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Thanee M Uittenhout,1 Jesse Jansen,1 Kon-Siong Jie,2 Lieke Welling,3 Barbara L van Leeuwen,4 Leti van Bodegom-Vos,5 Anne M Stiggelbout,5,6 Trudy van der Weijden,1 IPTO Consortium: the scientific members of the IPTO ConsortiumA. Koch, F.M. van Nuenen, H. van der Wal-Huisman, L.L. van Baalen, M.J. Nogarede, S. Festen, V.C. Hanewinkel On behalf of the IPTO Consortium1Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health Medicine and Life Sciences (FHML) Maastricht University, Maastricht, the Netherlands; 2Department of Internal Medicine, Zuyderland Medical Center, Sittard, the Netherlands; 3Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands; 4Department of Surgery, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 5Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; 6Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, the NetherlandsCorrespondence: Thanee M Uittenhout, Email t.uittenhout@maastrichtuniversity.nlIntroduction: There is a growing interest in personalized decision-making in oncology. According to the Integrated Oncological Decision-Making Model (IODM), decisions should be based on information from three domains: (1) medical technical information, (2) patients’ general health status and (3) patients’ preferences and goals. Little is known about what kind of tool/strategy is used to collect the information, by whom this is collected (nurse, clinician) when this is collected (moment in the care pathway), and how this information should be collected and integrated within decision-making in oncological care pathways, and what its impact is.Methods: We searched PUBMED, Embase and Web of Science in October 2023 for studies looking at tools to collect and integrate information from the three domains of the IODM. We extracted data on the content and implementation of these tools, and on decision and patient outcomes.Results: The search yielded 2576 publications, of which only seven studies described collection of information from all three domains (inclusion criteria). In the seven included studies, information on the three domains was collected through dialogue, questionnaires, and assessments (what) by a nurse (2 out of 7 studies) or by other members of the Multi-Disciplinary Team (by whom) (5 out of 7 studies). Members of the Multi-Disciplinary Team subsequently integrated the information (5 out 7 studies) during their meeting (when), with patients and family attending this meeting in 2 studies (how). In terms of decision outcomes, 5 out of 7 studies compared the treatment recommendations before and after implementation of the tools, showing a modification of the treatment plan in 3% to 53% of cases. The limited data on patient outcomes suggest positive effects on well-being and fewer complications (3 out of 7 studies).Conclusion: The seven studies identified that integrated information from the three IODM domains into treatment decision-making lacked comprehensive information regarding the strategies, process, timing and individuals involved in implementing the tools. Nevertheless, the few studies that looked at patient outcomes showed promising findings.Keywords: person centred care, patient centred care, patient preferences, treatment decision-making, multi-disciplinary team, oncology

Currently, there are many diagnostic self-tests on body materials available to consumers. The aim of this study was to assess the effect of an online decision aid on diagnostic self-testing for cholesterol and diabetes on knowledge among consumers with an intention to take these tests. A randomized controlled trial was designed. A total of 1259 consumers with an intention to use a diagnostic cholesterol or diabetes self-test were selected from an existing Dutch Internet panel. The intervention group was invited to view an online decision aid offering general information on self-testing and test-specific information on cholesterol and diabetes self-testing, including indications for testing, how to perform the test and interpreting the result. The placebo condition consisted of a limited information sheet. Multiple regression analysis showed that the knowledge level in the diabetes arm was higher in the intervention group than in the control group (B = 0.657; 95% CI, 0.011-1.303), as was the number of participants with an informed choice (OR, 1.672; 95% CI, 1.134-2.465). No differences were found in the cholesterol arm. Consumers who are considering doing a self-test should have access to independent information on self-testing and be encouraged to read this information.

Diagnostic reasoning is considered to be based on the interaction between analytical and non-analytical cognitive processes. Gut feelings, a specific form of non-analytical reasoning, play a substantial role in diagnostic reasoning by general practitioners (GPs) and may activate analytical reasoning. In GP traineeships in the Netherlands, trainees mostly see patients alone but regularly consult with their supervisors to discuss patients and problems, receive feedback, and improve their competencies. In the present study, we examined the discussions of supervisors and their trainees about diagnostic reasoning in these so-called tutorial dialogues and how gut feelings feature in these discussions. 17 tutorial dialogues focussing on diagnostic reasoning were video-recorded and transcribed and the protocols were analysed using a detailed bottom-up and iterative content analysis and coding procedure. The dialogues were segmented into quotes. Each quote received a content code and a participant code. The number of words per code was used as a unit of analysis to quantitatively compare the contributions to the dialogues made by supervisors and trainees, and the attention given to different topics. The dialogues were usually analytical reflections on a trainee's diagnostic reasoning. A hypothetico-deductive strategy was often used, by listing differential diagnoses and discussing what information guided the reasoning process and might confirm or exclude provisional hypotheses. Gut feelings were discussed in seven dialogues. They were used as a tool in diagnostic reasoning, inducing analytical reflection, sometimes on the entire diagnostic reasoning process. The emphasis in these tutorial dialogues was on analytical components of diagnostic reasoning. Discussing gut feelings in tutorial dialogues seems to be a good educational method to familiarize trainees with non-analytical reasoning. Supervisors need specialised knowledge about these aspects of diagnostic reasoning and how to deal with them in medical education.

