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    Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery
    (2024-03-26)
    Glauer, Martin
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    Fabian Neuhaus
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    Simon Flügel
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    Mossakowski, Till
    Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data. Ontologies are semantic annotation resources that provide definitions and a classification hierarchy for a domain. They are widely used throughout the life sciences. ChEBI is a large-scale ontology for the domain of biologically interesting chemistry that connects representations of chemical structures with meaningful chemical and biological categories. Classifying novel molecular structures into ontologies such as ChEBI has been a longstanding objective for data scientific methods, but the approaches that have been developed to date are limited in several ways: they are not able to expand as the ontology expands without manual intervention, and they are not able to learn from continuously expanding data. We have developed an approach for automated classification of chemicals in the ChEBI ontology based on a neuro-symbolic AI technique that harnesses the ontology itself to create the learning system. We provide this system as a publicly available tool, Chebifier, and as an API, ChEB-AI. We here evaluate our approach and show how it constitutes an advance towards a continuously learning semantic system for chemical knowledge discovery.
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    Scopus© Citations 10
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    Factors Guiding Clinical Decision‐Making in Genitourinary Oncology
    (2024-10) ;
    Aeppli, Stefanie
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    Fischer, Stefanie
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    Peres, Tobias
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    Rothermundt, Christian
    The authors would like to thank all clinicians who graciously permitted us to observe various consultation and tumor board meetings, thereby offering invaluable insights into the intricate clinical decision-making process in urologic oncology. We acknowledge and appreciate their openness and willingness to share their experiences have made a significant contribution to this study.
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    Scopus© Citations 2
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    The Paradoxes of Digital Tools in Hospitals: Qualitative Interview Study
    Background: Digital tools are progressively reshaping the daily work of health care professionals (HCPs) in hospitals. While this transformation holds substantial promise, it leads to frustrating experiences, raising concerns about negative impacts on clinicians' well-being. Objective: The goal of this study was to comprehensively explore the lived experiences of HCPs navigating digital tools throughout their daily routines. Methods: Qualitative in-depth interviews with 52 HCPs representing 24 medical specialties across 14 hospitals in Switzerland were performed. Results: Inductive thematic analysis revealed 4 main themes: digital tool use, workflow and processes, HCPs' experience of care delivery, and digital transformation and management of change. Within these themes, 6 intriguing paradoxes emerged, and we hypothesized that these paradoxes might partly explain the persistence of the challenges facing hospital digitalization: the promise of efficiency and the reality of inefficiency, the shift from face to face to interface, juggling frustration and dedication, the illusion of information access and trust, the complexity and intersection of workflows and care paths, and the opportunities and challenges of shadow IT. Conclusions: Our study highlights the central importance of acknowledging and considering the experiences of HCPs to support the transformation of health care technology and to avoid or mitigate any potential negative experiences that might arise from digitalization. The viewpoints of HCPs add relevant insights into long-standing informatics problems in health care and may suggest new strategies to follow when tackling future challenges.
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    Scopus© Citations 19
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    Practical Recommendations for Navigating Digital Tools in Hospitals: Qualitative Interview Study
    (2024-11-27) ; ;
    Kraehenmann, Simone
    ;
    Background: The digitalization of health care organizations is an integral part of a clinician's daily life, making it vital for health care professionals (HCPs) to understand and effectively use digital tools in hospital settings. However, clinicians often express a lack of preparedness for their digital work environments. Particularly, new clinical end users, encompassing medical and nursing students, seasoned professionals transitioning to new health care environments, and experienced practitioners encountering new health care technologies, face critically intense learning periods, often with a lack of adequate time for learning digital tools, resulting in difficulties in integrating and adopting these digital tools into clinical practice. Objective: This study aims to comprehensively collect advice from experienced HCPs in Switzerland to guide new clinical end users on how to initiate their engagement with health ITs within hospital settings. Methods: We conducted qualitative interviews with 52 HCPs across Switzerland, representing 24 medical specialties from 14 hospitals. The interviews were transcribed verbatim and analyzed through inductive thematic analysis. Codes were developed iteratively, and themes and aggregated dimensions were refined through collaborative discussions. Results: Ten themes emerged from the interview data, namely (1) digital tool understanding, (2) peer-based learning strategies, (3) experimental learning approaches, (4) knowledge exchange and support, (5) training approaches, (6) proactive innovation, (7) an adaptive technology mindset, (8) critical thinking approaches, (9) dealing with emotions, and (10) empathy and human factors. Consequently, we devised 10 recommendations with specific advice to new clinical end users on how to approach new health care technologies, encompassing the following: take time to get to know and understand the tools you are working with; proactively ask experienced colleagues; simply try it out and practice; know where to get help and information; take sufficient training; embrace curiosity and pursue innovation; maintain an open and adaptable mindset; keep thinking critically and use your knowledge base; overcome your fears, and never lose the human and patient focus. Conclusions: Our study emphasized the importance of comprehensive training and learning approaches for health care technologies based on the advice and recommendations of experienced HCPs based in Swiss hospitals. Moreover, these recommendations have implications for medical educators and clinical instructors, providing advice on effective methods to instruct and support new end users, enabling them to use novel technologies proficiently. Therefore, we advocate for new clinical end users, health care institutions and clinical instructors, academic institutions and medical educators, and regulatory bodies to prioritize effective training and cultivating technological readiness to optimize IT use in health care.
