The Dutch healthcare system currently operates under intensifying fiscal and workforce pressures, transforming nurse scheduling from a simple operational task into a complex strategic conflict involving divergent stakeholder priorities. Addressing this "wicked problem," this study addresses nurse scheduling as a strategic decision challenge, introducing a novel methodological framework that bridges Operations Research and Game Theory. Unlike traditional optimization models, this research introduces the "Nurse Scheduling Game" (NSG), formulating the problem as an Integer Programming Game (IPG) to capture the behaviors and interactions of stakeholders explicitly.

The interaction is analyzed through two distinct equilibrium concepts representing different governance structures. First, the uncoordinated state is modeled as a simultaneous Nash Equilibrium, formulated as a Generalized Nash Equilibrium Problem (GNEP). Second, the potential for strategic improvement is explored through the hierarchical Stackelberg Equilibrium, formulated as a Bilevel Integer Problem (BIP). In the latter, the Hospital Manager (Leader) explicitly anticipates the Nurses' (Followers) reactions. To solve this computationally intractable bilevel problem, the study implements a novel Monte Carlo Multilevel Optimization (MCMO) framework.

Applied to a representative case study of a mid-sized Dutch hospital, the computational results quantify the significant costs associated with uncoordinated planning. Under Nash dynamics, the system converges to a state of "defensive buffering," resulting in outcomes approximately twice as expensive as the coordinated alternative. Conversely, the Stackelberg Equilibrium demonstrates the value of strategic anticipation. By transitioning from volume-based to precision-based allocation, the hierarchical model achieved a 51.0% reduction in total system costs and an 11.8% reduction in patient waiting times compared to the Nash baseline.

These findings translate into actionable policy implications, suggesting that the solution to budget overruns lies in shifting from reactive to anticipatory governance. The study supports the implementation of Algorithmic Workforce Management systems that couple budget setting with schedule design. Ultimately, this research offers a unified game-theoretic optimization framework that reconciles financial constraints with workforce autonomy, providing a viable pathway toward sustainability for the Dutch healthcare system. ... Read more

Healthcare systems in the Netherlands and other Western countries are under increasing strain due to growing demand for care and a shrinking workforce. A critical challenge is understanding why nurses choose to remain in the profession, particularly in high-pressure settings such as hospital emergency departments. This thesis investigates how a systems science approach can contribute to understanding nurses’ workload and their intention to stay in the profession, using a case study of the Emergency Department at Erasmus MC.

Drawing on System Dynamics modeling and participatory Group Model Building with nurses, the study explores the complex relationships among perceived workload, job satisfaction, stress, and team dynamics. The research combines literature review, participatory modeling sessions, and simulation using Exploratory Modeling and Analysis to identify the conditions under which nurses are most likely to remain in the profession.

Findings show that perceived workload has a stronger influence on the intention to stay than objective measures such as patient-to-nurse ratios. A key insight is the concept of “shift balance,” which captures whether nurses experience more positive than negative shifts over time. This metric proved more meaningful than traditional workload indicators. The study also highlights the importance of eustress, a motivating form of stress, and the need to distinguish it from other stress types.

By applying systems science tools to a complex workforce issue, the thesis offers practical guidance for designing interventions that support nurses' long-term commitment to their profession. It has deepened the understanding of stress and workload dynamics around the nurses' intention to stay in the profession. ... Read more

The Netherlands faces significant capacity challenges in healthcare due to staff shortages. Many healthcare positions remain unfilled, despite rising demand for medical services. This also impacts the Emergency Department (ED), as higher patient volumes lead to longer wait times and increased responsibilities for emergency physicians. The combination of these variables leads to an increase in personnel turnover, putting additional strain on capacity. These capacity difficulties may also be seen in Deventer Hospital's ED, highlighting the significance of effectively organising patient flows and addressing system dynamics.

