@inproceedings{MaierHaugHuberetal., author = {Maier, Johannes and Haug, Sonja and Huber, Michaela and Katzky, Uwe and Neumann, Sabine and Perret, J{\´e}r{\^o}me and Prinzen, Martin and Weber, Karsten and Wittenberg, Thomas and W{\"o}hl, Rebecca and Scorna, Ulrike and Palm, Christoph}, title = {Development of a haptic and visual assisted training simulation concept for complex bone drilling in minimally invasive hand surgery}, series = {CARS Conference, 5.10.-7.10.2017}, booktitle = {CARS Conference, 5.10.-7.10.2017}, language = {en} } @article{DotterHaugSchnelletal., author = {Dotter, Caroline and Haug, Sonja and Schnell, Rainer and Raptis, Georgios and Weber, Karsten}, title = {Sharing health data for research purposes: results of a population survey in Germany}, series = {BMC health services research}, volume = {25}, journal = {BMC health services research}, publisher = {BMC}, doi = {10.1186/s12913-025-12706-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-81622}, pages = {9}, abstract = {BACKGROUND: Increased use of health data has the potential to improve both health care and health policies. Several recent policy initiatives at the European and German legislative levels aim to increase the primary and secondary use of health data. However, little is known about general population views on health data access for research. Most studies are based on subsets defined by specific illnesses. METHODS: We commissioned a national computer-assisted dual-frame telephone survey (landline and mobile). Logit estimation models were used to identify predictors of willingness to provide access to health data to different organizations (universities in Germany, universities worldwide, German government organizations, pharmaceutical companies). RESULTS: A high willingness to share health data for research purposes is observed, depending on the specific data recipient. The willingness is highest for research at universities in Germany and German governmental organizations, and lowest regarding research by pharmaceutical companies. The main drivers for sharing health data are the level of trust in public institutions, the respondents' assessment of the seriousness and likelihood of data misuse, and the level of digital literacy. Age, gender, and level of education have small effects and do not determine the willingness to share health data for all organizations. CONCLUSION: We present evidence from a random sample of the German population. The results indicate widespread support among the population for providing access to health data for research purposes. Similar to findings in other countries, the willingness depends strongly on the recipient of the data. This paper evaluates the impact of various determinants - identified in previous qualitative and quantitative research - on the willingness of the German population to share health data. While previous studies have found that patients are generally more willing to share health data, we found that the presence of a medical precondition does not translate into respondents' unequivocal support for health data sharing. We identify privacy concerns, general trust, and digital literacy as key factors influencing the willingness to share health data. Therefore, policymakers and stakeholders need to ensure and communicate the necessary privacy protection measures to increase the willingness of the German population to share health data.}, language = {en} } @article{HaugCurrleWeber, author = {Haug, Sonja and Currle, Edda and Weber, Karsten}, title = {Acceptance of Medical History-Taking Supported by Artificial Intelligence and Chatbots: A Population-Based Survey in Germany}, series = {Healthcare}, volume = {14}, journal = {Healthcare}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/healthcare14070905}, pages = {21}, abstract = {Background/Objectives: Digital anamnesis tools, including chatbots, are increasingly being developed and evaluated, yet their implementation in German medical practices remains limited. This study examines the acceptance of medical history-taking assisted by artificial intelligence (AI) among the German population. The objective is to derive implications for integrating such systems into digitalization strategies of medical practices. Methods: This study is based on an online survey of the German population, aged between 18 and 74 years, conducted in two independent cross-sectional waves (trend design) in 2024 and 2025 with n = 1000 respondents in each year. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), three hypotheses regarding the use of AI in medical history-taking were developed and tested using linear regression models. Results: Both waves reveal a high acceptance level of AI-supported anamnesis systems for people aged between 18 and 74, regardless of whether a chatbot is used in medical practice (Scenario 1) or at home (Scenario 2). The latter received slightly less approval for the intention to use (mean intention scores: 3.50 and 3.45, range from 1.0 to 5.0) than Scenario 1 (3.59, 3.56). The indices of Performance Expectancy (PE), Effort Expectancy (EE), and perceived Social Influence (SI) determine the intention to use a chatbot with the strongest correlation of the PE index (Scenario 1: ß =0.466, Scenario 2: ß = 0.475). Most respondents (73\% and 75\%) expressed a favorable opinion for digitally storing medical history data within their electronic health record (EHR). Conclusions: The findings suggest that gender- and age- specific differentiation—aside from considering the needs of older adults—may be less relevant for designing digitalization strategies than previously assumed. Instead, the focus of medical practices should lie on the practicability of the tool used. Despite currently low EHR utilization rates in Germany, medical practices may expect broad patient approval regarding the digital storage of medical history data.}, language = {en} }