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    Scopus© Citations 3
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    Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review
    Background: The digitalization of health care has many potential benefits, but it may also negatively impact health care professionals' well-being. Burnout can, in part, result from inefficient work processes related to the suboptimal implementation and use of health information technologies. Although strategies to reduce stress and mitigate clinician burnout typically involve individual-based interventions, emerging evidence suggests that improving the experience of using health information technologies can have a notable impact. Objective: The aim of this systematic review was to collect evidence of the benefits and challenges associated with the use of digital tools in hospital settings with a particular focus on the experiences of health care professionals using these tools. Methods: We conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to explore the experience of health care professionals with digital tools in hospital settings. Using a rigorous selection process to ensure the methodological quality and validity of the study results, we included qualitative studies with distinct data that described the experiences of physicians and nurses. A panel of 3 independent researchers performed iterative data analysis and identified thematic constructs. Results: Of the 1175 unique primary studies, we identified 17 (1.45%) publications that focused on health care professionals' experiences with various digital tools in their day-today practice. Of the 17 studies, 10 (59%) focused on clinical decision support tools, followed by 6 (35%) studies focusing on electronic health records and 1 (6%) on a remote patient-monitoring tool. We propose a theoretical framework for understanding the complex interplay between the use of digital tools, experience, and outcomes. We identified 6 constructs that encompass the positive and negative experiences of health care professionals when using digital tools, along with moderators and outcomes. Positive experiences included feeling confident, responsible, and satisfied, whereas negative experiences included frustration, feeling overwhelmed, and feeling frightened. Positive moderators that may reinforce the use of digital tools included sufficient training and adequate workflow integration, whereas negative moderators comprised unfavorable social structures and the lack of training. Positive outcomes included improved patient care and increased workflow efficiency, whereas negative outcomes included increased workload, increased safety risks, and issues with information quality. Conclusions: Although positive and negative outcomes and moderators that may affect the use of digital tools were commonly reported, the experiences of health care professionals, such as their thoughts and emotions, were less frequently discussed. On the basis of this finding, this study highlights the need for further research specifically targeting experiences as an important mediator of clinician well-being. It also emphasizes the importance of considering differences in the nature of specific tools as well as the profession and role of individual users.
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    Scopus© Citations 84
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    Understanding the Root Causes of Healthcare Professionals’ Frustration with Digital Tools
    (2025-04-08)
    The increasing integration of digital tools in healthcare has the potential to improve patient outcomes, yet often leads to frustration among users. This study explores the root causes of healthcare professionals' frustrations with technologies in hospitals. Secondary analysis of qualitative interviews with 52 clinicians in Switzerland revealed recurring challenges with digital tools, particularly clinical information systems, that are related to performance, usability, and data accessibility. These issues disrupt clinical workflows, compromise patient care, and can affect the well-being of clinicians. Addressing these issues through collaborative efforts could improve system design, reduce frustration, and enhance healthcare delivery.
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    Scopus© Citations 2
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    Applying Large Language Models to Interpret Qualitative Interviews in Healthcare
    To address the persistent challenges in healthcare, it is crucial to incorporate firsthand experiences and perspectives from stakeholders such as patients and healthcare professionals. However, the current process of collecting, analyzing and interpreting qualitative data, such as interviews, is slow and laborintensive. To expedite this process and enhance efficiency, automated approaches aim to extract meaningful themes and accelerate interpretation, but current approaches such as topic modeling reduce the richness of the raw data. Here, we evaluate whether Large Language Models can be used to support the semiautomated interpretation of qualitative interview data. We compare a novel approach based on LLMs to topic modeling approaches and to manually identified themes across two different qualitative interview datasets. This exploratory study finds that LLMs have the potential to support incorporating human perspectives more widely in the advancement of sustainable healthcare systems.
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    Scopus© Citations 12
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    Human Factors Influencing the Experience of Healthcare Professionals Using Digital Tools
    <jats:p>Digitalization in healthcare has the potential to offer numerous advantages to various stakeholders, however, healthcare professionals often encounter difficulties while using digital tools. We conducted a qualitative analysis of published studies to examine the experience of clinicians using digital tools. Our findings revealed that human factors influence clinicians’ experiences and that integration of human factors into the design and development of healthcare technologies is of high importance to improve user experience and overall success.</jats:p>
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    Application of a general LLM-based classification system to retrieve information about oncological trials
    (2024-12-05)
    Fabio Dennstädt
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    Paul Windisch
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    Irina Filchenko
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    Johannes Zink
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    Paul Martin Putora
    ABSTRACT Purpose The automated classification of clinical trials and medical literature is increasingly relevant, particularly in oncology, as the volume of publications and trial reports continues to expand. Large Language Models (LLMs) may provide new opportunities for automated diverse classification tasks. In this study, we developed a general-purpose text classification framework using LLMs and evaluated its performance on oncological trial classification tasks. Methods and Materials A general text classification framework with adaptable prompt, model and categories for the classification was developed. The framework was tested with four datasets comprising nine binary classification questions related to oncological trials. Evaluation was conducted using a locally hosted version of Mixtral-8x7B-Instruct v0.1 and three cloud-based LLMs: Mixtral-8x7B-Instruct v0.1, Llama3.1-70B-Instruct, and Qwen-2.5-72B. Results The system consistently produced valid responses with the local Mixtral-8x7B-Instruct model and the Llama3.1-70B-Instruct model. It achieved a response validity rate of 99.70% and 99.88% for the cloud-based Mixtral and Qwen models, respectively. Across all models, the framework achieved an overall accuracy of >94%, precision of >92%, recall of >90%, and an F1-score of >92%. Question-specific accuracy ranged from 86.33% to 99.83% for the local Mixtral model, 85.49% to 99.83% for the cloud-based Mixtral model, 90.50% to 99.83% for the Llama3.1 model, and 77.13% to 99.83% for the Qwen model. Conclusions The LLM-based classification framework exhibits robust accuracy and adaptability across various oncological trial classification tasks. The findings highlight the potential of automated, LLM- driven trial classification systems, which may become increasingly used in oncology.
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