C. Pistorius
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Designing Optimized Rheumatology Patient Journeys
Enhancing Capacity Planning and Sustainable Engagement of Rheumatologists
Currently, Rheumatology outpatient clinics face increasing physician workload because patient demand varies over time, rheumatologic diagnoses can be chronic in nature, and the number of active physicians is decreasing. Based on a case study provided by Leiden University Medical Center (LUMC) and in collaboration with Reinier de Graaf Gasthuis Delft (RdGG), this thesis develops a mathematical framework to improve capacity utilization while supporting sustainable employability and timely access to care.
This thesis focuses on the tactical and operational levels of appointment scheduling (AS). Tactical AS addresses medium-term capacity allocation decisions, whereas operational AS focuses on scheduling individual appointments. Therefore, we develop a multi-stage approach in which uncertain factors
such as patient inflow and follow-up patterns are included. These factors are based on historical data provided by LUMC.
At the tactical level, different blueprint AS models are formulated. Tactical blueprint AS refers to the allocation of capacity into time slots across physicians and appointment types. Each model corresponds to a blueprint AS scenario, differentiating between new appointment distributions among physicians,
urgent appointment restrictions, and slot allocation within the blueprint schedules. These models consider hierarchical optimization problems to deal with multiple objectives having different priorities, such as preferred cyclic blueprint schedules, urgent and regular slot allocations, and accessibility for new patients.
Next, at the operational level, the realized blueprint schedules from the tactical level are used for the AS algorithms. We develop a sequential algorithm that traverses our arrival horizon (e.g., one year) day by day and schedules follow-up appointments if necessary. Three operational scheduling approaches are considered: fixed and flexible blueprint scheduling, and open scheduling. Moreover, we evaluate whether reserving future capacity affects timely access to care.
Computational results show that good tactical blueprint scheduling performance does not necessarily match good operational performance, for example, with respect to new appointments scheduled within their target timeframe of four weeks. This emphasizes the importance of evaluating tactical scheduling decisions within a realistic operational setting. Among the blueprint scheduling approaches, semi-flexible blueprint scheduling marginally outperformed fixed blueprint scheduling, while flexible blueprint scheduling performed best in terms of timely access to care. Open scheduling is also a suitable approach, as it offers good access to care. However, these improvements come at the cost of less
predictable physician schedules. Hospitals can decide whether performance improvements outweigh the additional planning effort and implementation costs.
Instead of recommending one optimal scheduling approach, the proposed framework enables hospitals to choose the approach that best fits their organizational setting, priorities, and desired balance between timely access to care and sustainable employability. Finally, we discuss the limitations of this research and suggest directions for future work, such as incorporating patient satisfaction, staff job satisfaction, and the use of AI tools in appointment scheduling (AS). ...
This thesis focuses on the tactical and operational levels of appointment scheduling (AS). Tactical AS addresses medium-term capacity allocation decisions, whereas operational AS focuses on scheduling individual appointments. Therefore, we develop a multi-stage approach in which uncertain factors
such as patient inflow and follow-up patterns are included. These factors are based on historical data provided by LUMC.
At the tactical level, different blueprint AS models are formulated. Tactical blueprint AS refers to the allocation of capacity into time slots across physicians and appointment types. Each model corresponds to a blueprint AS scenario, differentiating between new appointment distributions among physicians,
urgent appointment restrictions, and slot allocation within the blueprint schedules. These models consider hierarchical optimization problems to deal with multiple objectives having different priorities, such as preferred cyclic blueprint schedules, urgent and regular slot allocations, and accessibility for new patients.
Next, at the operational level, the realized blueprint schedules from the tactical level are used for the AS algorithms. We develop a sequential algorithm that traverses our arrival horizon (e.g., one year) day by day and schedules follow-up appointments if necessary. Three operational scheduling approaches are considered: fixed and flexible blueprint scheduling, and open scheduling. Moreover, we evaluate whether reserving future capacity affects timely access to care.
Computational results show that good tactical blueprint scheduling performance does not necessarily match good operational performance, for example, with respect to new appointments scheduled within their target timeframe of four weeks. This emphasizes the importance of evaluating tactical scheduling decisions within a realistic operational setting. Among the blueprint scheduling approaches, semi-flexible blueprint scheduling marginally outperformed fixed blueprint scheduling, while flexible blueprint scheduling performed best in terms of timely access to care. Open scheduling is also a suitable approach, as it offers good access to care. However, these improvements come at the cost of less
predictable physician schedules. Hospitals can decide whether performance improvements outweigh the additional planning effort and implementation costs.
Instead of recommending one optimal scheduling approach, the proposed framework enables hospitals to choose the approach that best fits their organizational setting, priorities, and desired balance between timely access to care and sustainable employability. Finally, we discuss the limitations of this research and suggest directions for future work, such as incorporating patient satisfaction, staff job satisfaction, and the use of AI tools in appointment scheduling (AS). ...
