Care for all is no longer guaranteed according to the Dutch Healthcare Authority. Dutch hospitals face increasing pressure in dealing with an increase in healthcare demand, staff shortages and financial uncertainty. To address these challenges the Dutch government introduced the Integral Care Agreement in which Shared Decision Making (SDM) is introduced as a way to tackle current healthcare problems. To support SDM, tools such as Decision Aids can be used. Decision Aids contain evidence based information on the treatment of a patient. This information helps the patient understand their medical condition and it facilitates them in choosing a treatment method that aligns with their values and preferences.

However, the adoption of decision aids seem to be lagging. This is due to the lack of clarity and financial uncertainty the implementation of Decision Aids bring. Hospitals are reimbursed for the care they deliver through a Diagnosis Treatment Combination (DBC). It is known that surgical DBCs generate more income for hospitals than non-surgical DBCs. A consequence of effective decision aid implementation is that patients opt for more conservative treatments, which could press the rise in demand for healthcare. However, as more patients choose for conservative treatments, the income of hospitals may decrease. If hospitals do not anticipate these changes in patient distribution across treatment options, they might become financially unstable.

This study aimed to help hospitals in their transition to Value Based Healthcare by evaluating the impact of Decision Aids. Current evaluation methods include analysing the changes in DBC and care activity volumes. A mixed methods approach was applied through 4 phases. Research methods included semi-structured interviews with healthcare professionals and System Dynamics Modeling.

Results showed a decrease in revenue for hospitals under the current financial system when Decision Aids are implemented, given that they cause for an increase in conservative treatments.

This study highlighted that these evaluation methods fall short in doing right compared to the quality improvement VBHC brings to care. Recommendations include finding a new evaluation method that is based on value-driven outcomes.

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.

Situation
In the Netherlands, the deductible is an essential part of the healthcare system, designed to regulate healthcare usage and keep healthcare costs manageable. The current deductible is €385 per year, but there are growing concerns about the accessibility of care for vulnerable groups such as chronic healthcare users and care avoiders. These concerns have led to political debates and policy proposals to revise the deductible. The Dutch government is set to adjust the deductible in 2027, spreading the cost over multiple treatments, €50 per treatment with an annual maximum of €165. While these adjustments are intended to increase access to care, little is yet known about the broader social impact of such changes to the deductible.

Complication (Question)
The central question of this thesis is: “What are the social costs and benefits of the policy change in the Dutch deductible to €50 per treatment with an annual maximum of €165, compared to the current system, for the entire Dutch society over the period 2027-2070?”

Approach
To answer this question, a Social Cost-Benefit Analysis (SCBA) was conducted to evaluate the social impact of four policy options: elimination of the deductible, reduction to €50, reduction to €165, and the Spread scenario. The SCBA looked at both quantified and monetized impacts, including the benefits of accessibility through perceived cheaper care, increased costs of nominal premiums and income-dependent contributions through changes in healthcare usage, costs, and health benefits. The analysis period was from 2027 to 2070 and took into account uncertainty factors such as population growth and price elasticity of health care demand. Sensitivity and uncertainty analyses were conducted to evaluate the robustness of the results, and critical assessments were made of the assumptions underlying the model.

Results
The analysis showed that all four scenarios result in a negative Net Present Value (NPV), indicating that costs exceed benefits over the entire period of analysis. The Spread scenario resulted in a total NPV of -€194 billion, with an average annual NPV of -€4.41 billion with extremes values
-€4.92 billion and -€2.56 billion. These results suggest that the social costs, mainly caused by higher nominal premiums and income-related contributions, significantly exceed the expected benefits, such as reduced spending on deductibles and health benefits from improved access to care.

In addition, the results show that the intended financial threshold of the Spread scenario may not be effective in reducing healthcare usage. The analysis indicates that total costs for healthcare users under this scenario are often lower than under the current situation, even after multiple treatments. This implies that the lower cost per treatment may actually encourage rather than inhibit healthcare usage, leading to higher healthcare usage, longer waiting times for care, quality reduction in care, and higher costs to society.

