Robotic-assisted surgical methods are advancing rapidly, offering potential advantages such as improved precision, accuracy, and reduced patient recovery times. However, widespread adoption depends not only on technological advancements but also on patient acceptance and willingness to consent. Informed consent plays a crucial role in the successful integration of robotic-assisted surgery into clinical practice, making it essential to understand the factors influencing patient decision-making.

This study investigates the key factors influencing patient willingness to consent to robotic-assisted total hip replacement surgery in the Netherlands, using a direct-effects model and an extended Technology Acceptance Model (eTAM). A questionnaire-based survey was conducted to assess trust, perceived usefulness (PU), perceived complexity (PC) and willingness to consent across five different surgical scenarios with varying levels of robotic involvement and surgeon presence.

Results indicated that trust and PU showed the strongest association with willingness to consent across all scenarios, emphasizing the need for healthcare providers to prioritize transparency, building trust and discussion risks and benefits. Testing the mediating effect of showed that PU did not act as a consistent mediating variable in any of the scenarios. ANOVA results revealed significant differences between surgical scenarios, with robotic-assisted surgery performed by a surgeon receiving the highest ratings in PU, trust, and willingness to consent. Remote surgery was perceived as the most complex surgical scenario. Participant preferred autonomous robotic surgery over conventional (manual) surgery, despite higher trust and willingness scores for the latter.

Participants also had varying views on the role of technicians in robotic surgery. While many saw their presence as essential for system reliability, others associated it with potential system flaws, emphasizing the need for clear communication about their role. Additionally, most participants mentioned the need for full disclosure regarding surgical methods, reinforcing the importance of patient autonomy.

These findings provide valuable insights for healthcare providers and policymakers to improve informed consent procedures and increase the adoption of robotic-assisted surgery. By addressing patient concerns through education, transparency and building trust, healthcare providers can improve patient comfort and willingness to consent to robotic (assisted) surgery.

Hospitals place patients in overflow beds when specialty departments reach capacity, maintaining patient flow but potentially compromising patient safety. This study examines how hospitals address these risks in daily decision-making. Using a qualitative, single-center approach, data were collected through observations, hospital documentation, and 12 semi-structured interviews. Inductive thematic analysis identified three key challenges: (1) balancing operational efficiency with clinical suitability, (2) interdepartmental collaboration difficulties, and (3) safety risks for outlying patients.

Findings revealed that the Patient Flow Coordinator (PFC) leads overflow placements using structured tools, but nurses’ limited specialty knowledge contributes to delays and increased length of stay. Existing mitigation strategies include repatriation, decision-making support, and signaling quality concerns.

This study contributes to healthcare management by mapping overflow placement processes and identifying systemic challenges. Recommendations include fostering shared responsibility, enhancing interdepartmental knowledge-sharing, and formalizing decision-making. Future research should evaluate the proposed relationship between decision-making, interdepartmental collaboration, and patient safety risks.

With increasing pressure on healthcare systems, hospitals frequently face bed shortages, requiring patient admissions outside clinically appropriate wards—so-called overflow beds. While efforts are made to place patients in the most suitable available wards, concerns persist regarding the potential impact of overflow beds on patient safety. Despite these concerns, safety implications are not explicitly considered in clinical decision-making during capacity shortages due to a lack of clear evidence.
This study investigated the extent to which patient exposure to overflow beds influences mortality risk. Adopting a data-analysis and statistical prediction modelling approach, hospital capacity, patient characteristics, and safety data were analysed in three phases: (1) identifying clinically relevant exposure patterns, (2) quantifying and predicting the impact on mortality using predictive models, and (3) evaluating the clinical applicability of findings for decision-making.
These findings highlighted increased mortality risks based on three risk dimensions: general exposure, location, and duration in assessing overflow bed risks. The results revealed a significant association between overflow bed exposure and increased mortality risk. Patients placed in overflow beds had a relative 65% higher odds of mortality than those not exposed. In addition, specific risks were identified for mismatched ward placements, with increased mortality risks for patients placed in overflow bed locations din different types of wards than their intended clinically appropriate ward. Short exposure durations (<34 hours) were also linked to higher odds of mortality.
To translate these insights into clinical practice, risk-based guidelines were developed based on common patient parameters at hospital admission (age, comorbidities, and place of origin). Heat maps visually indicate which patients can be relatively safely placed in overflow beds and which should be avoided, enabling hospital staff to make more informed decisions.
These findings should raise awareness among hospitals and staff of the increased mortality risks when capacity constraints require admissions outside clinically appropriate wards. It is recommended that hospitals initiate discussions across the entire organisation - medical, nursing and support staff - about the identified safety risks, and integrate the implications of the research into future policies and guidelines. This will contribute to a more risk-informed, proactive and explicit consideration of safety in hospital capacity decisions, ultimately leading to a more efficient and, above all, safer approach to managing overflow beds in times of increasing healthcare pressures.

