testiranja obremenjenosti in odzivnosti sistema pri odgovarjanju na vprašalnike. Iz rezultatov je razvidno, da zastavljen sistem uspešno izvaja pretakanje podatkov za podano število 200 pacientov v projektu PERSIST, in lahko podpre tudi večje število uporabnikov.
Keywords: večmodalno zaznavno omrežje, kakovost življenja, skrb za zdravje, zbiranje podatkov pacientov, Apache Camel, Apache ActiveMQ Artemis, Apache Kafka
Published in DKUM: 18.10.2021; Views: 972; Downloads: 110
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