Colorectal cancer is the third most common cancer disease worldwide with highly fatal. It has accounted for 10% of the global cancer incidence (Bray et al., 2018). In the Netherlands, patients who have undergone treatment and surgery for colorectal cancer typically enter a five-year follow-up phase. During this period, patients often experience long-term health issues. Colorectal cancer not only has physical implications for patients but also significantly affects various aspects of their daily lives, including their social networks and mental health (Arndt et al., 2006). On the other hand, remote patient monitoring (RPM) can enhance efficiency and improve patient care delivery. However, there has been a noticeable lack of research and design focusing on the inclusion of social and mental health monitoring within these systems. According to the World Health Organization, health should include “Physical, Social and Mental” aspects. Therefore it’s important to focus on how to develop remote patient monitoring in these aspects. The project first used a patient community journey map (Jung, 2023) to summarize the experiences of colorectal cancer patients from a large number of posts on an online communication platform and invited medical experts from the Netherlands to a co-creation session to validate the map and explore research directions. The final research question was defined as: How to develop a remote patient monitoring system for patients’ social and daily activity and related emotion when they are away from the hospital? The research question was followed by user research. Patients received diary studies, including finishing a probe based on their activities. Upon completion of the diary studies by the patients, there is a follow-up interview to obtain patients' feedback and sentiments regarding the probe. Subsequently, the insights from the user research act as input during a collaborative designers’ brainstorm session. Finally, these research findings were exported into the initial guidelines for developing the RPM in social, daily activity and related emotions for colorectal cancer patients. After evaluations, the final guidelines for colorectal cancer patients’ RPM and a toolkit summarizing the "develop" and "deliver" phases were presented. This toolkit allows future researchers to develop RPM in social and daily activity and related emotion for patients in other contexts besides colorectal cancer patients, supporting the remote patient monitoring system’s evolution of physical, social and mental health.

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.

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.

There are various organisations such as the European Society of Cardiology (2012) and American Heart Association (2013), which describe why physical activity is essential for the development in youth. Unfortunately, children who have a Congenital Heart Defect (CHD), may suffer from a lack of opportunity to perform physical activity, decreasing cognitive maturation, motor development and autonomy during childhood (Krol, 2003). This impediment arises due to a misunderstanding from parents, who do not know to what extent their child can exercise safely, and therefore adopt overprotective behaviours (Schwerzmann, Thomet, & Moons, 2016).In order to understand better overprotection during childhood, 305 online parental stories from various patient-association websites were analysed using Natural-Language-Processing techniques. The results exhibited the lifetime journey of these families, where an uncertain future evocated a constant search for symptoms. The findings of this phase were employed during generative interviews with seven families with a CHD paediatric patient to understand the continuous search for symptoms during exercise. The combination of the insights gathered from interviews and the lifetime journey was presented to five medical team members to inspire a co-creation session. The outcome of the meeting helped to define the problem and create a design vision which helped to ideate a potential Product-Service-System (PSS) solution to support parents to let their children safely perform physical activities. To encourage families to have a safe, ordinary sports-life, BO is introduced, a smart PSS aiming to support parents and their children with a CHD to understand better the safety boundaries of exercise during free-living conditions. With an activity tracker and his nine system modules, Bo aims to guide the child through different heart rate zones defined by doctors. Furthermore, Bo has a conversational agent function where parents can send concerns to the medical team and find relief when seeing their child’s heart rate zone.A functional prototype of the conversational agent was developed and implemented in the real context of four families to understand how could it influence overprotection. The implementation experience and overall concept of Bo were evaluated through in-depth interviews with paediatric CHD patients and their parents and three different specialities from the medical team. The results showed that Bo provides a supportive exploratory environment for the family, where the child can self-discover the safety boundaries and parents, instead of limiting the child, adopt an encouraging attitude towards physical activity.