Although Deventer Hospital has conducted analyses to improve capacity management, current approaches, both locally and in the broader literature, primarily focus on technical aspects, such as staffing planning within the ED. However, by focusing solely on technical factors, such analyses may fail to account for social factors that influence system performance, such as human behaviour, communication patterns, decision-making processes, and institutional constraints. To bridge this gap, this study adopts a socio-technical systems approach that incorporates both technical and social factors of capacity management in the ED.

The main question is:
How can Emergency Department capacity challenges be improved using a social-technical approach?

To answer this question, the current system at Deventer Hospital's ED was examined through a socio-technical systems framework. The IDEF0 model was used to map both the technical and social factors influencing capacity management. This model systematically represents workflow processes, resource dependencies, regulatory constraints, and the stakeholders involved. The system mapping was informed by multi-day observations, open interviews with ED physicians, document analysis, and a stakeholder analysis. The stakeholder analysis provided deeper insights into communication patterns, knowledge distribution, and power-interest relationships among the actors. Together, these methods resulted in a comprehensive and structured representation of the ED.

The system analysis revealed that ED capacity challenges involve both technical and social dynamics. These were categorized across three phases: inflow, throughput, and outflow. Specific issues included variability in triage processes, workforce shortages, delays in diagnostics, and asymmetric information exchange between departments. Furthermore, ED physicians noted that a proportion of patients could potentially have been treated elsewhere, affecting resource allocation and patient flow.

Based on these insights, potential interventions were developed to optimize patient redirection within the socio-technical landscape. A snapshot analysis estimated the proportion of patients who, according to ED physicians, could have been treated in alternative settings, such as by general practitioners or outpatient clinics. This analysis was complemented by interviews with ambulance personnel, GPs, and medical specialists, which provided deeper insight into barriers and opportunities for patient redistribution.

The findings indicate that while technical opportunities exist, particularly in redirecting patients to urgent outpatient clinics, success is dependent on effective capacity planning and accessibility in alternative care settings. Social factors play an equally critical role. Efficient patient diversion requires a robust communication framework among general practitioners, ambulance services, and specialists. In practice, however, such communication is not always optimal.

Additionally, a growing claims culture increasingly influences referral behavior. Concerns regarding legal liability and potential complaints compel healthcare providers to refer patients to the ED more frequently as a precaution, even when alternative care options could be more appropriate. This defensive referral behavior exacerbates ED workload and reflects a broader trend of risk aversion in healthcare decision-making.

To address these issues, both technical and social interventions are proposed. Technically, expanding urgent outpatient clinic capacity could offer potential, provided that accessibility and appointment scheduling are carefully managed. Socially, interventions such as structured feedback systems between EDs and referring providers, enhanced real-time communication channels, and targeted legal literacy training for healthcare professionals could reduce unnecessary referrals.

In conclusion, the socio-technical system analysis provided a more comprehensive understanding of ED capacity management by considering both technical and social dimensions. This approach revealed important dynamics that a purely technical analysis might have overlooked, such as communication gaps, coordination barriers, and organizational constraints. It led to the development of potential interventions that not only address logistical aspects of patient redirection but also promote social structural improvements within the healthcare system. By targeting both technical workflows and social coordination mechanisms, this socio-technical approach offers a more realistic pathway to optimizing Emergency Department capacity. ... Read more

Background Emergency departments (ED) are under increasing pressure, a phenomenon often linked to the rising complexity of care rather than patient volume alone. This complexity is frequently driven by multimorbidity – the presence of two or more chronic conditions – which necessitates patient-centred, interdisciplinary decision-making. However, traditional hospital structures remain organised in a monodisciplinary, organ-specific manner, leading to fragmentation and coordination diliculties. This misalignment creates a socio-technical environment where patients with complex or undilerentiated complaints risk falling between departments.

Objectives The objective of this study was twofold. First, to describe how interdisciplinary decision-making regarding treatment and admission occurs in practice for patients with multimorbidity in the ED. Second, to identify and explain the factors that shape this decisionmaking process within the socio-technical system of the hospital.