Currently, Rheumatology outpatient clinics face increasing physician workload because patient demand varies over time, rheumatologic diagnoses can be chronic in nature, and the number of active physicians is decreasing. Based on a case study provided by Leiden University Medical Center (LUMC) and in collaboration with Reinier de Graaf Gasthuis Delft (RdGG), this thesis develops a mathematical framework to improve capacity utilization while supporting sustainable employability and timely access to care.
This thesis focuses on the tactical and operational levels of appointment scheduling (AS). Tactical AS addresses medium-term capacity allocation decisions, whereas operational AS focuses on scheduling individual appointments. Therefore, we develop a multi-stage approach in which uncertain factors
such as patient inflow and follow-up patterns are included. These factors are based on historical data provided by LUMC.
At the tactical level, different blueprint AS models are formulated. Tactical blueprint AS refers to the allocation of capacity into time slots across physicians and appointment types. Each model corresponds to a blueprint AS scenario, differentiating between new appointment distributions among physicians,
urgent appointment restrictions, and slot allocation within the blueprint schedules. These models consider hierarchical optimization problems to deal with multiple objectives having different priorities, such as preferred cyclic blueprint schedules, urgent and regular slot allocations, and accessibility for new patients.
Next, at the operational level, the realized blueprint schedules from the tactical level are used for the AS algorithms. We develop a sequential algorithm that traverses our arrival horizon (e.g., one year) day by day and schedules follow-up appointments if necessary. Three operational scheduling approaches are considered: fixed and flexible blueprint scheduling, and open scheduling. Moreover, we evaluate whether reserving future capacity affects timely access to care.
Computational results show that good tactical blueprint scheduling performance does not necessarily match good operational performance, for example, with respect to new appointments scheduled within their target timeframe of four weeks. This emphasizes the importance of evaluating tactical scheduling decisions within a realistic operational setting. Among the blueprint scheduling approaches, semi-flexible blueprint scheduling marginally outperformed fixed blueprint scheduling, while flexible blueprint scheduling performed best in terms of timely access to care. Open scheduling is also a suitable approach, as it offers good access to care. However, these improvements come at the cost of less
predictable physician schedules. Hospitals can decide whether performance improvements outweigh the additional planning effort and implementation costs.
Instead of recommending one optimal scheduling approach, the proposed framework enables hospitals to choose the approach that best fits their organizational setting, priorities, and desired balance between timely access to care and sustainable employability. Finally, we discuss the limitations of this research and suggest directions for future work, such as incorporating patient satisfaction, staff job satisfaction, and the use of AI tools in appointment scheduling (AS).
This thesis focuses on the tactical and operational levels of appointment scheduling (AS). Tactical AS addresses medium-term capacity allocation decisions, whereas operational AS focuses on scheduling individual appointments. Therefore, we develop a multi-stage approach in which uncertain factors
such as patient inflow and follow-up patterns are included. These factors are based on historical data provided by LUMC.
At the tactical level, different blueprint AS models are formulated. Tactical blueprint AS refers to the allocation of capacity into time slots across physicians and appointment types. Each model corresponds to a blueprint AS scenario, differentiating between new appointment distributions among physicians,
urgent appointment restrictions, and slot allocation within the blueprint schedules. These models consider hierarchical optimization problems to deal with multiple objectives having different priorities, such as preferred cyclic blueprint schedules, urgent and regular slot allocations, and accessibility for new patients.
Next, at the operational level, the realized blueprint schedules from the tactical level are used for the AS algorithms. We develop a sequential algorithm that traverses our arrival horizon (e.g., one year) day by day and schedules follow-up appointments if necessary. Three operational scheduling approaches are considered: fixed and flexible blueprint scheduling, and open scheduling. Moreover, we evaluate whether reserving future capacity affects timely access to care.
Computational results show that good tactical blueprint scheduling performance does not necessarily match good operational performance, for example, with respect to new appointments scheduled within their target timeframe of four weeks. This emphasizes the importance of evaluating tactical scheduling decisions within a realistic operational setting. Among the blueprint scheduling approaches, semi-flexible blueprint scheduling marginally outperformed fixed blueprint scheduling, while flexible blueprint scheduling performed best in terms of timely access to care. Open scheduling is also a suitable approach, as it offers good access to care. However, these improvements come at the cost of less
predictable physician schedules. Hospitals can decide whether performance improvements outweigh the additional planning effort and implementation costs.
Instead of recommending one optimal scheduling approach, the proposed framework enables hospitals to choose the approach that best fits their organizational setting, priorities, and desired balance between timely access to care and sustainable employability. Finally, we discuss the limitations of this research and suggest directions for future work, such as incorporating patient satisfaction, staff job satisfaction, and the use of AI tools in appointment scheduling (AS).