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Abstract Background Informing individuals about their risk of cancer can sometimes have negative consequences, such as inflicting unnecessary worry and fostering stigma. This study aims to explore how patients diagnosed with breast or colorectal cancer perceive and experience risk communication, particularly concerning the increased focus on lifestyle behaviors as the cause of cancer. Methods Semi-structured interviews were conducted during autumn 2023, with 23 Swedish individuals, aged 34 to 79 years, diagnosed with breast or colorectal cancer. The collected data were analyzed using inductive thematic analysis described by Braun & Clark. The study adopted an experiential orientation grounded in critical realism. Results Five themes with ten sub-themes were identified: Thoughts and feelings about the causes of cancer, Moralizing messages and negative encounters, The need to take action, Balancing uncertain risks and a fulfilling life, and Societal benefits of risk communication. The participants expressed that knowledge of the the cause of cancer is closely related to the possibility of taking preventive action against relapses. Ability to take action was also perceived important for their well-being. Therefore, risk information entails both feelings of self-blame and hope for the future. Participants asked for both information and lifestyle support from healthcare professionals. Lifestyle interventions and patient support groups were solicited and perceived as an important aspect of cancer survivals’ well-being, and may help to reduce the cancer-related stigma. Conclusion Individuals that have or have had breast or colorectal cancer, including those leading healthy lifestyles, found moralistic risk information offensive, leading to feelings of shame when thinking about other peoples thoughts. Balancing information involves providing transparent, evidence-based information while considering individual and social contexts, avoiding stigmatization and blame, and supplementing information with support.

This study explores a new way to model the adoption of AI, specifically online recommender systems. It aims to find factors that can explain the variation in usage in terms of differences between individuals and differences over technologies. We analyzed survey data from users of online platforms in the U.S. using a two-level structural equation model (SEM) (N = 1007). In this model, the dependent variable was the usage rate, which was defined as the share of time a person used a particular recommender system (e.g., “People You May Know”) when they use the platform (e.g., Facebook). The individual responses (within-systems level) were clustered in the 26 recommender systems (between-systems level). We hypothesized that three technology-specific factors, adapted from the Diffusion of Innovations (DOI) theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), could explain the variations in usage at both levels: perceived performance expectancy (PE), perceived effort expectancy (EE), and perceived hedonic motivation (HM). Our estimated model showed that usage was associated with PE and HM at the within-system level and only with PE at the between-system level. A considerable part of the variation in usage across the 26 systems could be explained by PE only (R2 = 0.30). The most important contribution to practitioners is that this study provides evidence for the idea that there are inherent, measurable differences across recommender technologies that affect their usage rates, and specifically it finds usefulness to be a key factor. This is potentially valuable for app developers and marketeers who look to promote the adoption of novel recommender systems. The main contribution to the literature is that it presents a proof-of-concept of a two-level model for AI adoption, conceptualizing it as an effect of both variations over users and variations over applications. This finding is potentially valuable for policymakers, as better predictive models might enable improved assessments of AI's social implications. In future studies, the two-level approach presented here could be applied to other forms of AI, such as voice assistants, chatbots, or Internet of Things (IoT).

Abstract Background New disease-modifying ways to treat Parkinson’s disease (PD) may soon become a reality with intracerebral transplantation of cell products produced from human embryonic stem cells (hESCs). The aim of this study was to assess what factors influence preferences of patients with PD regarding stem-cell based therapies to treat PD in the future. Methods Patients with PD were invited to complete a web-based discrete choice experiment to assess the importance of the following attributes: (i) type of treatment, (ii) aim of treatment, (iii) available knowledge of the different types of treatments, (iv) effect on symptoms, and (v) risk for severe side effects. Latent class conditional logistic regression models were used to determine preference estimates and heterogeneity in respondents’ preferences. Results A substantial difference in respondents’ preferences was observed in three latent preference patterns (classes). “Effect on symptoms” was the most important attribute in class 1, closely followed by “type of treatment,” with medications as preferred to other treatment alternatives. Effect on symptoms was also the most important attribute in class 2, with treatment with hESCs preferred over other treatment alternatives. Likewise for class 3, that mainly focused on “type of treatment” in the decision-making. Respondents’ class membership was influenced by their experience in treatment, side effects, and advanced treatment therapy as well as religious beliefs. Conclusions Most of the respondents would accept a treatment with products emanating from hESCs, regardless of views on the moral status of embryos. Preferences of patients with PD may provide guidance in clinical decision-making regarding treatments deriving from stem cells.

