This project aimed to facilitate information support between clinicians and patients that is dynamic to the milestones in their care path and can be incrementally adapted to different chronic diseases at ErasmusMC. The project strived to envision a foundational service that informs holistically about the doubts and concerns of patient communities throughout their care journey and can be progressively incorporated into clinicians’ workflows.

Research was done to find patterns between the online patient stories from community support forums and to identify value opportunities for intervention that align with the clinicians’ aspirations, motivations and needs. The research activities included:

Desk research of relevant literature (Chapter 2).
Contextual inquiry through a combination of human interpretation of patient experience data and computational analysis (Chapter 3).
Co-creation sessions to gather information about opportunities for improving information support from a data-enabled design perspective (Chapter 4).

The data categories derived from the contextual inquiry were used to map transactional services in the online patient support groups and ideate on new transactional services for the context of remote patient monitoring. The co-creation sessions inspired a service vision and a set of guiding principles that were used to conceptualise a service system for information support, which could improve the curation of patient support knowledge resources. It was decided to focus on information support among the different types of social support due to the co-exploration of the data categories with clinicians.
Ideation on a service system enabling dynamic and incremental information support resulted in three essential modules or features of the service system:
The first module, dynamic guidance, enables Erasmus MC to use recurrent milestones in the personalised care plan of patients to standardise the provision of information resources in templates. The patient community could progressively rate the usefulness and clarity of such resources to provide recommendations to the rest of the patient community.
The second module, PX data collection, offers the efficient collection of patients’ self-reported concerns and doubts for internal system and content improvements.
The third module, community appraisal, discusses how the development and moderation of conversations among peers could not only facilitate patients’ self-evaluation and emotional support but also the periodic research of shifting or uncovered areas of concerns, experiences and doubts among the patient community.
The interconnections between these modules have been conceptualised through a service blueprint, which was presented to ML and AI researchers to refine the supporting software processes.
These service features or modules could strategically be developed and implemented within existing eHealth applications within specific departments or in a foundational self-monitoring application for ErasmusMC that is shared by different departments (e.g., surgical oncology, pulmonology).

Outcomes
Thematic categorization of patient experience data has been established, which can be used to cluster results of unsupervised topic modelling for other patient communities and compare the results. A better understanding of guiding principles to design data-enabled services and systems, which facilitate information support for patient communities, has been achieved. A service system is proposed to standardise and incrementally fine-tune resources for different patient communities. Future developments are envisioned which encompass state-of-the-art machine learning techniques and interface/service design.

This thesis, titled "Designing for the Wellbeing of Healthcare Professionals: Using AI to Reduce the Administrative Load of Healthcare Professionals & Improve Their Job Satisfaction," explores the critical issue of healthcare professional wellbeing. It delves into the factors affecting their job satisfaction and the potential of artificial intelligence (AI) to alleviate administrative burdens. This research is part of a Master's graduation project in Strategic Product Design, specializing in Medisign, at the Faculty of Industrial Design Engineering, TU Delft.

Key Findings
The study identifies three major domains impacting the wellbeing of healthcare professionals: efficiency of practice, culture of wellness, and personal resilience. Efficiency of practice is influenced by workplace systems and processes, such as electronic medical records. Culture of wellness encompasses the values, attitudes, and behaviors within an organization that promote self-care and growth. Personal resilience refers to the skills, behaviors, and attitudes contributing to an individual's physical, emotional, and social wellbeing.

The research employs a desk research methodology, followed by semi-structured interviews with healthcare professionals, to explore these domains further. The findings highlight the importance of job demands and resources, including social support, skill utilization, autonomy, and job security, in influencing healthcare professionals' wellbeing.

Conclusions
Wellbeing in healthcare professionals is a complex interplay of job demands and resources, with significant implications for patient care quality and healthcare system vitality. The study underscores the need for a supportive work environment that fosters professional fulfillment and engagement. It also points to the potential of AI in reducing administrative burdens, thereby improving job satisfaction among healthcare professionals.

Recommendations
The thesis recommends focusing on enhancing the efficiency of practice, fostering a culture of wellness, and supporting personal resilience among healthcare professionals. It suggests that strategic product design, particularly through the implementation of AI, can play a crucial role in achieving these objectives.

Impact & future work
This thesis aims to make a valuable contribution to the healthcare sector by identifying ways to improve the industry from within. It hopes to inspire designers, policymakers, and healthcare professionals to prioritize the wellbeing of healthcare workers, recognizing their essential role in delivering high-quality patient care.

This study addresses the challenges faced by remote care centers in the Netherlands, which struggle to meet increasing demands due to inefficiencies in managing unplanned care. Centralists, tasked with addressing unexpected incidents, face an imbalance of limited control and excessive, unutilized autonomy, compounded by systemic ambiguities and a lack of comprehensive data. Through a focus on occupational affective well-being, the research identifies four interconnected emotional challenges: hollow autonomy, insufficient communication, tensions between empathy and urgency, and a lack of recognition.

