David Lampe is a Researcher at the Faculty of Health Sciences , Bielefeld University , Germany. He holds dual master's degrees in Public Health and Healthcare Policy, Innovation and Management from Bielefeld and Maastricht Universities. His research focuses on health services research using routine health insurance data , health economic evaluation , and drug therapy safety , with particular emphasis on interdisciplinary care pathways , patient safety , and digital health interventions . Conducts health economic evaluations of digital health innovations Specializes in medication safety and clinical decision support systems Manages the Transsectoral Optimization of Patient Safety (TOP) project Co-investigator in the eRIKA project on ePrescription systems His publications demonstrate expertise in AI-based clinical decision support , antimicrobial resistance , and continuity of care effects on polypharmacy. He has developed decision-analytic models for televisits in intensive care and herpes zoster risk factors . Current projects include implementation science studies of electronic medication management systems and health technology assessment of digital health applications.
Dr. David McAllister is a Wellcome Trust Intermediate Clinical Fellow at the University of Glasgow's Institute of Health and Wellbeing, where he has been employed since December 2016. His research bridges clinical medicine and advanced epidemiological methods, focusing on generating evidence that directly informs real-world healthcare practice. Dr. McAllister's research primarily centers on diabetes (both Type 1 and Type 2), multimorbidity, and cardiovascular outcomes. His work examines how treatments perform in diverse patient populations beyond the controlled settings of clinical trials, with particular attention to how factors like age, sex, and multiple chronic conditions affect treatment effectiveness. He has developed expertise in analyzing routinely collected health data from sources like UK Biobank and national healthcare databases to address methodological challenges in comparative effectiveness research. His recent publications (2023-2025) demonstrate a strong focus on precision medicine approaches, examining sex and age differences in treatment responses, developing methods for better clinical guideline development, and investigating how social factors interact with clinical conditions. His work frequently appears in high-impact journals including JAMA, Diabetes Care, and Nature Communications. Dr. McAllister has secured substantial research funding, including multiple Wellcome Trust awards, Medical Research Council grants, and Diabetes UK funding. His current projects (running through 2029) focus on standardized protocols for research using routinely collected health data, understanding problematic polypharmacy in diabetes, and predictors of early trial termination. Wellcome Trust Intermediate Clinical Fellowship Canadian Institutes of Health Research grant (2024-2029) Diabetes UK grant (2024-2029) Medical Research Council grants UK Research and Innovation funding His methodological expertise includes individual participant data meta-analysis, network meta-analysis, instrumental variable methods, and latent class analysis for multimorbidity clustering. These approaches allow him to address critical questions about how treatments work for specific patient subgroups in real-world settings, helping bridge the gap between clinical trial evidence and everyday medical practice.
Elizabeth Ford is an Associate Professor in Health Data Science at Brighton and Sussex Medical School, with expertise in analyzing primary care data and linked routinely collected health data to identify areas for healthcare improvement. She serves as Lead for Data Science in the NIHR Applied Research Collaboration in Kent, Surrey, and Sussex (ARC KSS), where she works to enhance health data science capacity across the region. Her research focuses on developing early detection models for mental health conditions and dementia, and understanding groups at risk of delayed diagnosis of long-term conditions. Professor Ford's academic journey began with a BA in Psychology from Oxford University, followed by an MRC-funded DPhil in clinical health psychology at the University of Sussex. She has held progressive academic positions at Brighton and Sussex Medical School, advancing from Research Fellow to her current role as Associate Professor. Her career demonstrates a clear trajectory in health data science, epidemiology, and public engagement with health data. Her research spans health data science methodology, ethical considerations in data use, and practical applications for healthcare improvement. Professor Ford specializes in risk prediction modeling, data quality assessment using free text, Bayesian methods for handling missing data, and public engagement approaches to build social license for data sharing. Her work addresses critical challenges in using routinely collected health data for research while maintaining public trust. Professor Ford's recent publications reflect her leadership in navigating the technical, ethical, and governance dimensions of health data science. Her work addresses data privacy concerns, quality assessment of electronic health records, development of risk prediction models, and public perspectives on data sharing - all critical areas for advancing responsible health data research. Future Leader in Health Data Science by the Farr Institute of Health Informatics Research (2016) As an educator, Professor Ford oversees undergraduate research methods curriculum and teaches epidemiology and health data science to Masters students. She runs a specialized course on AI in healthcare for medical undergraduates and regularly supervises research projects at all levels. Her teaching reflects her commitment to developing the next generation of health data scientists who can navigate both technical and ethical challenges in the field. Professor Ford actively collaborates with NHS, public health, and public stakeholders across Sussex, Kent, and Surrey to support responsible use of routinely collected data in research. Her leadership in developing secure regional data environments and understanding public perspectives on data sharing positions her at the forefront of health data science infrastructure development in the UK.
