Appointment scheduling of oncology outpatients

Sadki, Abdellah; Xie, Xiaolan; Chauvin, Franck

doi:10.1109/case.2011.6042417

Cited by 24 publications

(17 citation statements)

References 10 publications

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“…Both local search methods [42,44] and other meta-heuristics [33] are applied to develop patient schedules, as well as approximate stochastic approaches [30] and simple planning rules [22,41].…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

Stochastic integer programming for multi-disciplinary outpatient clinic planning

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Hans

2017

Health Care Manag Sci

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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.

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Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Leeftink

Vliegen

Hans

2017

Health Care Manag Sci

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“…System performance in flow-shop systems is measured by means of completion times, throughput times, tardiness, the number of patients admitted, utilization, and overtime [20,35,86,102,108,148,150,160]. Due to the sequencing relations in a flow-shop system, flexibility in the planning is limited.…”

Section: Type Of System Referencesmentioning

confidence: 99%

Organizing timely treatment in multi-disciplinary care

Bikker

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T u e s d a y 3. 30 pm f o l l o w u p F r i d a y 8 . 3 0 a m m o l d t e s t i n g M on da y 9a m C T sc an Th ur sd ay 8a m cy st os co py Th urs da y 11.30 ex pla na tio n of tr ea tm en t Th ur sd ay in ta k e fo r pr os th es is M o n d a y 1 0 a m r e g u la r c h e c M o n d a y 3p m d ie t ic ia n Tue sda y 2pm phy si oth era pi st Wednesday + Thursday 4.30 pm group therapy F r i d a y 1 1 a m o c c u p a t i o n a l t h e r a p i s t Tu es da y 3p m r e h a b il it a t io n d o c t o r l i n a c s e s s i o n s 10am M on d a y ti ll F ri d a y M on da y 11.3 0 a m co ok in g g ro up th er a py Friday 11am intake with doctor M o n d a y 2 . 3 0 p m P E T -C T s ca n Organizing timely treatment in multi-disciplinary care Ingeborg A. Bikker T u e s d a y 3. 30 pm f o l l o w u p F r i d a y 8 . 3 0 a m m o l d t e s t i n g M on da y 9a m C T sc an Th ur sd ay 8a m cy st os co py Th urs da y 11.30 ex pla na tio n of tr ea tm en t Th ur sd ay in ta k e fo r pr os th es is M o n d a y 1 0 a m r e g u la r c h e c M o n d a y 3p m d ie t ic ia n Tue sda y 2pm phy si oth era pi st Wednesday + Thursday 4.30 pm group therapy F r i d a y 1 1 a m o c c u p a t i o n a l t h e r a p i s t Tu es da y 3p m r e h a b il it a t io n d o c t o r l i n a c s e s s i o n s 10am M on d a y ti ll F ri d a y M on da y 11.3 0 a m co ok in g g ro up th er a py

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“…System performance in flow-shop systems, is measured by means of completion times, throughput times, tardiness, the number of patients admitted, utilization, and overtime [25,50,147,175,178,263,266,277]. Due to the sequencing relations in a flow-shop system, flexibility in the planning is limited.…”

Section: Type Of Systemmentioning

confidence: 99%

“…These time-related objectives are especially relevant in healthcare settings where patients are not physically present, such as laboratories and sterilization departments [175,263]. When patients are physically present, the number of patients admitted, utilization, and overtime are relevant measures [25,50,178,266]. When a fixed capacity is reserved for such a one-stop-shop, rapid diagnostics, or carousel program, the maximum number of patients to be admitted is restricted [262].…”

Section: Type Of Systemmentioning

confidence: 99%

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Organizing integrated processes in cancer care

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Appointment scheduling of oncology outpatients

Cited by 24 publications

References 10 publications

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Organizing timely treatment in multi-disciplinary care

Organizing integrated processes in cancer care

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