Background: Depressive disorders are associated with a high burden of disease. However, due to the burden posed by the disease on not only the sufferers, but also on their relatives, there is an ongoing debate about which costs to include and, hence, which perspective should be applied. Therefore, the aim of this paper was to examine whether the change between healthcare payer and societal perspective leads to different conclusions of cost-utility analyses in the case of depression. Methods: A systematic literature search was conducted to identify economic evaluations of interventions in depression, launched on Medline and the Cost-Effectiveness Registry of the Tufts University using a ten-year time horizon (2008–2018). In a two-stepped screening process, cost-utility studies were selected by means of specified inclusion and exclusion criteria. Subsequently, relevant findings was extracted and, if not fully stated, calculated by the authors of this work. Results: Overall, 53 articles with 92 complete economic evaluations, reporting costs from healthcare payer/provider and societal perspective, were identified. More precisely, 22 estimations (24%) changed their results regarding the cost-effectiveness quadrant when the societal perspective was included. Furthermore, 5% of the ICURs resulted in cost-effectiveness regarding the chosen threshold (2% of them became dominant) when societal costs were included. However, another four estimations (4%) showed the opposite result: these interventions were no longer cost-effective after the inclusion of societal costs. Conclusions: Summarising the disparities in results and applied methods, the results show that societal costs might alter the conclusions in cost-utility analyses. Hence, the relevance of the perspectives chosen should be taken into account when carrying out an economic evaluation. This systematic review demonstrates that the results of economic evaluations can be affected by different methods available for estimating non-healthcare costs.

Background: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration of artificial intelligence (AI) into these systems has the potential to support physicians in selecting the most effective drug therapy for a given patient. Objective: This study aimed to analyze the feasibility of an AI-based CDSS pilot version for antibiotic therapy in sepsis patients and identify facilitating and inhibiting conditions for its implementation in intensive care medicine. Methods: The evaluation was conducted in 2 steps, using a qualitative methodology. Initially, expert interviews were conducted, in which intensive care physicians were asked to assess the AI-based recommendations for antibiotic therapy in terms of plausibility, layout, and design. Subsequently, focus group interviews were conducted to examine the technology acceptance of the AI-based CDSS. The interviews were anonymized and evaluated using content analysis. Results: In terms of the feasibility, barriers included variability in previous antibiotic administration practices, which affected the predictive ability of AI recommendations, and the increased effort required to justify deviations from these recommendations. Physicians’ confidence in accepting or rejecting recommendations depended on their level of professional experience. The ability to re-evaluate CDSS recommendations and an intuitive, user-friendly system design were identified as factors that enhanced acceptance and usability. Overall, barriers included low levels of digitization in clinical practice, limited availability of cross-sectoral data, and negative previous experiences with CDSSs. Conversely, facilitators to CDSS implementation were potential time savings, physicians’ openness to adopting new technologies, and positive previous experiences. Conclusions: Early integration of users is beneficial for both the identification of relevant context factors and the further development of an effective CDSS. Overall, the potential of AI-based CDSSs is offset by inhibiting contextual conditions that impede its acceptance and implementation. The advancement of AI-based CDSSs and the mitigation of these inhibiting conditions are crucial for the realization of its full potential.

