Max Blumenstock

Healthy volunteers (HVs) are essential for phase I trials, yet their recruitment and screening remain resource‐intensive and ethically complex. High screening failure (SF) rates highlight the need to evaluate and understand all aspects of the enrolment processes. We analyzed SF patterns in a large German HV cohort and explored participants' motivations, risk perceptions, and expectations regarding trial participation and screening procedure. We retrospectively evaluated screening results from phase I trials (2012–2022) at Heidelberg University Hospital. Additionally, we performed a prospective mixed‐methods assessment with semi‐structured interviews and a questionnaire evaluating recruitment, informed consent (IC), motivation, and decision making, distributed to experienced volunteers of four trial sites (October 2024–August 2025). Across 30 phase I trials, 900 volunteers were screened with a SF rate of 31.9% ( n = 287), mainly due to laboratory abnormalities, medical history of concern, and organizational issues. Results from five semi‐structured interviews and the questionnaire survey ( n = 196) showed an overall acceptable satisfaction with the IC process, but identified areas of improvement in weighting of topics and risk perception. Although financial compensation was voted as the most important motivation, the decision process is multifactorial and an interplay of compensation, organizational aspects, and expected risks. We identified trial characteristics that are modifiable to facilitate interest in trial participation and provide data‐driven recommendations to improve the ethical, efficient, and operational conduct of phase I trials. Furthermore, the results underline the relevance of addressing public perceptions and misconceptions surrounding clinical trials to improve willingness to engage in early‐phase research.

Background: Efficiently finding and exploring relevant health studies is critical for informed, evidence-based health care. However, study information remains distributed across multiple resources, hindering interoperability, search, and reuse. Enhancing the findability of study data is a key challenge in promoting the findability, accessibility, interoperability, and reusability (FAIR) principles in health research. Objective: This study aimed to improve the findability and comparability of health studies by developing a semantically enriched graph-based framework that supports intuitive search and exploration for diverse stakeholders, including clinicians, researchers, and patients. Methods: We developed the BRAinS-Graph (“Biomedical Knowledge Graph for Recommending and Analysing Health Studies”), a semantically enriched knowledge base that integrates data from ClinicalTrials.gov, the Portal for Medical Data Models, the Unified Medical Language System, and MeSH into a single graph database. The framework applies an extract-transform-load process to integrate heterogeneous data structures and link related information across study resources and biomedical ontologies. Results: The BRAinS-Graph supports fine-grained, semantic searches across study metadata, eligibility criteria, and structural properties. Use cases illustrate its potential for clinicians, patients, and researchers, including analyses of study type distributions for meta-analyses and the identification of studies relevant to individual patients. Conclusions: By integrating heterogeneous study data into one interconnected knowledge base, the BRAinS-Graph improves the findability, accessibility, and reusability of study information. This work establishes a foundation for graph-based study recommendation systems and cross-institutional research infrastructures.

This study assessed the technical and clinical feasibility of capturing detailed, high-frequency Patient-Reported Outcome Measure (PROM) trajectories by integrating short instruments directly into the Electronic Health Record (EHR) system. Current practice uses infrequent PROMs, failing to capture the dynamic nature of a patient’s trajectory. We designed and implemented an electronic data capture system that securely sent single-item Quality of Life (QoL) and pain assessments to patients’ smartphones for direct EHR integration. The system was tested in cohorts from neurosurgery (182 patients, every 2-3 days for 30 days) and psychosomatics (71 patients, weekly for 60 days). Results showed high temporal variability in QoL (mean coefficient of variation 30.5% in neurosurgery), underscoring the need for frequent assessment. The single-item QoL demonstrated clinical validity, correlating highly significantly with pain (p<10E-10). We successfully maintained high response rates, finding that the combination of EHR integration (providing clinical relevance) and short instruments (minimizing burden) is crucial for success. These high-resolution patient trajectories are feasible in real-world settings, offering a powerful tool for both personalized clinical management (e.g., early complication detection) and serving as a comprehensive outcome for future clinical research which can be more informative than survival.

