Histopathology laboratories aim to deliver high quality diagnoses based on patient tissue samples. Timely and high quality care are essential for delivering high quality diagnoses, for example in cancer diagnostics. However, challenges exist regarding employee workload and tardiness of results, which both impact the diagnostic quality. In this paper the histopathology operations are studied, where tissue processors are modeled as batch processing machines. We develop a new 2-phased decomposition approach to solve this NP-hard problem, aiming to improve the spread of workload and to reduce the tardiness. The approach embeds ingredients from various planning and scheduling problems. First, the batching problem is considered, in which batch completion times are equally divided over the day using a Mixed Integer Linear Program. This reduces the peaks of physical work available in the laboratory. Second, the remaining processes are scheduled to minimize the tardiness of orders using a list scheduling algorithm. Both theoretical as well as historical data were used to assess the performance of the method. Results show that using this decomposition method, the peaks in histopathology workload in UMC Utrecht, a large university medical center in The Netherlands, may be reduced with up to 50 % by better spreading the workload over the day. Furthermore, turnaround times are reduced with up to 20 % compared to current practices. This approach is currently being implemented in the aforementioned hospital.

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multidisciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

BackgroundWaiting time from diagnosis to treatment has emerged as an important quality indicator in cancer care. This study was designed to determine the impact of waiting time on long-term outcome of patients with esophageal cancer who are treated with neoadjuvant therapy followed by surgery or primary surgery.MethodsPatients who underwent esophagectomy for esophageal cancer at the University Medical Center Utrecht between 2003 and 2014 were included. Patients treated with neoadjuvant therapy followed by surgery and treated with primary surgery were separately analyzed. The influence of waiting time on survival was analyzed using Cox proportional hazard analyses. Kaplan–Meier curves for short (<8 weeks) and long (≥8 weeks) waiting times were constructed.ResultsA total of 351 patients were included; 214 received neoadjuvant treatment, and 137 underwent primary surgery. In the neoadjuvant group, the waiting time had no impact on disease-free survival (DFS) [hazard ratio (HR) 0.96, 95 % confidence interval (CI) 0.88–1.04; p = 0.312] or overall survival (OS) (HR 0.96, 95 % CI 0.88–1.05; p = 0.372). Accordingly, no differences were found between neoadjuvantly treated patients with waiting times of <8 and ≥8 weeks in terms of DFS (p = 0.506) and OS (p = 0.693). In the primary surgery group, the waiting time had no impact on DFS (HR 1.03, 95 % CI 0.95–1.12; p = 0.443) or OS (HR 1.06, 95 % CI 0.99–1.13; p = 0.108). Waiting times of <8 weeks versus ≥8 weeks did not result in differences regarding DFS (p = 0.884) or OS (p = 0.374).ConclusionsIn esophageal cancer patients treated with curative intent by either neoadjuvant therapy followed by surgery or primary surgery, waiting time from diagnosis to treatment has no impact on long-term outcome.