Adaptive resource allocation for efficient patient scheduling

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

Improving patient activity schedules by multi-agent Pareto appointment exchanging

We present a dynamic and distributed approach to the hospital patient scheduling problem: the multi-agent Pareto-improvement appointment exchanging algorithm, MPAEX. It respects the decentralization of scheduling authorities and is capable of continuously adjusting the different patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of th

Dynamische Optimalisatie van Patiëntenplanning

In dit hoofdstuk bespreken wij het optimaliseren van de afsprakenplanning voor pati{\"e}nten bij centrale diagnostische faciliteiten. Automatische optimalisatie in het planningsproces is nodig omdat het in de praktijk vaak veel tijd en moeite kost om de agenda effici{\"e}nt te gebruiken. Door middel van een systeem dat het gebruik van de agenda kan monitoren en de beste aanpassingen kan berekenen, kan de agenda zo goed mogelijk worden gebruikt. Naast effici{\"e}ntie, is de service die aan de pati{\"e}nten wordt verleend van belang. We presenteren een methode om rekening te houden met pati{\"e}ntvoorkeuren in het planningsproces. Voor het maken van combinatieafspraken zal er bovendien co{\"o}rdinatie tussen afdelingen moeten plaatsvinden

Adaptive Patient Scheduling with Dynamic Resource Usage

Can patient planning be more efficient? The Computational Intelligence and Multiagent Games (SEN4) research group at CWI uses software agents and smart, adaptive algorithms to improve hospital patient scheduling and to better match patients’ appointments to their own preferences

Adaptive Patient Scheduling with Dynamic Resource Usage

Can patient planning be more efficient? The Computational Intelligence and Multiagent Games (SEN4) research group at CWI uses software agents and smart, adaptive algorithms to improve hospital patient scheduling and to better match patients’ appointments to their own preferences

An Efficient Turnkey Agent for Repeated Trading with Overall Budget and Preferences

For various e-commerce applications autonomous agents can do the actual trading on behalf of their users. We consider an agent who trades repeatedly on behalf of his user, given an overall budget and preferences per time step, both specified at the start. For many e-commerce settings such an agent has limited computational resources, limited prior information concerning price fluctuations, and little time for online learning. We therefore develop an efficient heuristic that requires little prior information to work well from the start, even for very roughed nonsmooth problem instances. Extensive computer experiments conducted for a wide variety of customer preferences show virtually no difference in performance between a dynamic programming (DP) approach and the developed heuristic carrying out the agent's task. The DP approach has, however, the important drawback of generally being too computationally intensive

Multi-agent Pareto appointment exchanging in hospital patient scheduling

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings

Multi-agent Pareto appointment exchanging in hospital patient scheduling

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this p

Improving Patient Schedules by Multi-agent Pareto Appointment Exchanging

We present a dynamic and distributed approach to the hospital patient scheduling problem: the multi-agent Pareto-improvement appointment exchanging algorithm, MPAEX. It respects the de