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Welcome to the second European Conference on Diagnostic Error in Medicine in Bern, the capital of Switzerland. The conference will be an excellent platform to present the latest research and policy initiatives on diagnostic quality and safety, and to network with international peers active in this field.
This conference will host multi-disciplinary speakers from a range of scientific domains involved in improving diagnosis, including psychology, medical education, clinical and laboratory medicine, human factors engineering, epidemiology, informatics, and quality and safety research.
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The conference is organized in collaboration with Patient Safety Switzerland.
This conference is in English.
Sponsors and Supporters
Mundipharma Medical Basel
EBSCO Information Services GmbH Berlin - Germany
Department of Emergency Medicine, Inselpital University Hospital Bern
Department of Emergency Medicine, Inselspital University Hospital Bern, Switzerland
Ms. Sascha Fischer
Diagnosticians face many aspects of risk and uncertainty when making decisions. They need to understand the benefits and harms of employing diagnostic tests, for instance for the early detection of disease, and they need to be able to understand the meaning of test results. Furthermore, they need to identify patients at risks quickly and, more generally, find the right diagnosis for each patient. None of these tasks is trivial, and the literature has identified various pitfalls and systematic errors. But the decision sciences have also developed methods to support diagnosticians. Importantly, research has shown that there is not one tool to solve all diagnostic tasks, but that diagnostic decision making needs to be an adaptive process where the right decision making tool is applied to the right task. The adaptive toolbox for successful diagnosticians includes transparent representations for understanding statistical information, smart heuristics for dealing with uncertainty, and social intelligence, which is particularly useful when pooling several independent judgments.
Diagnostic errors contribute substantially to patient morbidity and mortality. Among the many factors contributing to diagnostic error, cognitive errors of the diagnosing physician have received considerable attention. Many authors have attributed cognitive errors to the use of fast, intuitive thinking and consequentially advocated the implementation of strategies that enforce deliberate considerations. However, the notion of a general superiority of slow analytic thinking in medical decision making has been contested recently. For example, a number of studies correlating diagnostic accuracy with time to diagnosis have found strong inverse relationships, where faster diagnoses were associated with higher quality of diagnoses. Additionally, human “intuition” has evolved well over time and developed a number of mechanisms that work quite well in uncertain and probabilistic environments. A particularly successful approach of strategies neglecting the vast majority of potentially available information and instead focusing on few key characteristics are so called heuristics. After a lifetime of research devoted to heuristic reasoning and information literacy, Gerd Gigerenzer is in a unique position to familiarize the audience with the “fast and frugal” heuristic approach in general and in diagnostic reasoning specifically and contrast his views with extensive research into information literacy of physicians, arguably an important prerequisite for any more “analytic” approach.
Missing a serious diagnosis, such as a cancer, is the most important kind of diagnostic error in general practice because it can have the worst consequences for patients: inappropriate or delayed treatment can directly affect survival. I will describe the research that underlay the design of a diagnostic support system for General Practitioners (currently in prototype form), and how it was evaluated in different studies in both low and high-fidelity simulations. I will also discuss GPs’ and patients’ attitudes to diagnostic support and possible ways to improve acceptance of external aids by decision makers.
The terms overdiagnosis, underdiagnosis, and diagnostic error are increasingly used in many different clinical contexts, with varying definitions and different contributing mechanisms, leading to troublesome communication. A conceptual framework is presented describing the overarching problems surrounding tests into different types of uncertainty and errors. Briefly, uncertainty about whether more good is being done than harm may exist at several critical decision points: who should receive a test, how to precisely define a disease and its subtypes, and how should a condition be managed? Alongside these uncertainties, various errors can occur. This framework assists researchers to think more carefully about the multiple underlying key mechanisms leading to problems jointly labeled as overdiagnosis or underdiagnosis and diagnostic error.
Machine learning techniques, including deep learning, seem to have a great potential in reducing diagnostic errors in radiology, After an introduction into different causes of diagnostic errors and key ideas of machine and deep learning, this talk will show several applications in the medical imaging field with a focus on neuroimaging, including own work on multiple sclerosis and different types of dementia. Challenges in using machine learning such as the need for much data and the black box-problem will be discussed.
