A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

Shared decision making (SDM) and patient-centered care require patients to actively participate in the decision-making process. Yet with the increasing number and complexity of cancer treatment options, it can be a challenge for patients to evaluate clinical information and make risk–benefit trade-offs to choose the most appropriate treatment. Clinicians face time constraints and communication challenges, which can further hamper the SDM process. In this article, we review patient decision aids (PDAs) as a means of supporting SDM by presenting clinical information and risk data to patients in a format that is accessible and easy to understand. We outline the benefits and limitations of PDAs as well as the challenges in their development, such as a lengthy and complex development process and implementation obstacles. Lastly, we discuss future trends and how change on multiple levels—PDA developers, clinicians, hospital administrators, and health care insurers—can support the use of PDAs and consequently SDM. Through this multipronged approach, patients can be empowered to take an active role in their health and choose treatments that are in line with their values.

Tutors have only limited time to support students. In this paper, we discuss a model that addresses the question of how to help students answer content-related questions. A small group of students is created, which consists of the student who asked the question and peers who should be able to answer it. Criteria used to compose the group are the content of the question in relation to the knowledge and skills of the peers. The model supports the collaboration with text fragments selected from the study materials. We will introduce the model and briefly discuss the results of the calibration and a simulation of the model. Finally, we will discuss the outcome of an experiment with two groups of approximately 50 students, who used the model for a period of 8 weeks. The results indicate that the students positively value the model and that it is possible to solve a substantial number of their questions.

Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05Learning Networks are online social networks through which users share knowledge which each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks, trust formation and profiling in Learning Networks, and peer-support among Learning Network participants. This discussion will be interspersed with implementation guides for Learning Networks and with a discussion of the more extended case of a Learning Network for Higher Education. Taking into consideration research currently carried out at our own centre and elsewhere, the chapter will close off with a look into the future of Learning Networks

Background Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly. Methods We followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our user-base consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers. Results Informational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/ . Conclusions User-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable. Electronic supplementary material The online version of this article (10.1186/s12911-019-0862-4) contains supplementary material, which is available to authorized users.

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ObjectivePatients diagnosed with advanced larynx cancer face a decisional process in which they can choose between radiotherapy, chemoradiotherapy, or a total laryngectomy with adjuvant radiotherapy. Clinicians do not always agree on the best clinical treatment, making the decisional process for patients a complex problem.MethodsGuided by the International Patient Decision Aid (PDA) Standards, we followed three developmental phases for which we held semi‐structured in‐depth interviews with patients and physicians, thinking‐out‐loud sessions, and a study‐specific questionnaire. Audio‐recorded interviews were verbatim transcribed, thematically coded, and analyzed. Phase 1 consisted of an evaluation of the decisional needs and the regular counseling process; phase 2 tested the comprehensibility and usability of the PDA; and phase 3 beta tested the feasibility of the PDA.ResultsPatients and doctors agreed on the need for development of a PDA. Major revisions were conducted after phase 1 to improve the readability and replace the majority of text with video animations. Patients and physicians considered the PDA to be a major improvement to the current counseling process.ConclusionThis study describes the development of a comprehensible and easy‐to‐use online patient decision aid for advanced larynx cancer, which was found satisfactory by patients and physicians (available on http://www.treatmentchoice.info). The outcome of the interviews underscores the need for better patient counseling. The feasibility and satisfaction among newly diagnosed patients as well as doctors will need to be proven. To this end, we started a multicenter trial evaluating the PDA in clinical practice (http://ClinicalTrials.gov Identifier: NCT03292341).Level of EvidenceNA Laryngoscope, 129:2733–2739, 2019