Search the digital library catalog

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

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: 8
Full text (1,19 MB)

Abstract: The review examines the long-term impact of community psychiatric care on improving the quality of life of people with schizophrenia. It addresses the global burden of this disorder and the need for effective community-based care strategies. A systematic literature search was conducted in databases such as CINAHL, Medline, Web of Science, Sage and ScienceDirect, with the search lasting until March 2024 and following the PRISMA guidelines. The inclusion criteria targeted studies that addressed the long-term effects of community mental health care for people aged 18 years and older with schizophrenia and included both quantitative and qualitative research designs. Studies unrelated to the research question or with significant methodological flaws were excluded. The risk of bias was assessed using GRADE and GRADECERqual, in addition to critical appraisal using the Joanna Briggs Institute (JBI) checklists. Independent screening and data extraction was performed, with results summarised by thematic analysis. In total, 11 studies met the inclusion criteria and included cross-sectional, cohort, qualitative and randomised controlled trial designs. The results showed that community psychiatric care significantly improves the quality of life, well-being and social integration of people with schizophrenia. Effective interventions identified include psychoeducation, cognitive behavioural therapy, social skills training and individualised care plans. However, challenges such as limited resources, labour shortages and social stigma, particularly in low-income neighbourhoods, were also identified. This study highlights the importance of continuous, personalised, multidisciplinary community-based care for sustainable mental health outcomes. Further research is recommended to investigate the long-term impact and strategies to overcome implementation challenges.
Keywords: schizophrenia, quality of life, community, psychiatric care, patient, systematic review
Published in DKUM: 22.11.2024; Views: 0; Downloads: 11
Link to file
This document has many files! More...

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: 15
Full text (5,29 MB)

Abstract: The day-to-day work of primary care (PC) was substantially changed by the COVID-19 pandemic. Teaching practices needed to adapt both clinical work and teaching in a way that enabled the teaching process to continue, while maintaining safe and high-quality care. Our study aims to investigate the effect of being a training practice on a number of different outcomes related to the safety culture of PC practices. PRICOV-19 is a multi-country cross-sectional study that researches how PC practices were organized in 38 countries during the pandemic. Data was collected from November 2020 to December 2021. We categorized practices into training and non-training and selected outcomes relating to safety culture: safe practice management, community outreach, professional well-being and adherence to protocols. Mixed-effects regression models were built to analyze the effect of being a training practice for each of the outcomes, while controlling for relevant confounders. Of the participating practices, 2886 (56%) were non-training practices and 2272 (44%) were training practices. Being a training practice was significantly associated with a lower risk for adverse mental health events (OR: 0.83; CI: 0.70-0.99), a higher number of safety measures related to patient flow (Beta: 0.17; CI: 0.07-0.28), a higher number of safety incidents reported (RR: 1.12; CI: 1.06-1.19) and more protected time for meetings (Beta: 0.08; CI: 0.01-0.15). No significant associations were found for outreach initiatives, availability of triage information, use of a phone protocol or infection prevention measures and equipment availability. Training practices were found to have a stronger safety culture than non-training practices. These results have important policy implications, since involving more PC practices in education may be an effective way to improve quality and safety in general practice.
Keywords: safety culture, patient safety, quality of care, primary health care, COVID-19, medical education, vocational training, PRICOV-19, infectious disease, multi-country, general practice
Published in DKUM: 04.07.2024; Views: 128; Downloads: 17
Full text (376,37 KB)
This document has many files! More...