Abstract Aim Early assessment of patient preferences has the potential to support shared decisions in personalized precision medicine for patients with rheumatoid arthritis (RA). The aim of this study was to assess treatment preferences of patients with RA (

Background Understanding women’s perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women’s perceptions and attitudes towards the use of AI in mammography.Method Semistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT 04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis.Results In general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice.Conclusion The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.

Objectives Over the last few decades, there have been significant improvements in the treatment of rheumatoid arthritis (RA), with the development of new treatments and guidelines for teamwork and patient self-care and access to digital tools. This study aimed to explore the experiences of individuals with RA interacting with healthcare. It also looked at how a self-care application, an educational programme called the ‘healthcare encounter’, improved patient–doctor communication.Design Semistructured interviews were conducted, and qualitative content analysis was performed.Setting The potential participants, individuals with established, or under investigation for, RA diagnosis at rheumatology clinics in Sweden, were asked to participate in the study via a digital self-care application called the Elsa Science Self-care app.Participants Ten interviews were performed with participants from nine clinics following a meeting with the rheumatologist or other healthcare personnel between September 2022 and October 2022. Phrases, sentences or paragraphs referring to experiences from healthcare meetings and opinions about the digital programme were identified and coded. Codes that reflected similar concepts were grouped; subcategories were formulated, and categories were connected to their experiences and opinions.Results Among our participants, three main categories emerged: the availability of healthcare, individual efforts to have a healthier life and personal interaction with healthcare. Participants described that the ‘healthcare encounter’ educational programme can be a source of information, which confirms, supports and creates a sense of control.Conclusion The participants valued being seen and taking part in a dialogue when they had prepared themselves (observed symptoms over time and prepared questions). The implementation of digital self-care applications might need to be incorporated into the healthcare setting, so that both the patients and the healthcare personnel have a shared understanding. Collaboration is essential in this context.

BackgroundWith new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public’s views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. ObjectiveThis study investigated public preferences for digital health data sharing. MethodsA discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes (“data collector,” “data user,” “reason for data use,” “information on data sharing and consent,” and “availability of review process”), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class’s pooled and regional data sets. ResultsA total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. ConclusionsThis study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for “data collector,” “data user,” “reason,” “type of consent,” and “review” calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Abstract Background Human embryonic stem cells (hESC) as a source for the development of advanced therapy medicinal products are considered for treatment of Parkinson’s disease (PD). Research has shown promising results and opened an avenue of great importance for patients who currently lack a disease modifying therapy. The use of hESC has given rise to moral concerns and been the focus of often heated debates on the moral status of human embryos. Approval for marketing is still pending. Objective To Investigate the perspectives and concerns of patients with PD, patients being the directly concerned stakeholders in the ethical discussion. Methods Qualitative semi-structured interviews related to this new therapy in seventeen patients from two Swedish cities. Results The participants expressed various interests related to the use of human embryos for development of medicinal therapies; however, overall, they were positive towards the use of hESC for treatment of PD. It was deemed important that the donating woman or couple made the choice to donate embryos voluntarily. Furthermore, there were concerns that the industry does not always prioritise the patient over profit; thus, transparency was seen as important.