In this graduation thesis I investigate how to design for patient empowerment in the context of head and neck cancer (HNC) patients in their follow-up phase (the first 5 years after treatment). This project is set up with the Erasmus MC and was initiated to elaborate on their current service called the Healthcare Monitor (HM). The HM is a structure of electronic patient-reported outcome measures filled in by patients before every follow-up consultation. The answers are shown in a dashboard to the treating physician and used during patient consultations (Dronkers et al., 2020). What the current HM lacks however, is insight for the patients themselves into their health and addressing their concerns in between the consultations. Therefore this project aims to develop an additional service to the HM that empowers patients in between consults whilst also providing the doctor with a more frequent and realistic insight into their wellbeing. Patient empowerment can ultimately lead to a better quality of life. In this project it is viewed as a process that can be influenced through design. Therefore an inspirational model is created in this thesis that designers can use when designing for patient empowerment. HNC patients in their follow-up need to deal with a lot of different physical and psychosocial side effects (Aguilar et al., 2017). I investigate their concerns using the double loop method of Jung (2021). I analyze the community context through a computational analysis of online cancer community platforms. The patient context is investigated through a focus group with 6 care professionals and interviews with 9 ex-patients. 4 main concerns are found. Patients want to: •Be prepared for what they can expect and do •Have the overview of how they are doing •Be supported when they have a doubt •Have a relevant service for them The model for patient empowerment and patient concerns are combined to create a design vision. The vision is to provide patients with an overview that enables them to gain more control in dealing with issues that concern them. This overview was designed in a prototype application through 3 main elements; a logbook with previous experiences, a possibility to track current experiences and an overview of expected experiences based on peer experiences. This prototype was tested by 6 ex-patients who found value in the low threshold way to contact others, find trustworthy information and track their wellbeing. They valued peer experience exchange and better insight into themselves as this can ultimately lead to more reassurance in between consults. Based on this test further improvements, suggestions for the next steps and possibilities to scale the concept are presented. When developing an app for patient empowerment the Erasmus MC should; •Include peer patient experiences •Provide the patient with insight into their wellbeing over time •Integrate tools and databases in one complete overview For designing in the field of value based healthcare, this project stresses the importance of incorporating experiences of the community and using a multidisciplinary approach.

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.

Summarising patient interactions creates a huge workload for the healthcare professionals. This research finds that patient interactions contain a lot of noise that is subjective of nature. To explore the problem area interviews with a summarisation prototype have been conducted to extract system requirements and validate those by implementing them in the prototype. Filtering noise, reinforcement learning and numerical factual correctness are of paramount importance to a successful summarisation system.

Colorectal cancer is a widespread disease that significantly impacts the health of individuals worldwide. Understanding the needs and concerns of those affected by this disease is crucial for improving patient outcomes and enhancing the quality of care. Patient web forums have emerged as valuable platforms for individuals to openly share their experiences and thoughts related to colorectal cancer, providing unique insights into the social, physical and emotional aspects of their patient journey. These forums offer a more comprehensive and authentic portrayal of patient experiences compared to traditional patient data collection methods, such as questionnaires and interviews, which may not capture the full scope of patients experiences in the colorectal cancer carepath. However, analyzing the vast amount of unstructured data within these patient web forums presents a significant challenge. Traditional manual analysis by human experts is time-consuming, labor-intensive, and limited in scalability, making it impractical to analyze the sheer volume of patient-generated content. This is where the application of natural language processing (NLP) techniques becomes crucial. NLP enables the automated processing and analysis of textual data, allowing for efficient extraction and interpretation of the large amounts of patient forum posts. Nevertheless, relying solely on machine intelligence, such as topic modeling and natural language generation, for interpreting patient forum data carries inherent risks, including the potential for disseminating misleading information. While these machine-driven techniques offer efficient and scalable ways to analyze and generate insights from the large amount of diverse and unstructured patient forums, they may lack the necessary contextual understanding and domain expertise to ensure the accuracy, relevance, and ethical implications for interpreting colorectal cancer patient experiences. To close this gap between human experts and machine intelligence, this thesis explores the potential of hybrid intelligence (HI) for topic interpretation in colorectal cancer research. The main research question is: ``How can topic modeling, GPT-3.5 language generation and human expertise be combined to explore the interpretation of patient web forums in colorectal cancer (CRC) research?" To address the research question, three human studies were conducted. The first study employed NMF topic modeling to compare topic interpretations created independently by medical workers and GPT-3.5. This comparative analysis discovered unique observations that differentiate human-written and AI-generated interpretations on online patient stories. In the second study, it was investigated how medical researchers collaborate with GPT-3.5 to develop hybrid interpretations on patient experience topics generated by the BERTopic model. A Flask web application served as the interactive platform for combining their knowledge with the AI model. Finally, the third study made professional human evaluators assess the topic relevance of the interpretations generated by medical researchers and GPT-3.5 to determine whether the combination of GPT-3.5 and human expertise leads to improved topic interpretations compared to individual interpretations. The proposed solution to the research problem is to explore a hybrid workflow that compares, combines and validates GPT-3.5 language generation and human expertise, aiming for enhanced interpretations of topics extracted from colorectal cancer patient forums. The three studies provide opportunities for researchers and medical professionals to integrate machine intelligence from topic models and GPT-3.5 in their field of work. The hybrid workflow has conclusively demonstrated that human experts were successfully able to compare and enhance the relevance of human and GPT-3.5 interpretations of colorectal cancer patient experience topics. This allowed human experts to efficiently reach a more comprehensive understanding of patient forum data, which is essential for improving patient health in colorectal cancer research.

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.

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.

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.