To resolve these issues, this strategic design paper proposes leveraging data-driven intelligent platforms, exemplified by Intelligent Syntilio, to transform care centers into proactive hubs for patient care. Key strategies include step-by-step workflow guidance and tools for identifying and managing care patterns, empowering Centralists to enhance care quality. By addressing systemic inefficiencies and fostering proactive care, the study provides a roadmap for integrating intelligent platforms into homecare systems, contributing to sustainable, client-centered healthcare and a more effective partnership between Syntilio and care centers.

This paper offers practical solutions for improving operational efficiency and supporting the evolving demands of modern healthcare systems.

This study addresses the challenges faced by remote care centers in the Netherlands, which struggle to meet increasing demands due to inefficiencies in managing unplanned care. Centralists, tasked with addressing unexpected incidents, face an imbalance of limited control and excessive, unutilized autonomy, compounded by systemic ambiguities and a lack of comprehensive data. Through a focus on occupational affective well-being, the research identifies four interconnected emotional challenges: hollow autonomy, insufficient communication, tensions between empathy and urgency, and a lack of recognition.

To resolve these issues, this strategic design paper proposes leveraging data-driven intelligent platforms, exemplified by Intelligent Syntilio, to transform care centers into proactive hubs for patient care. Key strategies include step-by-step workflow guidance and tools for identifying and managing care patterns, empowering Centralists to enhance care quality. By addressing systemic inefficiencies and fostering proactive care, the study provides a roadmap for integrating intelligent platforms into homecare systems, contributing to sustainable, client-centered healthcare and a more effective partnership between Syntilio and care centers.

This paper offers practical solutions for improving operational efficiency and supporting the evolving demands of modern healthcare systems.

Despite the low adoption rates of artificial intelligence (AI) in respiratory medicine, its potential to improve patient outcomes is substantial. To facilitate the integration of AI systems into the clinical setting, it is essential to prioritise the development of explainable AI (XAI) solutions that improve the understanding of the AI predictions. These XAI solutions empower clinicians to collaborate effectively with AI systems, thereby enhancing the overall outcomes for patients in respiratory medicine. Unfortunately, the lack of user-centric studies in this domain has made it challenging to identify the specific aspects of explainability that are most effective in improving the adoption of AI in the real-world environment. To address this gap, we conducted a mixed-methods study of clinicians in respiratory medicine to identify the most relevant and crucial aspects of XAI solutions. Our study focused on understanding how XAI can be effectively translated into clinical practice by leveraging the expertise of doctors in the field. Because of the lack of knowledge about XAI concepts among pulmonologists a different approach is taken to regular user-centric XAI research and no direct examples of state-of-the-art XAI solutions are used. Rather the expertise of doctors is used to make them implicitly identify their needs and intents. Our findings reveal that the successful adoption of XAI solutions in respiratory medicine requires tailored solutions that address communication barriers, promote patient-centric care, and overcome AI adoption challenges. The study highlights the significance of task-specific visualisations, comprehensive explanations, preferred granularity, and the ability to mimic human judgement in successful XAI solutions. Trust and collaboration between clinicians and AI systems are essential for effective adoption, wherein AI is perceived as a colleague rather than a replacement. This ensures that clinicians can easily understand and work with the model predictions, ultimately leading to improved patient outcomes. By aligning XAI design with the needs and intents of pulmonologists, we established the importance of Co-designing solutions with domain experts and embedding XAI within clinical workflows emerged as key strategies. Our research underscores the imperative of transparency, extended validation, and continuous alignment of AI technologies with medical values. By following these principles, XAI solutions can be developed to enhance the diagnosis and treatment of respiratory illnesses, ultimately improving patient outcomes in respiratory medicine.

Pulmonary Fibrosis (PF) is a rare and incurable interstitial lung disease, which causes lung scarring, leading to an irreversible decline in patients’ lung function and breathing difficulties. In Europe, Pulmonary fibrosis (PF) affects over 400,000 people, and it claims the lives of about 100,000 people each year (EU-IPFF, 2023). In light of the prevailing medical personnel deficit and the stress on healthcare systems, a collaborative synergy between healthcare practitioners and patients becomes indispensable to facilitate the transition of healthcare services from hospital settings to domiciliary environments and to tailor individualized home care.
This endeavor is spearheaded by Erasmus MC, a preeminent specialist center for PF in the Netherlands. In collaboration with the Convergence program and TU Delft, this initiative embarks on exploring the PF patient journey map while develop innovative design solutions. These solutions are aimed at empowering patients to engage in self-management and to enhance their Health-Related Quality of Life (HRQoL).
The overarching methodology of this project adheres to the Enhanced Data-enabled Design (EDED) approach by Jung (2023), which leverages data collected within the community, user, and design contexts and adopts an iterative design process. Firstly, getting inspired by the online patient community data which contains more than 40,000 of patient stories, the initial problem areas that patients have most struggles are defined.
Subsequently, the insights gained in the community context are further examined in the user context. First, the preliminary patient journey map was validated and refined, and the initial problem areas were scoped down through co-creation and interviews with Healthcare Professionals (HP). Secondly, a user behavior study was conducted to uncover PF patients needs in their self-management process by reusing community data and leveraging the Information-Motivation-Behavioral Skills framework. The result of this study addresses the problem of this project, as the primary needs of PF patients to achieve effective and coherent self-management behavior are to acquire information related to five main aspects:
- Understanding and management of medication;
- Understanding and management of oxygen therapy
- Understanding Treatment and medication options
- Lifestyle adjusting to adapt to changes in life
- Symptom monitoring and management
Therefore, the final design goal of this project is defined as: “ How to provide PF patients with required, reliable, and understandable information to support their self-management behavior in their post-diagnosis stage.”
Accordingly, the user needs in information and the design goal collectively establish the foundation design concept: Vomo, a Product-service systems (PSS) to facilitate PF patient self-management in the post-diagnosis stage. The delivery of Vomo is facilitated through an application that is accessible to its end users. The present PSS has undergone a redesign process, drawing inspiration from the preexisting framework of a PSS known as Erasmus MC's IPF-Online. This project presents a comprehensive analysis of the rationale for the redesign of certain functions and proposes strategies for the future implementation of these revised functions inside the existing product.