The presentation, “Diagnostic error from a patient perspective” offers an overview of 2 cases studies of diagnostic error through the lens of a mother and widow who has partnered with the healthcare system to prevent harm from missed, wrong and delayed diagnosis. The presentation will map the diagnostic journeys of her son, Cal, and late husband, Pat, onto the National Academy of Medicine’s Diagnostic Process diagram to illustrate the complexities of diagnosis as well as the resultant “what ifs” that patients, clinicians and healthcare systems ask themselves after a diagnostic error that can serve as catalysts for diagnostic improvement efforts.
The presentation will also offer an example of the power of patients partnering with researchers, educators, policy makers, NGOs, public health officials, quality assurance organizations and healthcare systems that resulted in a national change in the standard of care of newborn jaundice management in the USA.
As the Director of Patient Engagement for the Society to Improve Diagnosis in Medicine (SIDM), Sue Sheridan will also share how she is leveraging patient perspectives to improve all facets of SIDM’s diagnostic improvement efforts.
This presentation will discuss challenges and opportunities related to understanding and reducing diagnostic error through health system-centric approaches (i.e. focusing more than just what’s in “the doctors head” on broader system-related factors that inevitably influence these thought processes). Topics will include the use of health information technology and data from electronic health records, organizational approaches to measure the problem of diagnostic error, and application of learning health systems approaches to understand and improve diagnosis.
Workshops & Seminars
Marie-Claude Audetat/Mathieu Nendaz
Between 5% and 15% of medical students and residents suffer from academic difficulties; unfortunately, these difficulties are often recognized late in the learners’ course of study and training, usually when problems arise in clinical rotations.
Studies on diagnostic errors and difficulties, at undergraduate and postgraduate levels, indicate that a majority of errors include a cognitive component.
According to Graber et al. (2005), the majority of cognitive difficulties are not directly related to a lack of knowledge, but rather to a flaw in data collection, data integration, and data verification.
Limiting a remediation process to the sole knowledge dimension may thus prove insufficient to address reasoning difficulties.
Many studies indicate that delayed or poor identification and remediation of clinical reasoning difficulties can lead to clinician underperformance and can ultimately, risk compromising patient care.
Identifying and delineating concerns early may facilitate timely remediation of problems.
Jason Maude, Gordon Caldwell, Tobias Mueller
This session aims to provide participants with practical ideas and advice about how to improve diagnostic decision making. The panel includes seasoned practitioners from primary, secondary and tertiary care who will talk about the particular issues they face and solutions they use and have tried.
They will also talk about the all-important cultural issues which often hinder diagnostic improvement initiatives within an institution and how they have tackled them. There will be plenty of time left for questions and discussion.
Dr. Tobais Mueller will give a case based presentation on how computerized diagnostic decission support tools can assist in undiagnosed and rare diseases. State of the art tools will be presented, chances highlighted and limitations discussed.
Paul Epner, Mario Plebani, Cécile Ravesloot
In this session, diagnostic professionals will talk about how errors in the laboratory testing and imaging processes can lead to diagnostic errors, and will discuss the role clinical laboratory specialist and radiologists could fulfill in improving the diagnostic process and in reducing diagnostic harm.
First, Paul Epner, a leader in the field of improving the value of laboratory diagnostics for patients, will present his 5-cause framework for errors in the laboratory testing process, focusing on errors in the pre-pre- and post-post-analytical phases of lab testing (i.e. errors in ordering the right test and correct test interpretation).
Next, Prof. Plebani, (clinical biochemist) a laboratory medicine professional with broad experience in measuring errors in the total laboratory testing process, will present data the frequency and nature of these errors and will discuss how use of quality indicators can contribute to decreasing the incidence of diagnostic errors.
Finally, Dr. Cécile Ravesloot, radiologist, will present her research work on improving image interpretation skill by radiologists by introducing authentic images in assessments.
This session describes the mechanism by which clinical laboratory testing can lead to diagnostic error and then illustrates the leading role laboratory physicians and scientists could take to improve the diagnostic process and reduce diagnostic harm. Opportunities for laboratory professionals to deliver value to patients and the care delivery team through an outcomes-oriented approach will also be described as will effective Interventions and practices that can be implemented to shift from being lab-centric to patient-centered clinical laboratories.