Patients with initial stroke or transient ischemic attack (TIA) are at high risk for further strokes, death or cardiovascular events. Even the first-ever stroke is associated with a high chance of disability and need for assistance. The risk of long-term health care demands increases with each subsequent event. Although the inpatient sector already provides a high standard of care in Germany, it can be difficult to obtain cross-sectoral aftercare. Thus, the study investigated whether a structured case management program can avoid stroke recurrences. The study was conducted with a quasi-experimental study design in three regions in North Rhine-Westphalia. Patients with first-ever stroke or TIA were eligible to participate. The intervention group was prospectively recruited and supported by a case manager during a one-year follow-up. Optimal Full Matching was used to generate a control group based on statutory claims data. The primary outcome was the stroke recurrence. Recurrence and mortality were analysed by using Cox regression; other secondary outcomes were examined with test-based procedures and with logistic regressions. Additionally, subgroup analyses were performed. From June 2018 to March 2020, 1,512 patients were enrolled in the intervention group. Claims data from 19,104 patients have been transmitted for establishing the control group. After the matching process, 1,167 patients of each group were included in the analysis. 70 recurrences (6.0%) occurred in the intervention group and 67 recurrences (5.7%) in the control group. With a hazard ratio of 1.06 (95% CI: [1.42-0.69]; p=0.69), no significant effect was found for the primary outcome. With regard to the secondary outcome mortality, 36 patients in the intervention group and 46 in the control group died (3.1% vs. 3.9%). Again, there was no significant effect (HR: 0.86; 95% CI: [0.58-1.28], p=0.46). Based on the present findings, the case management approach for stroke patients evaluated here was unable to demonstrate an improvement in health care. Potential effects of case management might not be adequately depicted in short observation periods. Thus, future studies should consider longer observation periods. A panel of experts should discuss whether the core approach of case management to support cost-intensive individual cases is contrary to a broad implementation with a one-size-fits-all intervention for stroke patients. In this case, further research should focus on more specific study populations.

Background: Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy. Objective: This scoping review addresses the following research questions: (1) How do qualitative studies conceptualize and assess physician autonomy? (2) Which aspects of physician autonomy are addressed by these studies? (3) What are the potential benefits and harms of AI for physician autonomy identified by these studies? Methods: We performed a scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023, by searching MEDLINE and Web of Science. To answer research question 1, we determined whether the included studies explicitly include physician autonomy as a research focus and whether their interview, survey, and focus group questions explicitly name or implicitly include aspects of physician autonomy. To answer research question 2, we extracted the qualitative results of the studies, categorizing them into the 7 components of physician autonomy introduced by Schulz and Harrison. We then inductively formed subcomponents based on the results of the included studies in each component. To answer research question 3, we summarized the potentially harmful and beneficial effects of AI on physician autonomy in each of the inductively formed subcomponents. Results: The search yielded 369 studies after duplicates were removed. Of these, 27 studies remained after titles and abstracts were screened. After full texts were screened, we included a total of 7 qualitative studies. Most studies did not explicitly name physician autonomy as a research focus or explicitly address physician autonomy in their interview, survey, and focus group questions. No studies addressed a complete set of components of physician autonomy; while 3 components were addressed by all included studies, 2 components were addressed by none. We identified a total of 11 subcomponents for the 5 components of physician autonomy that were addressed by at least 1 study. For most of these subcomponents, studies reported both potential harms and potential benefits of AI for physician autonomy. Conclusions: Little research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed. Further qualitative and quantitative research is needed that focuses explicitly on physician autonomy and addresses all relevant components of physician autonomy.

Zielsetzung Ziel dieser Studie war es, eine umfassende Übersicht über die Literatur zu Kosten des Schlaganfalls oder der transitorischen ischämischen Attacke (TIA) in Deutschland zu geben. Methodik Es wurde eine systematische Literaturrecherche in PubMed und Embase durchgeführt, um kostenberichtende Studien zu Schlaganfall und TIA, die vor Juni 2019 veröffentlicht wurden, zu identifizieren. Studien wurden eingeschlossen, sofern die Gesamtkosten, eine spezifische Kostenkategorie oder mindestens eine Behandlungsphase nach einem Schlaganfall oder einer TIA dargestellt wurde. Dagegen wurden Studien ausgeschlossen, die den Schlaganfall lediglich als Folge einer anderen Erkrankung oder eines Risikofaktors berücksichtigt haben, soweit dort keine separate Beschreibung der schlaganfallspezifischen Kosten vorgenommen wurde. Um die Vergleichbarkeit der Studienergebnisse zu gewährleisten, wurden alle Kostenschätzungen mithilfe des Verbraucherpreisindex