Stochastic patient appointment scheduling for chemotherapy

Demir, Nur Banu
Chemotherapy appointment scheduling is a challenging problem due to uncertainty in pre-medication and infusion durations. We formulate a two-stage stochastic mixed integer programming model for chemotherapy appointment scheduling problem under the limited availability and number of nurses, and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime and patient waiting time. We sampled the pre-medication and infusion durations based on real data of a major oncology hospital. The computation times for the problem are significantly long even for the case of single-scenario problems. In order to strengthen the formulation, valid bounds and symmetry breaking constraints are incorporated. A Progressive Hedging Algorithm is implemented in order to solve the improved formulation. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and linearization of the model objective function. We conduct numerical experiments to compare the progressive hedging algorithm with several scheduling heuristics from the relevant literature. We generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules. Finally, we estimate the value of stochastic solution to assess the significance of considering uncertainty.


Multi-period appointment planning and scheduling in healthcare.
Bilgiç, Utku Tarık; Batun, Sakine; Department of Industrial Engineering (2019)
Appointment planning and scheduling (APS) plays a crucial role in patient service quality as well as utilization of valuable resources in healthcare. In this study, we considered the integrated problem of appointment planning and scheduling in an outpatient procedure center (OPC) over a planning horizon of multiple periods. We formulated the problem as a two-stage stochastic mixed-integer linear program (SMILP) with uncertainty in surgery durations. The first-stage problem consists of period assignment of s...
Lung ultrasound and computed tomographic findings in pregnant woman with COVID-19
Kalafat, Erkan; Yaprak, E.; ÇINAR, GÜLE; VARLI, BULUT; Ozisik, S.; UZUN, ÇAĞLAR; AZAP, ALPAY; KOÇ, ASLI (2020-06-01)
Imaging modalities play a crucial role in the management of suspected COVID-19 patients. Before reverse transcription polymerase chain reaction (RT-PCR) test results are positive, 60-93% of patients have positive chest computed tomographic (CT) findings consistent with COVID-19. We report a case of positive lung ultrasound findings consistent with COVID-19 in a woman with an initially negative RT-PCR result. The lung ultrasound-imaging findings were present between the negative and subsequent positive RT-PC...
Vessel segmentation in MRI using a variational image subtraction approach
SARAN, AYŞE NURDAN; Nar, Fatih; SARAN, MURAT (2014-01-01)
Vessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance ...
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
Polat, Gorkem; Kani, Haluk Tarik; Ergenc, Ilkay; Alahdab, Yesim Ozen; Temizel, Alptekin; Atug, Ozlen (2022-11-01)
Background Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. Methods The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep ...
Estimation of disease progression for ischemic heart disease using latent Markov with covariates
Oflaz, Zarina; Yozgatlıgil, Ceylan; Kestel, Sevtap Ayşe (2022-06-01)
Contemporaneous monitoring of disease progression, in addition to early diagnosis, is important for the treatment of patients with chronic conditions. Chronic disease-related factors are not easily tractable, and the existing data sets do not clearly reflect them, making diagnosis difficult. The primary issue is that databases maintained by health care, insurance, or governmental organizations typically do not contain clinical information and instead focus on patient appointments and demographic profiles. D...
Citation Formats
N. B. Demir, “Stochastic patient appointment scheduling for chemotherapy,” M.S. - Master of Science, Middle East Technical University, 2019.