Over the last decades, more and more timely and accurate laboratory test results have been produced leading the clinical laboratory to be recognized as the “nerve centre of diagnostic process”. Every day, clinical laboratories worldwide analyze billions of samples to provide essential information that allows a reliable clinical decision-making (diagnosis, drugs prescriptions, patients admission/discharge from hospital). However,the complex process that finally provide laboratory information is not error-free. Since 1997, with the publication of the landmark paper by Plebani and Carraro (1), a number of papers demonstrated that errors can occur in any step of the process, in the pre-analytical (46–68.2%), analytical (7–13%) and post-analytical phases (18.5–47%).
Although, compared to billions of laboratory results, the absolute percentage of errors could appear very low, on the contrary, it could become relevant when considered in relation to patient outcome. Although Plebani and Carraro demonstrated that 74% of laboratory errors did not affect patients’ outcome, the other 26% translates into a patient care problem, leading at least to further inappropriate investigations, patient discomfort, increased costs (19%) and, even worse, to inappropriate care and/or modification to therapy (6.4%).
Recent studies in this area have led to a better understanding of the frequency and nature of diagnostic errors, and their relationship to laboratory testing error. Data on errors in the pre-pre analytical phase (initial procedures performed neither in the clinical laboratory nor, at least in part, under the control of laboratory personnel) underline that failures to order appropriate diagnostic tests, including laboratory tests, accounted for 55% of observed breakdowns in missed and delayed diagnosis in the ambulatory setting and 58% of errors in the Emergency department. In the final steps of the TTP loop, the incorrect interpretation of diagnostic or laboratory tests was found to be responsible for a high percentage of errors in the ambulatory setting as well as in Emergency departments. The search for valuable quality indicators (QIs) for intra- and extra-analytical phases of the testing process and for harmonizing all steps, including test ordering and data interpretation, represents a fundamental issue in projects aiming to improve quality and patient safety.
Diagnostic radiology errors have gained more attention in the last decades. The prevalence of errors in diagnostic radiology is not completely known, however in the past, rates up to 30 percent were reported (Garland 1949, Berlin 2014). Till know different strategies did not lower these rates much (Berlin 2007). One of these strategies is to learn from what went wrong. All hospitals in the Western world have by now implemented a workflow to safely report errors, scrutinize the process, learn and make changes to the diagnostic process. Increasing performance in image interpretation by improving training and education is another way of reducing errors, which has received less attention. Consequently not much is known about what radiological expertise implies and how it is developed. Therefore radiology training programs and education differ greatly between institutions and did not change much over the years. This in great contrast with the clinical practice of radiologists which is constantly changing.
Radiological image interpretation can be roughly divided in three components: perception, analysis and diagnosis (Van der Gijp 2014). During perception the observer is occupied with detection of abnormalities, and knowledge on normal anatomical structure plays an important role. Analysis implies in depth observation of features of the abnormalities. In the diagnostic phase a conclusion (diagnosis) or action plan for additional diagnostic tests is made. We know from many studies that there is a large inter- and also intra-observer variability in interpreting radiological images (Berlin 2014, Krupinski 2011). So the obvious question is, what makes a radiologist an expert in image interpretation? Image interpretation is traditionally learned in clinical practice, were residents learn from their supervising radiologists. After becoming a registered radiologist in most European countries no formal education and assessment takes places anymore. The common sense was that experience, amount of images read, automatically led to expertise (Manning 2006). However that does not explain the performance differences between experienced radiologists. Furthermore a study in a Dutch cohort of radiology residents showed that mean knowledge growth levels off at average exam scores of 50% with a large dispersion before the end of training (Ravesloot 2017).
In radiology major improvements in imaging and viewing techniques changed needed skills for radiologists completely in the last 15 years. At the end of the 20th century radiologists were viewing printed single slices of cross sectional imaging techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) hanging next to each (tile viewing). Nowadays all radiological images are digitally viewing and CT and MRI scans, which are often huge volumetric images, can be scrolled through in every possible direction. This is just one of the major changes effecting radiologists’ day-to-day work and different studies showed that the skills for radiological image interpretation changed accordingly. For example, participants interpreting volumetric CT scans were relatively more time occupied with detection of abnormalities in a CT scan than when they were viewing single slices from a CT scan (Van der Gijp 2015). In addition dispersion of scores between high- and low-performers was much larger in assessments with volumetric CT scan than for exams with single slices (Ravesloot 2015a), and in addition volumetric image exams had a better correlation with an external validation measure of volumetric anatomy knowledge (Ravesloot 2015b). Furthermore, in a study of Drew (2013) there appeared to be different strategies of interpretation of volumetric images among radiologists with some evidence that one technique was correlated with better detection rates.
