Integration of radiation oncology teaching in medical studies by German medical faculties due to the new licensing regulations

The new Medical Licensing Regulations 2025 (Ärztliche Approbationsordnung, ÄApprO) will soon be passed by the Federal Council (Bundesrat) and will be implemented step by step by the individual faculties in the coming months. The further development of medical studies essentially involves an orientation from fact-based to competence-based learning and focuses on practical, longitudinal and interdisciplinary training. Radiation oncology and radiation therapy are important components of therapeutic oncology and are of great importance for public health, both clinically and epidemiologically, and therefore should be given appropriate attention in medical education. This report is based on a recent survey on the current state of radiation therapy teaching at university hospitals in Germany as well as the contents of the National Competence Based Learning Objectives Catalogue for Medicine 2.0 (Nationaler Kompetenzbasierter Lernzielkatalog Medizin 2.0, NKLM) and the closely related Subject Catalogue (Gegenstandskatalog, GK) of the Institute for Medical and Pharmaceutical Examination Questions (Institut für Medizinische und Pharmazeutische Prüfungsfragen, IMPP). The current recommendations of the German Society for Radiation Oncology (Deutsche Gesellschaft für Radioonkologie, DEGRO) regarding topics, scope and rationale for the establishment of radiation oncology teaching at the respective faculties are also included.

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis

Background Medicine 2.0—the adoption of Web 2.0 technologies such as social networks in health care—creates the need for apps that can find other patients with similar experiences and health conditions based on a patient’s electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need. Objective This study aimed to evaluate the hypothesis that we can leverage similar EHRs to predict possible future medical concepts (eg, disorders) from a patient’s EHR. Methods We represented patients’ EHRs using time-based prefixes and suffixes, where each prefix or suffix is a set of medical concepts from a medical ontology. We compared the prefixes of other patients in the collection with the state of the current patient using various interpatient distance measures. The set of similar prefixes yields a set of suffixes, which we used to determine probable future concepts for the current patient’s EHR. Results We evaluated our methods on the Multiparameter Intelligent Monitoring in Intensive Care II data set of patients, where we achieved precision up to 56.1% and recall up to 69.5%. For a limited set of clinically interesting concepts, specifically a set of procedures, we found that 86.9% (353/406) of the true-positives are clinically useful, that is, these procedures were actually performed later on the patient, and only 4.7% (19/406) of true-positives were completely irrelevant. Conclusions These initial results indicate that predicting patients’ future medical concepts is feasible. Effectively predicting medical concepts can have several applications, such as managing resources in a hospital.

Gimme My Damn Data (and Let Patients Help!): The #GimmeMyDamnData Manifesto

Ten years ago, in 2009, “e-Patient Dave” deBronkart delivered an influential keynote speech at the Medicine 2.0 conference in Toronto, organized by the Journal of Medical Internet Research’s (JMIR’s) editor-in-chief Gunther Eysenbach, who themed the conference around the topics of participation, openness, collaboration, apomediation, and social networking to improve health care for the 21st century—with patient participation being a major component. Many see this as a defining event within the participatory medicine movement, perhaps the beginning of a social movement, similar to the women’s rights movement, with the title of Dave’s keynote “Gimme my damn data” becoming a rallying cry and hashtag for patients demanding more access to their electronic health records. On the occasion of the 20th anniversary of JMIR (and 10 years after the keynote), we are celebrating the impact of the keynote for the participatory medicine movement and #gimmemydamndata (also #GMDD) by publishing the transcript of these initial conversations as a manifesto of patients’ rights to access their data and their right to save their lives.

Prediction of Medical Concepts in Electronic Health Records: Similar Patient Analysis (Preprint)

BACKGROUND Medicine 2.0—the adoption of Web 2.0 technologies such as social networks in health care—creates the need for apps that can find other patients with similar experiences and health conditions based on a patient’s electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need. OBJECTIVE This study aimed to evaluate the hypothesis that we can leverage similar EHRs to predict possible future medical concepts (eg, disorders) from a patient’s EHR. METHODS We represented patients’ EHRs using time-based prefixes and suffixes, where each prefix or suffix is a set of medical concepts from a medical ontology. We compared the prefixes of other patients in the collection with the state of the current patient using various interpatient distance measures. The set of similar prefixes yields a set of suffixes, which we used to determine probable future concepts for the current patient’s EHR. RESULTS We evaluated our methods on the Multiparameter Intelligent Monitoring in Intensive Care II data set of patients, where we achieved precision up to 56.1% and recall up to 69.5%. For a limited set of clinically interesting concepts, specifically a set of procedures, we found that 86.9% (353/406) of the true-positives are clinically useful, that is, these procedures were actually performed later on the patient, and only 4.7% (19/406) of true-positives were completely irrelevant. CONCLUSIONS These initial results indicate that predicting patients’ future medical concepts is feasible. Effectively predicting medical concepts can have several applications, such as managing resources in a hospital.

Googling for neurological disorders: from seeking health-related information to patient empowerment, advocacy and open self-disclosure in the Neurology 2.0 era (Preprint)

UNSTRUCTURED The Internet, since its introduction, has played a major role in re-shaping the patient-physician communication and interaction, fostering a shift from a paternalistic to a patient-centered model. Because of its dynamic nature, the Internet has been used a platform not only for disseminating knowledge, facilitated by an improved access to an increasing figure of available resources, but also to spread advocacy, awareness, funding raising and even to openly self-disclosure one's own disease, breaking and removing any taboo. The era of Medicine 2.0, as stated by Eysenbach, is characterized by openness, collaboration, participation and social networking. The situation is completely different with respect to time when Odone's parents, after the diagnosis of adrenoleucodystrophy of their son, decided to attend the medical school in order to collect information about a devastating, unheard disease and had to fight against the medical establishment to convince of the effectiveness of their discovered therapeutics. Orphan and rare neurological diseases find nowadays their space and dignity online. However, not to dissipate Odone's family legacy and their “complicated lessons” (Lerner), some issues should be carefully addressed by health authorities, such as the reputability, reliability and accuracy of the online available material, avoiding to spread material that could instill in patients illusions and unjustified hopes. Neurologists should be aware of this, participating in online activities, and recommending high quality, selected sites to their patients.