Daniel Capurro, MD, PhD is an Associate Professor in the Department of Internal Medicine at Pontificia Universidad Católica de Chile and maintains an Affiliate Assistant Professor position in the Department of Biomedical Informatics and Medical Education at the University of Washington in Seattle. His academic appointments include: Associate Professor, Department of Internal Medicine, School of Medicine, Pontificia Universidad Católica de Chile Affiliate Assistant Professor, Department of Biomedical Informatics and Medical Education, University of Washington Former Chief Medical Information Officer of the university's healthcare network at Pontificia Universidad Católica de Chile Dr. Capurro's research program centers on developing innovative methods to improve the re-utilization of routinely collected clinical data for clinical and translational research, quality improvement, and public health initiatives. His work prominently features process mining techniques applied across various healthcare settings including emergency departments, primary care clinics, and specialized treatment pathways. He has made significant contributions to implementing health information systems in Chilean healthcare institutions and developing mobile health interventions for cervical cancer prevention in underserved populations. Analysis of his recent publication record reveals a strong emphasis on applying data science methods to extract meaningful insights from clinical workflows, with particular focus on emergency department operations, diabetes care processes, and medication management. His research bridges clinical medicine with informatics to create practical solutions that improve healthcare delivery and patient outcomes. As an educator, Dr. Capurro designs and leads the integration track for semesters 2 through 5 in the medical curriculum at Pontificia Universidad Católica de Chile, where students engage in practical problem-solving activities that connect knowledge from various disciplines. While maintaining limited outpatient clinical practice, he dedicates most of his professional time to advancing biomedical informatics research and its application to real-world healthcare challenges in Chile and beyond.
Dr Derek Sarovich is a Senior Research Fellow at the Advance Queensland Centre for Bioinnovation, University of the Sunshine Coast, Queensland, Australia. Joining in mid-2017, he specializes in molecular microbiology and bioinformatics with focus on high-morbidity antibiotic-resistant infections. Education: PhD, Queensland Graduate Certificate in Research Communication, Queensland Bachelor of Science (Honours), Queensland His research bridges clinical microbiology and genomics to combat antibiotic resistance through next-generation sequencing. Key interests include bacterial pathogenesis mechanisms, molecular epidemiology of resistant pathogens, and genomic diagnostics for infections like melioidosis and Staphylococcus aureus. He pioneers translational applications where genomic data directly informs clinical treatment protocols. Scientific Awards: Fellow of the Australian Society for Microbiology (FASM), 2018 Charles Darwin University Vice Chancellor’s Award for Exceptional Performance in Research, 2016 NHMRC Research Excellent Award, 2013 Derek leads multiple nationally funded projects advancing genomic diagnostics: Metagenomics for COPD lung infection diagnosis (Wishlist - Sunshine Coast HHS) Darwin Perspective Melioidosis Study Years 27-31 (NHMRC) Rapid diagnostics for resistant infections (QIMR Berghofer & Waste to Biofutures Fund) Staphylococcus aureus host diversity sequencing (Pathology Queensland) He directs the Pathogen-Omics Lab, collaborating with Sunshine Coast Health Institute clinicians to implement genomic technologies at the patient bedside, with ongoing work focused on making sequencing cost-effective for routine hospital use.
Espen Jimenez Solem serves as a Clinical Professor at the University of Copenhagen's Department of Clinical Medicine while holding a dual appointment as Senior Physician in the Clinical Pharmacology Department at Bispebjerg and Frederiksberg Hospital (Capital Region of Denmark). His work bridges academic research and clinical practice through the hospital's PhaseIV unit, focusing on real-world drug evaluation within Denmark's universal healthcare system. His research centers on pharmacoepidemiology with dual pillars: drugs and pregnancy (examining congenital malformations, non-insulin dependent diabetes outcomes) and drug side effects (Stevens-Johnson syndrome, neurologic events). Methodologically, he leverages Danish nationwide registries for cohort analyses and case-time control studies, particularly investigating cystic fibrosis therapies (elexacaftor/tezacaftor/ivacaftor), antibiotic safety, and cardiovascular drug effectiveness. His fingerprint reveals strong emphasis on National Cohort Studies (83%) and During Pregnancy exposures (47%). Recent publication trends (2024-2025) demonstrate expanding international collaborations (Denmark/Australia) in cystic fibrosis research, with 4 of 9 featured articles analyzing CFTR modulator impacts on healthcare utilization. His work consistently employs registry data to assess real-world drug safety and effectiveness, spanning pharmacovigilance (mebendazole, metronidazole), perinatal outcomes, and chronic disease management. Scientific recognition includes Scopus citations across multiple publications, though no specific awards are documented in the provided materials. Collaborative leadership is evident through the TransformCF Study Group and multi-center registry projects. While no formal advising relationships are listed, his role in the PhaseIV unit suggests mentorship in post-marketing drug evaluation. Current research priorities include cystic fibrosis therapeutics, pregnancy-exposure outcomes, and neurologic adverse event characterization using advanced epidemiological methods.
