Zustandsüberwachung mit Regelfahrzeugen

Die Künstliche Intelligenz (KI) bzw. das maschinelle Lernen ermöglicht neue Ansätze für die Zustandsüberwachung der Bahninfrastruktur im laufenden Betrieb. Dieser Beitrag gibt einen Überblick über Forschungsarbeiten des Austrian Institute of Technology (AIT) und des Deutschen Zentrums für Luft- und Raumfahrt DLR e. V. zur Nutzung der Fahrzeug-Fahrweg-Interaktion zur Erkennung von Fehlzuständen an Schienen

Tools That Can Be Used Now! Person-Centered Care Within Cancer Rehabilitation. Symposium at the American Congress of Rehabilitation Medicine

The purpose of this symposium is to provide a) the science behind the benefits of delivering person-centered care, and b) practical tips, training, and resources to successfully Integrate person-centered approach into your practice [7]. This will be accomplished by illustrating what current person-centered models are being used within cancer rehabilitation setting through the use of case examples and evidence in the literature. Within this illustration, we will demonstrate how common barriers to implementing person-centered care can be reduced within your practice. Attendees will be shown a) commonly used person-centered assessments that meet accreditation standards and reimbursement requirements, and b) where to locate community resources for your clients that can be effectively and efficiently given to them. Learning Objectives: Describe current person-centered care models within the cancer rehabilitation setting Identify and describe the most common barriers to implementation of person-centered care. Locate common person-centered assessments used among providers that also meet accreditation standards and reimbursement requirements. Locate community resources for your clients and describe how to provide these to your clients effectively and efficiently. Understand how person-centered care is currently used in practice

The use of the patient experience feedback for the codesign or improvement of rehabilitation services: scoping review protocol

Background: The collection and use of the patient feedback data for the codesign and improvement of rehabilitation services is paramount for a person-centered and better Patient Experience (Px) with rehabilitation care. Aim: To map and characterize the literature on patient engagement and on the use of Px feedback for codesigning or improving rehabilitation services. Eligibility: We include English-language, peer-reviewed (all time) and grey literature (last 5 years) on initiatives that engage patients, family members, patient representatives, or overall Px feedback (quantitative or qualitative) for informing or evaluating service development, codesign, or quality improvement (QI) activities in physical rehabilitation services. The Appendix 1, supplementary file, details key working definitions. Reports that address only collecting, assessing, or monitoring, Px data, i.e., without a reported use of these data for service improvement purposes, are excluded. We include every study type, including quantitative, qualitative or mixed-methods research, case studies, and data-based QI reports, but we exclude study protocols, perspectives, narrative reviews, letters, and broadly papers without designated methods or results being reported. Two independent reviewers (TJ, BS) will conduct the title-and-abstracts screenings and the full-text reviews after an initial review for calibration, the latter with up to two rounds afterwards toward consensus with a third reviewer (AD or AH) resolving any remaining disagreements. Sources: The search includes keyword searches in five databases of the peer-reviewed literature (PubMed/Medline, CINAHL, Rehabdata, Scopus, and Web-of-Science – Core Collection). The Appendix 2, supplementary file, provides the detailed search strategy for each scientific database. Specifically for the grey literature, we will also conduct keyword searches in Greylit and ProQuest database (dissertations & theses), in a generic search engine (i.e., Google), and in key websites (e.g., Institute for Healthcare Improvement, Beryl Institute – including its Patient Experience Journal). The search strategy also includes snowballing from the initially included papers (e.g., citation- and author-tracking) and from the references lists of included articles. Finally, a minimum of two key informants (e.g., academic and field experts) will be supplied with a preliminary list of inclusions and asked about other potential inclusions. Data Extraction: We will extract the study aim, biographic details (e.g., publication year, journal) and methodological features, such as study design, rehabilitation setting, country, participants (type and numbers), study outcomes measures, and analytical approaches. We will also extract key data for the type of Px feedback used, patient engagement methods, interventions, outcomes of interest, primary results, stated implications, recommendations, and any reported limitations. The extractions will be performed by one experienced reviewer (TJ), fully confirmed by another (BS or JS). Analysis: Descriptive statistics will be used to quantify and map the distribution of the literature per characteristic type and methodological features and a conventional content analysis will be used to synthesize the study aims, engagement procedures, results reported, stated implications, recommendations for further studies, and finally the reported direction for future research and the study limitations