This study investigates the potential of Self-Sovereign Identity (SSI) to empower individuals within Indonesia's health data ecosystem, particularly in the context of a Low- and Middle-Income Country (LMIC). The central concern is the loss of control that individuals face when their personal data becomes part of a broader health data ecosystem, utilized by various stakeholders like the government, healthcare facilities, and insurance companies. SSI, a decentralized and blockchain-enabled system, offers a solution by giving individuals control and ownership over their data, allowing them to decide how and to what extent their data is shared with third parties.

The research question focuses on how SSI functionalities should be designed to ensure the retention of personal data sovereignty (PDS) in an LMIC context. The study employs the Design Science Research (DSR) methodology, which includes the creation and evaluation of design artifacts to advance understanding in this domain. The research process involved developing SSI interface artifacts and testing them through a qualitative study with 15 Indonesian respondents, including individuals with socially stigmatized diseases like HIV/AIDS and TB. This population was chosen to ensure the study's inclusivity, given their heightened sensitivity to data privacy issues.

The study’s findings highlight the importance of data minimization and revocation functionalities in SSI systems. Data minimization allows users to share only the necessary information, while revocation gives them the ability to withdraw their consent for data sharing. These functionalities are crucial for fostering a sense of control, which is directly linked to a feeling of ownership over one's data. The study also revealed that a user’s digital literacy and affinity with data sharing significantly affect their perception of SSI functionalities. Users with a higher understanding of data implications tend to prefer more complex interfaces that encourage careful decision-making, whereas those with limited knowledge are more comfortable with simpler interfaces.

Additionally, the study underscores the importance of trust and contractual agreements as foundational elements in a user's willingness to participate in a data ecosystem. Without these, users may be hesitant to share their data, regardless of the control mechanisms in place.

This research contributes to the growing body of knowledge on SSI, particularly in the context of an LMIC like Indonesia. By taking a user-centric approach, the study explores the link between SSI functionalities and personal data sovereignty, providing insights that are currently underrepresented in existing literature. The inclusion of vulnerable populations further enriches the study’s findings, offering a comprehensive understanding of how SSI can enhance participation in the health data ecosystem while safeguarding personal data sovereignty.

Future research should aim to broaden the respondent pool to include more diverse socio-economic backgrounds, which would improve the generalizability of the findings. Additionally, further refinement of the SSI design artifact, informed by user feedback, is recommended to enhance its usability and effectiveness. Exploring the study's hypotheses through a quantitative approach could also provide deeper insights into the relationship between digital literacy, data sharing affinity, and personal data sovereignty.

This thesis examines the effects of safety notices on the utilization of knee implants in orthopedic practices. Utilizing a blend of correlation analysis, machine learning techniques, and interrupted time series analysis, this study evaluates the impact of safety notices on the market share of diverse knee implant models. The analysis leverages a dataset comprising safety notices, market share figures, and analytical model outputs to investigate both the direct and indirect influences of these notices on clinical decision-making processes.
Although safety notices are integral to regulatory compliance and patient safety under the Medical Device Regulation (MDR), which mandates continuous monitoring of device performance, findings indicate that these notices seldom significantly alter clinical practices or market shares. Notable exceptions include specific models such as Advance, Evolution, and Journey, which display variable impacts, suggesting the presence of other complex factors at play in clinical decision-making. These findings illustrate the complex dynamics of clinical responses to regulatory initiatives and underscore the need for more robust monitoring and evaluation strategies to determine the effectiveness of safety notices in post-market surveillance.
The thesis underscores the need to enhance post-market surveillance systems to more accurately assess the influence of safety notices on clinical practice, thereby addressing a crucial knowledge gap. By recommending directions for future research aimed at elucidating the subtle effects of safety notices, this work strives to maximize their utility and foster improved outcomes in patient care within the realm of medical devices. This research establishes a foundation for understanding the practical implications of safety notices in clinical environments, ensuring that regulatory measures are more closely aligned with clinical requirements, which will be useful for future research.