Viola Voncken-Brewster,1 Huibert Tange,1 Hein de Vries,2 Zsolt Nagykaldi,3 Bjorn Winkens,4 Trudy van der Weijden1 1Department of Family Medicine, 2Department of Health Promotion, School for Public Health and Primary Care (CAPHRI), Maastricht University Medical Center, Maastricht, Netherlands; 3Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; 4Department of Methodology and Statistics, CAPHRI, Maastricht University Medical Center, Maastricht, Netherlands Introduction: COPD is a leading cause of morbidity and mortality. Self-management interventions are considered important in order to limit the progression of the disease. Computer-tailored interventions could be an effective tool to facilitate self-management. Methods: This randomized controlled trial tested the effectiveness of a web-based, computer-tailored COPD self-management intervention on physical activity and smoking behavior. Participants were recruited from an online panel and through primary care practices. Those at risk for or diagnosed with COPD, between 40 and 70 years of age, proficient in Dutch, with access to the Internet, and with basic computer skills (n=1,325), were randomly assigned to either the intervention group (n=662) or control group (n=663). The intervention group received the web-based self-management application, while the control group received no intervention. Participants were not blinded to group assignment. After 6 months, the effect of the intervention was assessed for the primary outcomes, smoking cessation and physical activity, by self-reported 7-day point prevalence abstinence and the International Physical Activity Questionnaire – Short Form. Results: Of the 1,325 participants, 1,071 (80.8%) completed the 6-month follow-up questionnaire. No significant treatment effect was found on either outcome. The application however, was used by only 36% of the participants in the experimental group. Conclusion: A possible explanation for the nonsignificant effect on the primary outcomes, smoking cessation and physical activity, could be the low exposure to the application as engagement with the program has been shown to be crucial for the effectiveness of computer-tailored interventions. (Netherlands Trial Registry number: NTR3421.) Keywords: smoking cessation, physical activity, Internet intervention, tailoring, COPD

Mickael Hiligsmann,1-3 Caroline van Durme,2 Piet Geusens,2 Benedict GC Dellaert,4 Carmen D Dirksen,3 Trudy van der Weijden,5 Jean-Yves Reginster,6 Annelies Boonen21Department of Health Services Research, School for Public Health and Primary Care (CAPHRI), Maastricht University, The Netherlands, 2Department of Internal Medicine, CAPHRI, Maastricht University, The Netherlands, 3Department of Clinical Epidemiology and Medical Technology Assessment, CAPHRI, Maastricht University, The Netherlands, 4Department of Business Economics, Erasmus Rotterdam University, The Netherlands, 5Department of General Practice, CAPHRI, Maastricht University, The Netherlands, 6Department of Public Health, Epidemiology and Health Economics, University of Liege, BelgiumBackground: Attribute selection represents an important step in the development of discrete-choice experiments (DCEs), but is often poorly reported. In some situations, the number of attributes identified may exceed what one may find possible to pilot in a DCE. Hence, there is a need to gain insight into methods to select attributes in order to construct the final list of attributes. This study aims to test the feasibility of using the nominal group technique (NGT) to select attributes for DCEs.Methods: Patient group discussions (4–8 participants) were convened to prioritize a list of 12 potentially important attributes for osteoporosis drug therapy. The NGT consisted of three steps: an individual ranking of the 12 attributes by importance from 1 to 12, a group discussion on each of the attributes, including a group review of the aggregate score of the initial rankings, and a second ranking task of the same attributes.Results: Twenty-six osteoporotic patients participated in five NGT sessions. Most (80%) of the patients changed their ranking after the discussion. However, the average initial and final ranking did not differ markedly. In the final ranking, the most important medication attributes were effectiveness, side effects, and frequency and mode of administration. Some (15%) of the patients did not correctly rank from 1 to 12, and the order of attributes did play a role in the ranking.Conclusion: The NGT is feasible for selecting attributes for DCEs. Although in the context of this study, the NGT session had little impact on prioritizing attributes, this approach is rigorous, transparent, and improves the face validity of DCEs. Additional research in other contexts (different decisional problems or different diseases) is needed to determine the added value of the NGT session, to assess the optimal ranking/rating method with control of ordering effects, and to compare the attributes selected with the different approaches.Keywords: discrete choice experiment, nominal group technique, patient preference, medication attributes, osteoporosis