https://www.um.edu.mt/library/oar/handle/123456789/5626 2026-10-23T16:16:02Z https://www.um.edu.mt/library/oar/handle/123456789/150027 Title: Determinants of acceptance into inpatient geriatric rehabilitation programmes in Malta : a regression analysis Authors: Muscat, Francesca; Ferry, Peter; Attard, Conrad; Lungaro-Mifsud, Stephen Abstract: With limited bed availability at Malta's state-run rehabilitation hospital, efficient and accurate assessment of frail older adults is essential for appropriate inpatient geriatric rehabilitation (GR) admissions. This study aimed to identify key predictors influencing admission decisions for inpatient GR programmes in Malta. Prospective data were collected from 250 patients aged 60 and over. In total, 83 predictors, including demographic details, clinical history, functional levels and social factors, were analysed against admission outcomes. Logistic regression analysis, mainly univariate analysis, followed by multivariate analysis with backward stepwise elimination, was used to determine significant predictors. Seven key predictors were identified, which shed light on the decision-making processes of the assessing specialists, highlighting the importance of embracing the ICF model rather than relying solely on medical criteria. These predictors reflect the WHO ICF and CGA frameworks, supporting a holistic, patient-centred approach. They informed the development of a digital tool, incorporating a future predictive model to guide GR admission decisions in Malta, aiming to facilitate the delivery of stratified and personalised care by enabling clinicians to select a care pathway based on the patient's potential health trajectory in response to rehabilitation interventions. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/150026 Title: A turnaround time dashboard for hospital laboratory blood tests Authors: Camilleri, Sephora; Attard, Conrad Abstract: Turnaround time (TAT) is a critical performance indicator in laboratory medicine, directly influencing clinical decision-making and patient outcomes. Delays in reporting can compromise timely treatment, particularly for urgent or life-threatening conditions where rapid laboratory results are essential. Despite this importance, most laboratories at the local main hospital monitor TAT retrospectively and primarily through median values, offering limited insight into workflow inefficiencies. This study developed and evaluated a business intelligence dashboard for retrospective TAT monitoring with a conceptual extension to near real-time analysis, aligning with digital health initiatives that leverage data-driven tools to enhance healthcare quality and patient safety. Data extracted from the Laboratory Information System (LIS), comprising approximately 6 million test order records over 4 months, was transformed into key performance indicators, including median TAT, compliance with thresholds, and phase-specific data. Near real-time monitoring was defined conceptually, based on proposed frequent scheduled data refreshes rather than continuous live streaming. Semi-structured interviews with laboratory managers informed indicator selection and guided user-centred dashboard design. Usability was evaluated through structured testing with laboratory managers using the System Usability Scale (SUS), alongside task-based assessment of clarity, relevance, and ease of use. By transforming raw LIS data into actionable insights, the dashboard demonstrated the capacity to identify delay patterns and workflow inefficiencies that are not apparent in traditional retrospective reports, thereby supporting proactive operational decision-making. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/150020 Title: Simulation-based modelling of human-robot collaboration in long-term care : healthcare professionals’ perspectives on semi-humanoid and humanoid robots Authors: Attard, Conrad; Sultana, Roberta Abstract: Global population ageing and healthcare workforce shortages have led to increasing interest in robotic technologies within long-term care (LTC) environments. Notwithstanding growing evidence on robotic acceptance, limited research has used simulation-based modelling of human-robot interaction (HRI) scenarios with healthcare professionals particularly those working with older persons, to prospectively appraise collaborative workflows, escalation protocols, and organisational readiness. This qualitative, scenario-based simulation-informed conceptual modelling study, conducted as part of the HEARTS (Human-Centred Elderly Care: Semi Humanoid Robots for Collaborative Daily Support) project, used scenario-based human-in-the-loop simulation with ten health care professionals (HCPs) experienced in older persons' care. Eight structured HRI simulation scenarios; four video-based and four discussion-based, were designed using a semi-humanoid robot (Pepper). These HRI simulation scenarios encompassed practical operational failure conditions to stress-test human-robot collaboration (HRC) dynamics and handover protocols. Participants expressed a preference for a complementary human-robot model, in which a human carer works along with the robot instead of leaving it to operate independently. Results support the feasibility and analytical usefulness of simulation-based modelling as a methodological tool for pre-implementation workflow mapping. The study presents a state-based Human-Robot Collaboration Simulation Framework (HRCSF) identifying collaboration states, transition triggers and performance metrics for sustainable robotic integration in LTC environments. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/149047 Title: Adult learners' perception on whether educational technology can augment, limit or replace the human element Authors: Camilleri, Christian; Camilleri, Rebecca Abstract: This research aims to study whether educational technology is enhancing or limiting the human element. A quantitative survey, in the form of a self-administered questionnaire, was conducted amongst 134 adult learners following Level 6 part-time courses offered at MCAST, aged ≥21 & ≤ 56 years. The first part of the questionnaire consisted of a descriptive analysis whilst in the second part, linear multiple regression analysis was performed to assess the students overall learning experience (dependent variable) vis-à-vis different teaching scenarios consisting of different levels of technological usage. Primarily, it resulted that all the participants (n=134) are connected to the world of digital information via their smartphones and other telecommunication devices. Moreover, the vast majority (40%) of the students spend more than four hours a day on the web. Nonetheless, the results indicate that the participants strongly rejected the idea of minimizing or replacing the role of the educator. In the second part of the questionnaire, blended learning (mixed mode) emerged as the strongest predictor. Lastly, none of the students (p >0.05) opted for the traditional face-to-face lecturing without use of technological tools thereby indicating that the mode of teaching has become obsolete. 2021-01-01T00:00:00Z