des Statistischen Bundesamtes auf das einheitliche Preisjahr 2018 angepasst. Ergebnisse Die Suchen in den Datenbanken ergaben 482 Treffer zuzüglich 3 Handrechercheartikeln, von denen 66 Studien für die Volltextüberprüfung ausgewählt wurden. Davon wurden schlussendlich 16 Studien in die Ergebnissynthese eingeschlossen. Die durchschnittlichen direkten Kosten lagen zwischen 14 240 € und 44 060 € pro Patient. Wenn indirekte Kosten erhoben wurden, lagen diese zwischen 2470 € und 17 860 €. Im ersten Jahr nach einem Schlaganfall stellten die stationären Kosten den größten Kostenblock dar, während die ambulanten Behandlungs- und Pflegekosten insbesondere in den Folgejahren dominierten. Schlussfolgerung Die Ergebnisse der Studien betonen die monetäre Relevanz des Schlaganfalls für das deutsche Gesundheitssystem. Daneben verdeutlicht die Übersichtsarbeit die Notwendigkeit einheitlicher Ansätze zur Erhebung und Analyse von Krankheitskosten, um eine Vergleichbarkeit zu gewährleisten und Handlungsbedarf anhand kostentreibender Aspekte identifizieren zu können. Aim The aim of this study was to provide a comprehensive overview of previously published cost-of-illness studies in stroke or transient ischemic attack (TIA) in Germany. Method A literature search via PubMed and Embase was conducted to identify studies providing estimates of the economic burden of stroke published before June 2019. A study was included if it reported total costs of stroke or costs of a specific stroke treatment phase in Germany. Studies that considered stroke only as a consequence of other diseases or risk factors and therefore did not focus on the detailed description of stroke-related costs were excluded. To ensure comparability between the included studies, all cost estimates were adjusted to 2018 values using consumer price indices (CPI) obtained from the German Federal Statistical Office. Results The search yielded 482 hits. Another three publications were detected by manual search. Sixty-six of them were identified for full-text review. Sixteen out of these studies met the predefined criteria and were therefore included for data extraction. The average direct medical costs ranged from €14,240 to €44,060. If productivity losses were estimated, indirect costs after stroke varied between €2,470 and €17,860. Inpatient costs were accountable for the largest cost factor in the first year after stroke. In the following years, outpatient treatment and nursing costs dominated. Conclusion The results of the included studies reveal a high economic burden of stroke. In addition, the systematic review demonstrates the need for consistent approaches to measure costs to ensure comparability and identify cost drivers for future demand on care. Publication Date: 05 October 2020 (online) © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Background: Antibiotic therapies are the main treatment for bacterial infections, but growing antibiotic resistance is a major global health threat, severely impacting patients with sepsis. Rapid selection of the most effective antibiotic therapy is critical for survival and for preventing further resistance. Physicians must consider numerous factors for proper empiric treatment selection. A clinical decision support system (CDSS) aims to support physicians in this process, facilitating rapid and targeted therapy. Objective: The purpose of this work is to explore the extent to which the realization of a CDSS is possible based on the data available to us and to document insights gained during the development of a foundational model designed to assist physicians in determining empiric treatment options for patients with sepsis. In this regard, rather than aiming to develop a CDSS for clinical application, we highlight the importance of close interprofessional collaboration between scientists from various disciplines and analyze the effects of data quality and quantity on the performance of our statistical models. Methods: Empirical scientists conducted interviews with medical practitioners to acquire the medical knowledge required to develop sound statistical models. We developed and applied 2-step cross-sectional, as well as time-series classification models, to carefully preprocessed data of patients with sepsis admitted to the intensive care unit of a German hospital. Results: We identified several factors as crucial information for valid decisions on empiric therapy for treating patients with sepsis. These include the patients’ core data, especially the infection focus. To prevent further resistance, individual risk factors such as travel history and professional background should be considered. The evaluation of a therapy’s effectiveness is mainly based on the patient’s general condition and blood values such as procalcitonin and interleukin 6. One key factor in the acceptance of a CDSS is the explainability of the results produced by the applied methods. Our models demonstrated mainly weak predictive ability for all considered empiric antibiotic therapies. However, they are not yet suitable for use in clinical practice, especially as they are based on prescribing habits rather than on optimal treatment decisions. Conclusions: This work highlights the importance of interprofessional collaboration between medical experts and model developers, ensuring that data quality and clinical relevance are central to the process. It emphasizes the urgent need for high-quality, comprehensive data to overcome challenges such as data discontinuity and improve model performance, particularly through enhanced digitization in health care. This feasibility study will facilitate future efforts to develop a CDSS for treating patients with sepsis and to translate it into clinical use.