Abstract Background Health data-driven activities have become central in diverse fields (research, AI development, wearables, etc.), and new ethical challenges have arisen with regards to privacy, integrity, and appropriateness of use. To ensure the protection of individuals’ fundamental rights and freedoms in a changing environment, including their right to the protection of personal data, we aim to identify the ethical approaches adopted by scientists during intensive data exploitation when collecting, using, or sharing peoples’ health data. Methods Twelve scientists who were collecting, using, or sharing health data in different contexts in Sweden, were interviewed. We used systematic expert interviews to access these scientists’ specialist knowledge, and analysed the interviews with thematic analysis. Phrases, sentences, or paragraphs through which ethical values and norms were expressed, were identified and coded. Codes that reflected similar concepts were grouped, subcategories were formulated, and categories were connected to traditional ethical approaches. Results Through several examples, the respondents expressed four different ethical approaches, which formed the main conceptual categories: consideration of consequences, respect for rights, procedural compliance, and being professional. Conclusions To a large extent, the scientists’ ethical approaches were consistent with ethical and legal principles. Data sharing was considered important and worth pursuing, even though it is difficult. An awareness of the complex issues involved in data sharing was reflected from different perspectives, and the respondents commonly perceived a general lack of practical procedures that would by default ensure ethical and legally compliant data collection and sharing. We suggest that it is an opportune time to move on from policy discussions to practical technological ethics-by-design solutions that integrate these principles into practice.

Illness perceptions are associated with attitudes towards preventive behaviors and are therefore crucial to consider in the context of prevention of cardiovascular diseases. We investigated illness perceptions of the public about myocardial infarction, and whether they predict public preferences for health check test results.A randomly selected sample (N = 423) of the Swedish public aged 40–70 completed an online-survey. It included the brief illness perception questionnaire, items assessing sociodemographic, lifestyle and health factors and a discrete choice experiment incorporating six attributes of health checks (written results, notification method, consultation time, waiting time, lifestyle recommendation and cost). Associations between illness perceptions and sociodemographic- and cardiovascular risk factors were analyzed using multivariate linear regression. Preference data were analyzed with a mixed multinomial logit model.Presence of smoking, hypertension, obesity and lack of physical activity were associated with weaker causal beliefs for the relevant risk factor, while presence of a high stress level was associated with stronger causal beliefs for stress. Low control predicted unwillingness to receive lifestyle recommendations. Attributing family history as the most important personal cause of MI predicted unwillingness to participate in health checks.Illness perceptions differed due to presence of risk factors, age, sex and health literacy. Furthermore, illness perceptions influenced preferences for health check test results and willingness to participate in health checks. Illness perceptions should therefore be addressed when designing health communication and preventive interventions such as health checks, and methods for promoting accurate illness perceptions should be developed.

BackgroundDigital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people’s preferences for sharing digital health data. ObjectiveThe aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts. MethodsRespondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates. ResultsIn the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents’ willingness to share health information (P

Objectives: Patient preference studies are increasingly used to inform decision-making during the medical product lifecycle but are rarely used to inform early stages of drug development. The primary aim of this study is to quantify treatment preferences of patients with neuromuscular disorders, which represent serious and debilitating conditions with limited or no treatment options available. Methods: This quantitative patient preferences study was designed as an online survey, with a cross-over design. This study will target two different diseases from the neuromuscular disorders disease group, myotonic dystrophy type 1 (DM1) and mitochondrial myopathies (MM). Despite having different physio-pathological pathways both DM1 and MM manifest in a clinically similar manner and may benefit from similar treatment options. The sample will be stratified into three subgroups: two patient groups differentiated by age of symptom onset and one caregivers group. Each subgroup will be randomly assigned to complete two of three different preference elicitation methods at two different time points: Q-methodology survey, discrete choice experiment, and best-worst scaling type 2, allowing cross-comparisons of the results across each study time within participants and within elicitation methods. Additional variables such as sociodemographic, clinical and health literacy will be collected to enable analysis of potential heterogeneity. Ethics and Dissemination: This study protocol has undergone ethical review and approval by the Newcastle University R&D Ethics Committee (Ref: 15169/2018). All participants will be invited to give electronic informed consent to take part in the study prior accessing the online survey. All electronic data will be anonymised prior analysis. This study is part of the Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle (IMI-PREFER) project, a public-private collaborative research project aiming to develop expert and evidence-based recommendations on how and when patient preferences can be assessed and used to inform medical product decision making.