TY - JOUR
T1 - Adaptive resource allocation for efficient patient scheduling
AU - Vermeulen, I.B.
AU - Bohte, S.M.
AU - Elkhuizen, S.G.
AU - Lameris, J.S.
AU - Bakker, P.J.M.
AU - Poutré, La, J.A.
PY - 2009
Y1 - 2009
N2 - Objective
Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible.
Methods
We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam.
Results and conclusion
We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity.
AB - Objective
Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible.
Methods
We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam.
Results and conclusion
We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity.
U2 - 10.1016/j.artmed.2008.07.019
DO - 10.1016/j.artmed.2008.07.019
M3 - Article
C2 - 18845423
SN - 0933-3657
VL - 46
SP - 67
EP - 80
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
IS - 1
ER -