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Abstract-The healthcare sector is an incredibly complex system with many public and private actors and a wide diversity of services. Because of the aging society and the growing evolution of health expenditures, pressure on available resources (time, people, budget, etc.) is increasing. Innovative ICT supported eCare and eCure services are expected to increase efficiency, coordination and organization of care. Currently many initiatives already introduced ICT-supported eCare services into the current healthcare ecosystem. However, it's quite a challenge to estimate the impact and expected uptake of these new eCare services. It's no surprise that only few services commercially succeeded. This paper presents a methodology that identifies and evaluates the PEST factors (political, economical, social and technological aspects) that impact the involved care actors when introducing eCare services. The model takes into account the overall socio-economic aspects of the service, but also the subjective importance that an individual actor can ascribe to a particular eCare service. Combining the impact of these PEST factors leads to an expected adoption rate. Both new services and existing initiatives can be compared to one another and the impact of economies of scale can be investigated. In case a negative impact on some actors is observed, the model proposes cost allocation or service offer alternatives to ameliorate the business case for a particular service. Finally, a sensitivity analysis helps to indicate the most significant parameters that drive the business case.

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Minimizing item waiting time between stages is a general focus of operations research, and of particular concern for certain industries. We propose a two-stage production system where, to minimize the waiting time before stage 2, we focus on spreading the completion times of the stage 1 machines across the available interval. We contrast this objective with a similar problem defined in a healthcare context, but that has an assumption of fixed assignment. We obtain insights in the added value that free assignment can provide, by comparing the solutions of a local search method for assignment, with those of a reference case where assignment is fixed. Computational results show that this added value is highest in cases where task means differ insufficiently to be ordered effectively, and where task distributions have low variance. For the discussed instances, significant reductions in item waiting times can be achieved while making minimal concessions on expected makespan.