Methods A single-case study design based on Yin’s methodology was employed at the Erasmus Medical Centre (EMC) ED. The study was divided into two phases. The first phase utilised a holistic, descriptive approach grounded in Safety-II principles to map the "Work-as-Imagined" (WAI), derived from grey literature and protocols, against the "Work-as-Done" (WAD), observed in practice. The second phase used an embedded, explanatory approach to analyse subunits of analysis – individual professional behaviours and information flows – based on theoretical propositions regarding the Health Information System (HIS). Data collection involved 19 days of observations, ten semi-structured interviews with medical specialists, and a tabletop session with stakeholders.

Results The comparison of WAI and WAD revealed significant discrepancies; while protocols assume clear assignment of a "lead practitioner," practice is characterised by ambiguity and a "consultation carrousel" where multiple specialisms are consulted to find a problem owner. The study identified that defensive behaviours among medical specialists are prevalent, particularly when patients present with "discordant multimorbidity" (conflicting treatment goals) or "concordant multimorbidity" (undilerentiated complaints without a clear organ-specific owner). Superspecialists often utilise an "opt-in" strategy, accepting only patients fitting narrow diagnostic criteria, electively forcing generalist departments to "opt-out" or become the default safety net. Furthermore, technological systems (EHR) were found to be passive and fragmented, failing to adequately support the identification of responsibility or facilitate rapid communication.

Discussion The "ping pong" phenomenon is not merely a diagnostic dilemma but a symptom of deeper systemic issues. Hyperspecialisation has eroded generalist competencies, fostering a culture of defensive medicine where specialists avoid complex cases to mitigate risk. Additionally, external factors heavily influence ED decision-making; hospital budgeting prioritises elective care, incentivising departments to protect bed capacity and creating a "push" system where the ED struggles to admit patients to wards. To ameliorate these issues, the study recommends a cultural shift towards shared responsibility, the implementation of ‘active’ technological notifications and improved reachability to support interdisciplinary decisionmaking, and the formalisation of joint bedside decision-making to establish a ‘common ground’ for complex cases. ... Read more

The aim of this research is to gain an understanding of the balance between guideline standardisation and professional autonomy. This includes its impact on the safety and quality of healthcare. These are crucial aspects of providing good care as they can contribute to an overall healthcare system that is effective, reliable, and patient-centered. The reason for performing this research is the difficulty of finding a balance between guideline standardisation and professional autonomy. While guideline standardisation is crucial to ensure a structured approach to uniform care of high quality, professional autonomy is necessary to individualise this care for each patient. At times, the tension between these aspects arises as medical practitioners feel limited in their professional autonomy, but healthcare organisations require a structural set-up of healthcare systems to provide equal care.

By performing interviews with guideline developers, doctors, and patients, this tension and possible contributing factors were explored through a technical, human, and organisational perspective. The identification of a need for the inclusion of the psychosocial elements, research regarding sensitivity around professional autonomy, and structural changes is beneficial to get to a point where it is possible to identify a balance between guideline standardisation and professional autonomy and its impact on the safety and quality of care. While multiple points for further research are indicated in this study, these three factors contribute to improving the situation of both the tension itself and the discussions around it.
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Standardisation plays a crucial role in driving long-term productivity, competitive growth, and innovation in various industries. Its primary objective is to streamline processes by establishing consensus among different stakeholders, such as firms, users, academia and governments. Standards provide classifications such as quality, compatibility, safety, or measurements to ensure that products and procedures meet specific requirements and can be reliably used across different domains. In spite of the importance of standardisation, there is a lot to learn about the factors that influence standard success. As of now, literature in this field is mostly exploratory in nature. This study investigates the factors influencing the success of standardisation efforts, in particular, it examines the impact of technological diversity and geographical diversity within standard setting organisations (SSOs) or standards consortia on achieving standard success. Additionally, the study explores the role of the size of these SSOs or standards consortia in influencing the likelihood of standard success. Next to that, the influence of the definition of standard success is also examined, by providing three different theoretical definitions for standard success. This study adds on to the literature by providing larger scale qualitative research in an attempt to better understand the dynamics of standard success. In this study an original dataset was created of standards consortia in the context of Renewable Energy (RE) and Sustainability, Efficiency, and Green Initiatives (SEG). The chosen context is significant for addressing the global climate problem, a complex and multifaceted wicked problem that requires collaborative efforts. Therefore, the understanding of which factors influence the success of standards is essential to tackle climate change's adverse impacts. Standards are important in providing universal guidelines, products and protocols that are needed to combat climate change. The results indicate the presence of a U-shaped relationship between technological diversity and standards success, this result has not yet been found earlier and implies that instead of an optimum range of diversity, managers should to be at either extreme end of the technological diversity spectrum. Moreover, we observe a positive relationship between consortium size and standard success, meaning that whenever possible, managers should aim to increase consortium size. This study provides a better understanding of how the characteristics of standards consortia influence standard success which can also aid managers in making well-educated choices when setting up standards consortia. ... Read more