Highlights: • Experimental evaluation of in-house developed navigation on 3D-printed bone phantoms • Electromagnetic navigation for precise mandibular reconstruction • Color-coded visualization and feedback for simplified bone segment positioning • High accuracy (2.16 ± 1.1 mm) of mandibular reconstructions in model surgery

Background: Patient-reported outcome measures (PROMs) such as health-related quality of life (HRQoL) are usually assessed at greater time intervals such as diagnostic time points, after treatment, and during follow-up. Many PROMs require frequent data collection (weekly or daily). Electronic PROMs enable high-resolution tracking but face declining response rates. Integrating PROMs into electronic health records (EHRs) could improve response rates and personalize therapy. Objective: This study aimed to evaluate the technical and clinical feasibility of high-frequency HRQoL assessments for routine care in EHRs. Methods: Patients receive emails on their mobile devices with 1-time links to a web-based app called MyEDC. This app communicates with an electronic data capture proxy in the demilitarized zone of the hospital. With a polling mechanism, these patient data are transferred to the protected hospital network and uploaded to the EHR system. HRQoL on a visual analog scale is assessed over the course of treatment in 4 clinical use cases: psychosomatics, hematology, visceral surgery, and neurosurgery. Results: Quality of life (QoL) trajectories were collected for 110 patients with daily or weekly data collection between 2 weeks and 3 months. The HRQoL analyses revealed clinically relevant findings across the 4 different medical domains. In the use case psychosomatics, 36 patients showed a significant increase in HRQoL following 4 weeks of therapy, rising from a median of 42% (IQR 32%-52%) to 60% (IQR 41%-67%; P=.01). An analysis of 25 patients in hematology demonstrated a significant correlation between HRQoL and 30-item QoL Questionnaire (EORTC QLQ-C30) global health status score (P=.02). For 26 patients in visceral surgery, a significant association was observed between HRQoL and the reported pain level (P<.001). The clinical feasibility was further highlighted in the neurosurgery use case, where 23 patients showed a median response time to the electronic PROM questionnaires of 5.3 (IQR 0.6-17.7) hours. HRQoL values were associated with disease-specific symptoms and scores, indicating clinical validity of this readout. Considerable variability of HRQoL was observed over time, both intraindividually and interindividually. Median area under the curve of HRQoL ranged from 0.46 to 0.79. Median time to answer ranged from 0.9 to 7.1 hours. No significant association between number of responses and age was observed. Conclusions: High-resolution QoL trajectories with EHR integration are technically and clinically feasible. They offer a novel readout beyond survival analysis or PROM end point, enabling precise disease characterization and treatment comparison.

Background: Advancements in clinical medicine depend on state-of-the-art healthcare analytics, which require patient data from electronic health record (EHR) systems and electronic data capture (EDC). We developed and implemented an independent, yet integrated EDC system (designated IMI-EDC) in a tertiary hospital. This system ensures that clinical data are available for patient care and research while complying with regulatory standards. In this study, we compared the first cohort of IMI-EDC users against those using the predecessor paper-mediated system, measuring patient comfort and satisfaction, data quality, and facilitation of workflow facilitation. Methods: Until July 2025, more than 5000 patients were able to submit their medical history and health-related quality of life questionnaires via the IMI-EDC. All patients using tablets were surveyed regarding their user experience. Additionally, a subset of 100 EDC submissions was compared to 100 submissions via the paper-based system with respect to data completeness, correctness, clarity, and readability. Finally, the time to transfer data to the Heidelberg Pancreas Registry (HPR) research database was measured. Results: IMI-EDC was successfully implemented at our center. Approximately three-quarters (75%) of patients using tablets were comfortable with the system. Tablet users were significantly more likely to submit completed documents than those using paper (98/100 vs. 66/100), and among submitted forms, fully completed questionnaires were more frequent in the tablet group (70/98 vs. 24/66). Data transfers to the HPR were quicker for tablet submissions than for paper submissions in a small pragmatic subsample (4.2 minutes vs. 9.9 minutes; 7 tablet vs. 7 paper cases). Conclusions: IMI-EDC is satisfactory for patients and staff in terms of user experience, data completeness, and workload reduction in the setting of a high-volume surgical center.