Sarcoïdosis is a complex and rare disease, which can affect any part of the body. The disease has symptoms, side effects and treatment plans varying per patient. In the Netherlands, yearly up to 7000 to 8000 people are diagnosed with sarcoidosis, in an acute or chronic form (Sarcoïdose - UMC Utrecht, n.d.). In the Netherlands, there are three sarcoïdosis specialist centres, and Erasmus MC is one of them. The Interstitial Lung Disease department of the Erasmus MC is trying to provide the right care for sarcoïdosis patients. However, together with the Convergence Program, a collaboration between the Delft University of Technology and the Erasmus MC, they analyse how the care at the hospital can be optimised. However, for patient care at home, there is still room for improvement, as expressed by medical experts. In this project, the methods and results lead to a patient journey map that indirectly improves the sarcoïdosis patient journey. The method of Patient Community Journey Mapping from Jung (2023) sets the base for the analysis. This is a data-driven approach to utilising online patient stories for machine learning. Together with a data scientist from Erasmus University, posts of wijhebbensarcoidose.nl are analysed and converged into a patient journey map. Topic listing is applied to the database of 7500 posts, followed by quantification of popular topics, and a sentiment analysis to show the urgency of a topic. The conducted patient journey map consists of 1) a medical journey path and 2) the topics derived from the computational analysis. A co-creation session with pulmonologists and specialist nurses was done to validate the resulting patient journey map. Followed by an interview with a patient to validate this map. These sessions were organised to get an even more realistic patient journey map. Sarcoïdosis is a complex disease, and often multiple topics are discussed in one post. Therefore, an analysis is done to see the co-occurrence between topics. From this resulted that the key value is formed by everything related to finding a balance in life with sarcoidosis. The patient journey map emphasised multiple needs that are mentioned negatively, according to the sentiment analysis. This led to three important patient needs to take into account for this project. Together with the key value, the main four patient needs are the following:

1. Help patients with finding a balance in life after their diagnosis.
2. Give patients a clear overview of their medication intake.
3. Monitor the patients’ quality of life sarcoidosis aspects.
4. Provide patients with information about sarcoidosis-related topics.

These formed the base of the designed system in the form of an application:
Sarcoïdosis voor jou. This application has four main pages where the focus on each page is a different need. The self-management tool can be created with the internal company Digitaal Verbonden. Together with the ILD department, the application can be created with some additional research steps. This project can argue why certain functions should be implemented in the general Digitaal Verbonden application for Erasmus MC patients.

In common Dutch cardiac care, patients only have few follow-up meetings with their cardiologists after they have been treated at the hospital. Sometimes, they have to wait several months for their next visit. Therefore, patients often turn to online platforms where they can ask questions to other patients or healthcare professionals, namely health-based social networks. These platforms are managed by a small group of volunteers, cardiologists, who seek to inform patients in order to make efficient use of the limited time at the hospital. However, it is expected that the number of cardiac patients on these networks will increase due to overall growth of the number of Dutch cardiac patients and the recent trend of searching health information on the internet. Hence, it remains the question whether health-based social networks can scale up with this development. To investigate the sustainability of health-based social networks, user posts from the Dutch social platform Hart Volgers and American social platform DailyStrength were analysed. The results show that the number of patients on these platforms grows rapidly, and they reveal that users prefer to contact cardiologists instead of fellow patients. These findings suggest that there is an urgent need for new solutions that keep these platforms future-proof. To address this problem, Harthulp is proposed, a smart question-and-answer platform for cardiac patients to provide better and more efficient aftercare. Harthulp introduces a question wizard that enables patients to quickly find information on the platform, so that it is not always necessary to ask a question to a healthcare professional and to wait for a reply. As a core component of the question wizard, a novel search engine has been developed which employs a deep learning model that captures the semantics of words on health-based social networks. In this way, patients can search with short questions and retrieve relevant posts while they may not contain the exact same words. It has been demonstrated that the proposed search engine significantly outperforms traditional search engines when retrieving relevant question-and-answer posts from Hart Volgers. A web interface has been designed to show how all components can be embedded in a single user-centered design. This design has been evaluated together with an experienced cardiologist.