The first registered infection with the COVID-19 virus occurred in China at the end of 2019. The virus spread worldwide, turning a single infection into a pandemic. Most countries implemented policies to limit its spread. These policies can be divided into pharmaceutical and non-pharmaceutical interventions. Vaccines are pharmaceutical interventions, while non-pharmaceutical interventions include the temporary closure of restaurants and bars or limits to the number of people allowed to gather for social events.

Although these non-pharmaceutical interventions were effective in slowing the spread of COVID-19, they also severely affected social and economic life. Freelancers working in restaurants and bars lost their income, and students experienced a decrease in mental health due to reduced social engagement. These societal impacts gradually reduced public support and adherence to COVID-19 measures across different pandemic waves.

To measure how people weigh these societal impacts, Discrete Choice Experiments (DCEs) are used. These are often analyzed with Multinomial Logit (MNL) models, which quantify the relative importance of attributes for the population as a whole. However, alternative models exist, such as Mixed Logit (ML) and Latent Class (LC) models, as well as different DCE designs, such as labeled and unlabeled DCEs. Each approach has advantages and disadvantages. ML models capture preference heterogeneity across individuals, while LC models identify groups with distinct preferences. Labeled DCEs explicitly include COVID-19 measures, whereas unlabeled DCEs focus only on societal impacts without reference to the measures causing them.

As the pandemic transitioned into an endemic in late 2022 and early 2023, this study evaluates the advantages and disadvantages of these models and DCE designs in both contexts. The main research question is: What are the (dis)advantages of using ML and LC models over MNL models to analyze (un)labeled DCEs that weigh societal impacts of COVID-19 policy during the pandemic and endemic?

Three subquestions are addressed through a literature review, an empirical DCE conducted during the endemic, and expert interviews. The literature review compares results from MNL, ML and LC models in labeled and unlabeled DCEs during pandemic waves. The empirical study applies both labeled and unlabeled DCEs during the endemic to compare model outcomes. Expert interviews are used to assess methodological advantages and limitations.

The results show that ML models are useful for detecting preference heterogeneity, which helps assess the reliability of MNL estimates. If heterogeneity is substantial, MNL results may not adequately represent the sample. The LC model identifies subgroups in society with distinct preferences. However, it is sensitive to initial values and covariate specifications. ML models are computationally demanding.

For DCE design, labeled experiments are useful for assessing the impact of specific COVID-19 measures, while unlabeled experiments provide baseline preferences for societal impacts without contextual framing. Unlabeled DCEs are most suitable at the start of a pandemic to inform initial policy design. Labeled DCEs are more appropriate during and between pandemic waves to evaluate and adjust policies, and during the endemic to assess long-term impacts.

ML models are valuable for testing the robustness of MNL estimates, while LC models help explain heterogeneity and support tailored policy design to improve public acceptance and adherence.