Art Papier, Jason Maude
In this session, experts from the field of machine learning will present the current status of machine learning applications for diagnosis and will illustrate this by presenting state-of-the-art solutions.
First, Dr. Art Papier, will talk about machine learning, clinical decision support and diagnosis.
Finally, Jason Maude will share his thought on different approaches, e.g. rule-based and machine learning, for differential diagnosis generating software.
We are the beginning of a revolution in machine learning and artificial intelligence. The visual specialties radiology, dermatology, pathology, and ophthalmology are all seeing striking and rapid advances in machine interpretation of images. In dermatology there has been much focus on machine learning for the detection of skin cancer over the past decade. More recently progress has been made in general diagnosis of skin clues by machine learning. In this session we will discuss how machine learning can be integrated into broad diagnostic support, and how visual representation of medical diagnostic complexity enhances both System 1 and System 2 cognition.
With the proliferation of DDx Generators and Symptom Checkers it’s important for clinicians to understand the big differences in how they are built as this determines their capabilities. In this talk we will look at the key methods of construction, some examples and suggest criteria clinicians can use for evaluating them.
Health care systems are continually involved in quality improvement, but rarely directed at diagnostic error. We are early in the development of effective interventions. In this workshop, we will share case studies of what works, hear the evolution of a collaborative that sought to test some of these interventions, and in a highly interactive session, determine strategies for implementing diagnostic error quality improvement in your clinical setting now.
Worldwide, there are more than 15 million strokes annually, roughly one third of which result in death. In the US, stroke is the most commonly missed vascular event and results in both the most malpractice claims and the most total payouts. Strokes are most often missed when symptoms are transient, mild, or non-specific. The symptom most tightly linked to missed stroke in frontline healthcare settings is dizziness/vertigo. Causes appear disproportionately related to misinformation and lack of expertise in bedside assessment. Novel approaches to teamwork, training, technology, and tuning may facilitate timely, accurate diagnosis, thereby preventing serious misdiagnosis-related harms, including permanent disability and death.
Since 2008, the UK government and major charitable research funders have supported a range of policy initiatives, together with population health and healthcare system research projects, to improve the diagnosis of cancer. These policies, which were prompted by prior epidemiological research documenting unfavourable international comparisons for cancer outcomes in English patients, have since transformed both the evidence base and the configuration of health services and pathways for diagnosing cancer in symptomatic patients. Professor Lyratzopoulos will highlight key achievements in this transformational journey and summarise recent research findings. He will draw out the potential usefulness of UK research and policy to improve the diagnosis of cancer for other disease areas and health systems.
Many diagnoses are first made in the emergency department, which is particularly prone to error: information is limited and sometimes unreliable, time pressure and workload are high and diagnosticians work in ad hoc teams with interfaces to many colleagues from ever changing disciplines. What is more is that emergency diagnoses (and errors therein) are often particularly consequential for patients and diagnosticians alike.
Thursday, August 30 2018
Friday, August 31 2018
Inviting Junior Researchers Attending DEM Europe to a Special Preconference Session
Marie-Claude Audétat, M.Ps., Ma (Ed), Ph.D., is Associate Professor at the faculty of Medicine, University of Geneva. Since May 2014, she is in charge of the axis of educational research in the Primary Care Unit at the University of Geneva, Switzerland. Ms Audétat is involved in innovative projects regarding clinical reasoning and faculty development in the Unit of Development and Research (UDREM).
She is also Associate Professor in the department of Family and Emergency Medicine at the Université de Montréal, Canada, where she served as Faculty Development Director, from 2010 to 2014.