BACKGROUND Efficiently finding and exploring relevant health studies is critical for informed evidence-based healthcare. However, study information remains distributed across multiple resources, hindering interoperability, search, and reuse. Enhancing the findability of study data is a key challenge in promoting the FAIR principles in health research. OBJECTIVE This work aimed to improve the findability and comparability of health studies by developing a semantically enriched graph-based framework that supports intuitive search and exploration for diverse stakeholders, including clinicians, researchers, and patients. METHODS We developed the BRAinS-Graph (“Biomedical Knowledge Graph for Recommending and Analysing Health Studies”), a semantically enriched knowledge base that integrates data from ClinicalTrials.gov, the Portal for Medical Data Models, the Unified Medical Language System (UMLS), and Medical Subject Headings (MeSH) into a single graph database. The framework applies an extract–transform–load (ETL) process to integrate heterogeneous data structures and link related information across study resources and biomedical ontologies. RESULTS The BRAinS-Graph supports fine-grained, semantic searches across study metadata, eligibility criteria, and structural properties. Use cases illustrate its potential for clinicians, patients, and researchers, including analyses of study type distributions for meta-analyses and the identification of studies relevant for individual patients. CONCLUSIONS By integrating heterogeneous study data into one interconnected knowledge base, the BRAinS-Graph improves the findability, accessibility, and reusability of study information, thereby advancing the FAIRness of health research. The present work establishes a foundation for graph-based study recommendation systems and cross-institutional research infrastructures.

BACKGROUND Patient-reported outcome measures (PROMs) such as health-related quality of life (HRQoL) are usually assessed at greater time intervals such as diagnostic time points, after treatment, and during follow-up. Many PROMs require frequent data collection (weekly or daily). Electronic PROMs enable high-resolution tracking but face declining response rates. Integrating PROMs into electronic health records (EHRs) could improve response rates and personalize therapy. OBJECTIVE This study aimed to evaluate the technical and clinical feasibility of high-frequency HRQoL assessments for routine care in EHRs. METHODS Patients receive emails on their mobile devices with 1-time links to a web-based app called MyEDC. This app communicates with an electronic data capture proxy in the demilitarized zone of the hospital. With a polling mechanism, these patient data are transferred to the protected hospital network and uploaded to the EHR system. HRQoL on a visual analog scale is assessed over the course of treatment in 4 clinical use cases: psychosomatics, hematology, visceral surgery, and neurosurgery. RESULTS Quality of life (QoL) trajectories were collected for 110 patients with daily or weekly data collection between 2 weeks and 3 months. The HRQoL analyses revealed clinically relevant findings across the 4 different medical domains. In the use case psychosomatics, 36 patients showed a significant increase in HRQoL following 4 weeks of therapy, rising from a median of 42% (IQR 32%-52%) to 60% (IQR 41%-67%; P=.01). An analysis of 25 patients in hematology demonstrated a significant correlation between HRQoL and 30-item QoL Questionnaire (EORTC QLQ-C30) global health status score (P=.02). For 26 patients in visceral surgery, a significant association was observed between HRQoL and the reported pain level (P<.001). The clinical feasibility was further highlighted in the neurosurgery use case, where 23 patients showed a median response time to the electronic PROM questionnaires of 5.3 (IQR 0.6-17.7) hours. HRQoL values were associated with disease-specific symptoms and scores, indicating clinical validity of this readout. Considerable variability of HRQoL was observed over time, both intraindividually and interindividually. Median area under the curve of HRQoL ranged from 0.46 to 0.79. Median time to answer ranged from 0.9 to 7.1 hours. No significant association between number of responses and age was observed. CONCLUSIONS High-resolution QoL trajectories with EHR integration are technically and clinically feasible. They offer a novel readout beyond survival analysis or PROM end point, enabling precise disease characterization and treatment comparison.