A key limitation is the limited availability of comparable labeled and unlabeled DCEs across pandemic phases and countries, as well as differences in attributes and timing, which reduce comparability. Future research should explore extended choice models, such as mixed or latent class models with random parameters, as well as data-driven approaches outside traditional choice modeling. Further research is also needed on how these insights are integrated into pandemic policy decision-making and whether they should serve as advisory input or formal guidance. ... Read more

The Dutch healthcare system is facing increasing pressures on accessibility, quality, and affordability, making it unsustainable in its current form. This is particularly evident in cancer care, where rising incidence and treatment costs threaten timely and effective care. To maintain sustainable cancer care, innovations proven to improve outcomes must be quickly adopted. AI-driven Clinical Decision Support Systems (AI-CDSS) are often cited as promising tools to support adequate, patient-centered cancer care, reducing over-treatment and enhancing quality of life. Despite extensive research on AI-CDSS development, adoption and implementation remain limited, especially in the Netherlands. This thesis investigates why adoption of AI-CDSS in cancer care fails, using Mammaprint, a molecular diagnostic AI-CDSS for breast cancer, as a case study.

The research finds that the primary barrier to adoption is an institutional void in reimbursement by basic health insurance. Institutional voids refer to the absence or inadequacy of supportive structures, regulations, and frameworks. In the case of Mammaprint, the reimbursement process relies on the “state of science and practice” (SWP) criterion, which assesses whether clinical utility—demonstrated health benefits for the patient—is proven. Since no definitive requirements exist for diagnostic AI-CDSS, the SWP criterion is open to interpretation. Disagreements between policy analysts and medical specialists emerged regarding burden of proof, study design, and the trade-off between quality of life and survival, highlighting the institutional ambiguity that hinders adoption.

The study further reveals that the use of AI per se is not a determining factor in reimbursement. AI-CDSS adoption pathways vary depending on technology type (molecular diagnostics vs. image analysis) and use case (in-hospital vs. screening). Similar institutional voids were identified for other AI-CDSS, emphasizing that these challenges are not unique to Mammaprint. Broader contextual factors also affect adoption, including limited hospital-based development resources, regulatory hurdles for certification, and additional evidence requirements for marketing. Comparisons with other countries reveal significant differences between predominantly public European healthcare systems and private U.S. systems.

This thesis applies a case study methodology using interviews and grey literature. Eighteen interviews with nineteen stakeholders, including policy analysts and medical specialists, were analyzed through an institutional actor analysis framework. This framework maps formal and informal institutions, identifies key actors and interactions, and assesses their power and interests. Findings were categorized using a sequential framework of device adoption phases, allowing structured analysis of barriers across the innovation lifecycle.

The research identifies actionable recommendations for Mammaprint and similar AI-CDSS. Consensus is needed on the required burden of proof, appropriate study designs to establish clinical utility, and ethical trade-offs between quality of life and survival. Temporary admission policies can facilitate data collection while maintaining accessibility. Furthermore, clarifying reimbursement pathways for molecular diagnostics and image analysis-based AI-CDSS, alongside developing a strategic vision for AI-CDSS in Dutch cancer care, is essential to future-proof the system. Without a clear vision and strategy, AI-CDSS adoption risks being impeded, potentially undermining sustainability and quality of cancer care.

In conclusion, institutional voids in reimbursement are the main factor stalling adoption of AI-CDSS in Dutch cancer care. Addressing these gaps through consensus building, clearer frameworks, and strategic planning can enhance the uptake of AI innovations, contributing to a more sustainable, patient-centered healthcare system. ... Read more