Gordon Caldwell has been a Consultant Physician in the UK since 1993, first at Worthing Hospital in West Sussex, until February 2018 when he moved to the hospital in Oban on the west coast of Scotland. He studied pre-clinical medicine at Worcester College, Oxford University and completed his clinical training at King’s College Hospital Medical School London, qualifying in 1980. Gordon subsequently worked in Brighton, Edinburgh, the Royal Postgraduate Medical School, and Newcastle upon Tyne Hospitals, before taking up his post in Worthing. From early in the 2000s he developed an interest in the organisation of Ward Rounds from the point of view of Quality and Safety of patient care and training of junior doctors for future clinical leadership roles. His interests were kindled further after attending the International Forum on Quality and Safety in Healthcare in early 2009, and in 2010 he published an article in the BMJ on the need for a Diagnostic Cockpit to reduce misdiagnosis, similar to the Sterile Cockpit environment in a commercial plane during takeoff and landing. In UK hospitals most working environments in acute care settings such as Accident and Emergency, the Acute Assessment Units and general wards seem to have developed to the point that they actually enhance misdiagnosis. He has tried to standardise his working processes on ward rounds and has published on the use of a Considerative Checklist to enhance Quality and Safety. Gordon has also published on the inadequacies and unusability of clinical information systems at the patient side. Gordon Calwell lives in a very rural setting and enjoys hill walking and photography.
Juliane Kämmer is a psychologist by training. She received her diploma in 2009 and her PhD in 2013 from the Humboldt University Berlin. Since 2009, she is affiliated with the Max Planck Institute for Human Development in Berlin, first as a Predoc and then as a Postdoc. Currently, she is the Head Research Scientist at the AG Progress Test Medicine at the Charité Medical School Berlin. Ms Kämmer enjoys research at the intersection of psychology, medicine, medical education and computer science. She is interested in medical decision making, particularly diagnostic decision making in the emergency room and the role of teams in the diagnostic process.
Olga Kostopoulou is Reader in Medical Decision Making at Imperial College London. Prior to her current position, she held academic appointments at King’s College London (Senior Lecturer) and the University of Birmingham (Lecturer). She holds a first degree, an MSc and a PhD in Psychology. She applies psychology theory and (primarily quantitative) methods to the study of medical decision making. She studies the cognitive parameters that underlie professional medical judgements and decisions, and researches ways to support them. She has had funding from the Department of Health, Cancer Research UK, and the EU. She is Associate Editor of the journal Medical Decision Making, has served as elected Trustee on the Board of SMDM (smdm.org), and has chaired the biennial SMDM European meeting in London (June 2016). She delivers short courses on the psychology of medical decision making to UK and international audiences.
Jason Maude serves as Chief Executive Officer and Co-founder of Isabel Healthcare Ltd. Prior to co-founding Isabel Healthcare Ltd, Maude spent 12 years working in the finance and investment banking industry in Europe. Throughout his career, Maude served as a top-ranked equity analyst at Kleinwort Benson Securities, Smith Barney and Dillon Read. While at Dillon Read, a prestigious U.S. investment bank, Maude served as partner and managing director of the company’s UK office. This prominent position led Maude to AXA Investment Managers where he led equity research. In 1999, Maude’s three-yearold daughter, Isabel fell seriously ill as a result of a misdiagnosis. Isabel’s illness and experience inspired Maude to abandon his city career and create Isabel Healthcare. He is also on the editorial board of the journal Diagnosis.
Tobias Mueller has an educational background in computer science and medicine. Since 2014, he is working at the center for undiagnosed and rare diseases at the university clinic, Marburg, Germany. Established in December 2013, the center assists patients and physicians with diagnostic proposals in complex cases. The inquiries are generally patient initiated and the center is open to the general public. Dr. Mueller’s research activities focus on the evaluation of computerized decision support tools in rare and undiagnosed diseases and epidemiological aspects of these patient group. Since 2017, he also serves as head of digital tranformation for Rhön-Klinikum AG, a private healthcare provider with hospitals in five locations and about 16,500 employees across Germany.
Christiana Naaktgeboren, MPH, PhD is an epidemiologist working at the Julius Center for Health Sciences and Primary care at the University Medical Center Utrecht. In 2015 she completed her PhD thesis on ‘Improving the methodological framework for diagnostic studies’. As an assistant professor, she continues her research on this theme and on the closely related problems of overdiagnosis and diagnostic error. In addition to methodological research, she also provides advise for studies on improving laboratory testing the department of Laboratory, Clinical Chemistry and Hematology as well as for several trials (many of which are diagnostic) within the Dutch Consortium for Healthcare Evaluation in Obstetrics and Gynecology. She has co-authored over 20 publications in the field of diagnosis or prognosis.
