Publikationen

Wenn unsere Gesundheitssysteme sowohl zur menschlichen als auch zur planetaren Gesundheit beitragen sollen, stehen wir vor enormen Herausforderungen. Die Komplexität und das Ausmaß der notwendigen Veränderungen werden deutlich, wenn wir uns allein die Dimension der Reduktion von klima- schädlichen Emissionen im Gesundheitswesen anschauen.

Verfahren der künstlichen Intelligenz (KI) haben in den letzten Jahren stark an Bedeutung gewonnen. Die Umsetzung der KI in die Versorgungspraxis birgt viele Herausforderungen: Wie kann Vertrauen in die KI aufgebaut und so die Anwendung beschleunigt werden?

The aim of this study was to predict future high-cost patients with machine learning using healthcare claims data. The authors applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. They were able to develop machine learning models that predict high-cost patients with ‘good’ performance facilitating routinely collected sickness fund claims and cost data. Tree-based models performed best and outperformed the ANN and LR.

The authors conducted an evidence map on self-management interventions and patient-relevant outcomes for adults living with overweight/obesity. They conclude that there is a need of widening the scope of research by focusing on outcomes important to patients, assessing emotional/social/share-decision support, exploring remote techniques and including vulnerable populations.

To identify and describe the most relevant contextual factors (CFs) from the literature that influence the successful implementation of self-management interventions (SMIs) for patients living with type 2 diabetes mellitus, obesity, COPD and/or heart failure, the authors conducted a qualitative review of reviews. Four databases were searched, 929 reviews were identified, 460 screened and 61 reviews met the inclusion criteria. A great variety of CFs was identified on several levels, across all four chronic diseases. Most CFs were on the level of the patient, the professional and the interaction level, while less CFs were obtained on the level of the intervention, organization, setting and national level. No differences in main themes of CFs across all four diseases were found.

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. The protocol of the ADLIFE study is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. 

Cooperation is a core feature of integrated healthcare systems and an important link in their value-creating mechanism. The premise is that providers who cooperate can promote more efficient use of health services while improving health outcomes. The authors studied the performance of an integrated healthcare system in improving regional cooperation.

The objective of this study was to reach consensus amongst stakeholders about the most important contextual factors (CFs) that may influence the successful implementation of (components of) self-management interventions (SMIs) for type 2 diabetes, obesity, COPD and heart failure.

Self-management interventions (SMIs) are core components of high-quality care in type 2 diabetes mellitus (T2DM). The authors aimed to identify and summarise the scientific evidence exploring the perspectives of patients with T2DM and their informal caregivers on outcomes of SMIs, and the key themes to enhance T2DM patient-centred care.

This study, as part of the COMPAR-EU project, utilized a mixed-methods approach involving 37 individual, semi-structured interviews and one focus group with 7 participants to investigate the factors influencing the implementation and use of self-management interventions (SMIs) decision tools in clinical practice. The study identified five main dimensions and facilitators for implementation: decision tool factors, individual health professional factors, interaction factors, organizational factors, and social, political, and legal factors. The findings highlight the importance of structured implementation through SMI decision support tools, emphasizing the need to understand their benefits, secure organizational resources, and gain political support for sustainable implementation.

Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices’ connectedness to peers and their prescribing performance in two German regions.