The Netherlands is experiencing a significant increase in its aging population. Multi morbidity and a rapidly growing shortage of healthcare personnel are putting pressure on the healthcare system, leading to capacity issues. The capacity problems of care have medical, safety, social, and economic consequences. Policy measures are needed to address these challenges and improve the transfer care system. Therefore, the research question is: On which factors should policy interventions by the Ministry of Health, Welfare, and Sport be aimed, to improve the performance of the Dutch transfer care system? This study uses qualitative System Dynamics (SD), a method that uses causal loop diagrams, to analyze the relationships between factors that influence the transfer care system performance, such as the demographic changes and workforce shortage. The goal is to find strategies and solutions for a sustainable healthcare system. A visual SD model is used to highlight the complexity of the transfer care system and to reveal inter dependencies, interactions, and feedback loops, aiming to understand system dynamics. A literature review, multiple interviews and case studies have revealed factors and interactions that are modeled in a causal loop diagram. This qualitative System Dynamics analysis revealed both unidirectional (+ or -) and different polarity (+/-) interactions/feedback loops between/among the indicators, which implicates that certain policy interventions (mentioned in Dutch policy notes and other publications) may not achieve the intended results. Based on the interactions found, the Ministry of Health, Welfare and Sport can start using a bundle of interventions aiming on reducing workload/number of admissions and simultaneously keeping availability of aftercare constant. Additionally, the Ministry can improve other indicators, when keeping workload constant. However, to determine how many additional interventions are needed to keep workload and availability aftercare constant and to determine the net total effect of interactions with different polarities, quantitative System Dynamics is needed. In addition, quantitative System Dynamics is needed to be able to prioritize policy interventions, because qualitative System Dynamics is not able to determine the net effect of feedback loops. However, this qualitative System Dynamics study provides a solid foundation with which researchers (and the Ministry of Health, Welfare and Sport) can proceed to construct a quantitative model. ... Read more

A pressure ulcer (PU) is defined as localised damage to the skin and/or underlying tissue, as a result of pressure or pressure in combination with shear. Despite the fact that pressure ulcers have received a lot of attention recently in hospitals and long-term care homes, they still form a major issue. The ErasmusMC is one of the hospitals with concerns regarding pressure ulcerations. Currently, there is a need to know why the implementation of measures for the improvement of preventing pressure ulcers resulted in disappointing results within the hospital. Within a literature review, it was found that the concept of systems thinking and the application of this concept is lacking in the selected studies. Therefore, the research question of this thesis was: Why are the current measures for the prevention of pressure ulcers not working as expected and intended within the ErasmusMC hospital?

A case study approach with a single-case (holistic) design was chosen. From the concept of systems thinking (the dynamic interaction, synchronisation, and integration of people, processes, and technology), the multi-level perspective of Geels was applied to this case study. With the use of the six common sources of Yin, 22 recorded and summarised semi-structured interviews, 4 informal interviews, several observational moments whilst accompanying operational staff or participating in meetings, and countless informal conversations were collected. Furthermore, PowerPoint presentations, documents, and dashboard results of the ‘prevalentiemeting’ within the ErasmusMC were collected.

When applying the dynamic multi-level perspective on system innovations to the situation of preventing pressure ulcers within the ErasmusMC, it was found that the landscape developments represented the urgency of the policymakers of the hospital to lower the occurrence of pressure ulcers, the technological niches were the measures (or system innovations) created for preventing pressure ulcers, and the socio-technical regime could be described as the socio-technical system for preventing pressure ulcers within the ErasmusMC. Combining the hypothesis of the multi-level perspective and the findings of the theoretical reasons within the literature review, it was stated that a nested hierarchy (where regimes are embedded within landscapes and niches within regimes) was missing. The analysis of the collected data resulted in the practical socio-technical system of preventing pressure ulcer within the ErasmusMC, where the 10 system elements were filled in with overarching themes found within the qualitative data. Next to this, it was found that the system elements of the socio-technical were interconnected and these interconnections could be interpreted as the key leverage points for building resilience within the practical system. The key leverage points found within the interconnections of the practical socio-technical system for preventing pressure ulcers within the ErasmusMC are the following: communication, implementation, opinions of prevention measures, policy steps, doubts about measures, and responsibility.

By using the mutli-level perspective theory of Geels and applying it to a healthcare setting, this thesis showed why the current measures for preventing pressure ulcers are not working as expected and intended within the ErasmusMC. This research made a contribution to the generic actor-related patterns found within the dynamic multi-level perspective on system innovations by introducing the information related patterns. It is shown that this new pattern can be described by the crucial role of information and the flow of information within an organisation for the accelerations and slowing down in diffusion and breakthrough of new technologies.
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