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Summary Breast cancer is one of the most common types of cancer among women. National mammography screening programs can detect breast cancer early, but attendance rates have been decreasing in the Netherlands over the past decade. Non-attendees reported that overdiagnosis, the risk of false-negative results, x-ray exposure and mammography pain could be barriers to attendance, but it is not clear whether these disadvantages explain non-attendance and in which situations they are considered barriers. We conducted a national survey among 1227 Dutch women who did not attend mammography screening appointments in 2016. Logistic regression models were used to identify factors that influenced the likelihood of the abovementioned disadvantages leading to non-attendance. The results showed that the doctor’s opinion increased the likelihood of the risk of false-negative being perceived as a reason for non-attendance. Moreover, opportunistic screening increased the likelihood that the risk of false-negative, overdiagnosis and x-ray exposure would lead to non-attendance. Women with lower education levels were less likely to consider overdiagnosis and x-ray exposure as reasons for non-attendance, while women who had not undergone mammography screening before were more likely to reject the screening invitation because of concerns about x-ray exposure and mammography pain. These findings indicate how we can address the specific concerns of different groups of women in the Netherlands to encourage them to attend potentially life-saving breast-screening appointments. Screening organizations could provide accurate and unbiased information on the effectiveness of mammography screening to GPs, putting them in a better position to advise their patients.

Background The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

Selection and peer-review under responsibility of the scientific committee of the 11th Int. Conf. on Applied Energy (ICAE2019).