At the individual level, we are talking about an average annual NPV of €275 per person. But this includes an increase in nominal premium and income dependent contribution of €452.90 plus the cost of the deductible. This will increase nominal premium more than the deductible will decrease. Society's solidarity will be greatly called upon, because the difference between payments for healthcare users and non-healthcare users will be significantly reduced, while the cost of the nominal premium will increase by 26%.

Next Steps
The study contained several assumptions that require further research to reduce uncertainties. It is recommended that follow-up research be conducted on detailed demographic analyses to better understand societal responses to policy. Furthermore, it is important to identify the long-term effects of early disease interventions to better estimate future care needs. In addition, it is crucial to reconsider the current policy proposal and conduct extensive research on its potential effects to ensure that policy goals are achieved without undermining solidarity.

The findings of this thesis contribute to the broader debate on the future of the Dutch healthcare system by emphasizing that policy choices around the deductible should not only be financially feasible, but also fairly and sustainably aligned with the fundamental values of solidarity and accessibility in healthcare.

The reimbursement scheme in geriatric rehabilitation (GR) contains stepwise tariffs depending on both the length of stay and treatment intensity: after a pre-determined number of inpatient days or treatment hours, the total reimbursement jumps to a higher level and then stays constant until the next threshold is reached. These discontinuous reimbursements may distort physicians’ behaviour in a way that is detrimental to the efficiency of care provision. This thesis examines whether these behavioural distortions are present regarding GR and, if so, which factors are associated with stronger distortions on a GR provider level. For this goal, GR claims data from health insurer CZ covering the years 2017–2023 are used. Furthermore, this thesis qualitatively assesses the possible consequences of the proposed changes in the payment system of GR, including the abolition of the current stepwise tariffs, using insights from GR claims data and economic theory.

Background: A shift in the organisation of oncology care towards collaboration between hospitals in oncology networks (CCNs) is observed to allow high-quality, accessible, and affordable care in the future. The collaborating hospitals align their care processes and design a joined regional care pathway (RCP) to standardise and structure their care. Within an RCP the patient is transferred between hospitals in the CCN, often for specialised and complex care like surgery.

Problem statement: The various disciplines and institutions involved in care delivery ask for coordination of care, clear agreements, and proper handovers of tasks. Inter-organisational collaborations within a CCN introduce new challenges (e.g. in information exchange) regarding coordination compared to multidisciplinary intra-hospital collaboration, because organisational and geographic boundaries are crossed. Unfortunately, little is known about how coordination between collaborating hospitals in a regional oncology care pathway can or should be achieved. Therefore, this thesis aims to contribute by developing a framework guiding improvements in coordination for RCPs.

Methods: A mixed-method approach was used which included a literature search and semi-structured interviews with healthcare professionals in oncology networks, policymakers, and patients. A selected conceptual framework of care coordination was adapted based on literature and each aspect within the framework was defined. Additionally, evaluation measures and improvement actions for each aspect were identified. The interviews were used to elaborate the findings from literature and incorporate the right context for RCPs. Moreover, the interviews gave insights into the experienced challenges and best practices. Lastly, the developed framework was applied in a case study.

Results: The framework for Care Coordination in Chronic and Complex Disease Management was opted as an initial conceptual framework and was extended by three additional aspects identified by other studies. The framework categorises the aspects of care coordination in domains creating an overview of the relation of each aspect in the coordination processes. The interviews resulted in two extensions of the original framework: 1) Alignment of resources and processes besides cultural alignment; and 2) leadership/coordination at the network level besides the boundary-spanning role of the case manager at the patient level. All aspects of the framework fit the RCP context, although some overlap between the aspects was identified.

Conclusion: The framework supports hospitals in improving coordination between hospitals within the RCP by offering an overview of aspects, evaluation measures and improvement possibilities. Information exchange, communication, and clear RCP protocols with defined roles and responsibilities turn out to be important aspects that facilitate coordination. These should be supported by boundary spanning actions and emergent conditions, like personal relationships and trust. Especially organising digital exchange of patient information is important for CCNs, but is still challenging to establish. A well-designed monitoring system including reminders and alerts will support coordination. However, regional dashboard development is challenged by non-structured data and differences in data structures between the hospitals. To further scale up initiatives for larger challenges and more resourceful improvement initiatives (e.g. digital exchange of EMR data), close collaboration within and between CCNs, and exchange of knowledge, tools, and best practices should be established.