Mathieu Nendaz is an internist at the Geneva University Hospitals and trained in health professions education at the University of Illinois at Chicago. He is presently also Director of the Unit of Development and Research (UDREM) and Professor at the Faculty of Medicine, University of Geneva, Switzerland. His research interests include Internal Medicine and Medical Education. In this field, he is particularly interested in decision-making, clinical reasoning, clinical supervision, and interprofessional issues. He is deeply involved in direct teaching, clinical supervision and training, as well as in the development and organization of teaching concepts and medical curriculum.
Art Papier, MD is a founder of VisualDx and CEO. A dermatologist, and medical informaticist, Art has a particular interest in designing clinical systems that leverage the human ability for pattern recognition, thereby increasing clinical accuracy and reducing diagnostic error at the point of care. In line with this goal, he has led the development of VisualDx, the first diagnostic clinical decision support system to be widely used. Art is also passionate about the engagement of people in their medical decisions, in consumer health, and developing tools to educate and empower patients in their homes and on their mobile devices.
A graduate of Wesleyan University, Art received his MD from the University of Vermont College of Medicine, and completed his graduate medical training at the University of Rochester Medical Center. He is also an Associate Professor of Dermatology and Medical Informatics at the University of Rochester School of Medicine and Dentistry.
Cécile José Ravesloot, MD, PhD, graduated from Medical School at the University of Utrecht in The Netherlands in 2005. After she worked for a short period at the emergency department of St Jansdal Hospital in Harderwijk, The Netherlands, and as a junior teacher at the department of anatomy of the University Medical Center Utrecht, she was trained as a radiologist at the University Medical Center in Utrecht and Gelre ziekenhuizen Apeldoorn till August 2017. In addition to her radiology training she was radiology teacher at UMC Utrecht and was involved in several educational projects which aimed to improve radiology educational and assessment programs. In March 2016 she defended her PhD-thesis “Assessing Expertise in Radiology”. Since October 2017 she works as a radiologist at Radboudumc in Nijmegen, The Netherlands.
David Schwappach is scientific director of the Swiss Patient Safety Foundation since 2008 and professor for Patient Safety at the Institute of Social and Preventive Medicine (ISPM) at the University of Bern. David obtained a PhD in theoretical medicine in 2001 and received the venia legendi for Public Health in 2006. His research interests reside in acute care patient safety, including patient safety in clinical cancer care, communication between health care professionals, systems and process design, and implementation of safety measures into clinical practice. David Schwappach is engaged in teaching patient safety to various audiences, including medical students, postgraduate students of public health, and clinicians from many disciplines and professions. He serves as a referee for a variety of international journals and institutions and is member of several Patient Safety Boards.
The conference will take place at:
Kinderklinik (Childrens Hospital)
Ettore Rossi (lecture hall Ettore Rossi)
Inselspital University Hospital Bern, Switzerland
Getting ThereBy plane: Bern has a small airport with connections to only a few European cities. Larger airports are located in Basel and Zurich, both with decent train connections to Bern (one-hour train ride). Geneva has another international airport, around 2 hours by train.
By train: Bern Hauptbahnhof (Bern central station) is a 15-minute walk or a 5-minute bus ride from Inselspital. Take bus number 11 heading to “Holligen” and get off at “Inselspital”, not “Inselplatz”.
By car: Exit A1 at “Forsthaus / Inselspital” and follow the signs to “Inselspital”, about 5 minutes by car from the highway. Parking at and around Inselspital is very limited and expensive. We advise you not to come by car.
From AMEE 2018 Basel: AMEE will generously offer a free transfer to participants of both conferences.
Fees (Swiss Francs)
* Early bird registration ends June 10, 2018.
Fees listed are in Swiss Francs (CHF). Conversion
Reduced fees for full time students, PhD students, non-physician health professionals such as nurses, lab technicians, physician assistants, physiotherapists; (proof required at on-site registration)
Participants of AMEE 2018 receive a 10% discount. Discount will be payed on-site upon request (AMEE badge required).
Registration fee includes: conference participation and lunch and coffee breaks but not accommodations, travel, transport, or dinner. Participation in the "Meet the faculty" dinner is at your own expense.
Dinner on August 30, 2018: Restaurant close to conference premises and for EuroDEM participants only. Costs CHF 60 (payable with the registration fee) including salad/grill/dessert buffet and beverages.
Cancellation until August 1, 2018: CHF 30
Cancellation after August 1, 2018: No refund
Please note: Book your accommodation in Bern early.