Estimating the probability of positive crossmatch after negative virtual crossmatch

This paper estimates the probability of virtual crossmatch failure in kidney exchange matching. In particu-lar, the probability of a positive crossmatch after a negative virtual crossmatch is related to the recipient’s PRA level. Using Dutch kidney exchange data, we find significant evidence that this probability increases non-linearly with PRA level. We estimate a probit model that describes this relationship

Solution methods for the tray optimization problem

In order to perform medical surgeries, hospitals keep large inventories of surgical in- struments. These instruments need to be sterilized before each surgery. Typically the instruments are kept in trays. Multiple trays may be required for a single surgery, while a single tray may contain instruments that are required for multiple surgical procedures. The tray optimization problem (TOP) consists of three main decisions: (i) the assignment of instruments to trays, (ii) the assignment of trays to surgeries, and (iii) the number of trays to keep in inventory. The TOP decisions have to be made such that total operating costs are minimized and such that for every surgery sufficient instruments are available. This paper presents and evaluates several exact and heuristic solution methods for the TOP. We compare solution methods on computation time and solution quality. Moreover, we conduct simulations to evaluate the performance of the solutions in the long run. The novel methods that are provided are the first methods that are capable of solving instances of realistic size. The most promising method consists of a highly scalable advanced greedy algorithm. Our results indicate that the outcomes of this method are, on average, very close to the outcomes of the other methods investigated, while it may be easily applied by (large) hospitals. The findings are robust with respect to fluctuations in long term OR schedules

Physician Incentive Management in University Hospitals: Inducing Efficient Behavior Through the Allocation of Research Facilities

The imperative to improve healthcare efficiency is now stronger than ever. Rapidly increasing healthcare demand and the prospect of healthcare cost exploding require that measures be taken to make healthcare organizations become more efficiency-aware. Alignment of organizational interests is therefore important. One of the main hurdles to overcome is the provision of the right incentives to healthcare workers, in particular physicians. In this research we investigate the incentive system for physicians in university hospitals. We present an inquiry held in a large university hospital in the Netherlands and show that non-financial incentives receive significantly more support among physicians than financial incentives. Over 95 percent of the physicians indicated they derive more work stimulus from research possibilities or scientific status than from wage. Over 80 percent of the physicians also indicated they prefer to be able to do more research. We therefore identified a broad class of non-financial incentives aimed at physicians in university hospitals: research facilities. The main tradeoff in using research facilities within an incentive system is between efficient resource utilization and inducement effects. This thesis constructs a principal-multi-agent model where agents engage in both care and research and which includes heterogeneity and private information. We study how research facilities incentives can be used to improve hospital performance if the current wage system is left intact. We show that research facilities are optimally used as incentives for both care and research activities, and that the hospital offers different contracts depending on physician ability and valuation. Moreover, if physicians need to reveal their valuations for research facilities, the hospital finds it optimal to allow physicians to make a rent. We discuss some implications of extending the theoretical results to practice

The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared

The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and has many interesting applications in logistics, tourism and defense. To reflect real-life situations, we focus on an uncertain variant of the OP. Two main approaches that deal with optimization under uncertainty are stochastic programming and robust optimization. We will explore the potentialities and bottlenecks of these two approaches applied to the uncertain OP. We will compare the known robust approach for the uncertain OP (the robust orienteering problem) to the new stochastic programming counterpart (the two-stage orienteering problem). The application of both approaches will be explored in terms of their suitability in practice

Unspecified donation in kidney exchange: when to end the chain

This paper studies participation of unspecified donors in kidney exchange through simultaneous domino paired donation (DPD) and non-simultaneous extended altruistic donor (NEAD) chains. It extends existing research by investigating the termination of chains, the possibility of transplantation across the blood type barrier, and the impact of incentives in multi-center exchanges. Furthermore, it looks into the effect of various configuration parameters such as the time interval between exchanges. Our analysis is based on a simulation study that uses data of all 438 patient-donor pairs and 109 unspecified donors who were screened at Dutch transplant centers between 2003 and 2011. Because multi-center coordination may raise incentive issues, special attention is paid to individually rational implementation. We find that chains are best terminated when no further segment is part of an optimal exchange within 3 months. Transplantation across the blood type barrier allows for longer continuation of chains, more transplants and more equity among patient groups. NEAD chains perform slightly better than DPD chains, provided that the renege rate is sufficiently low. Additional substantial gains are due to national individually rational coordination. Particularly highly sensitized and blood type O patients benefit. Appropriate timing of ex-changes can further improve these results

Iterative branch-and-price for hierarchical multi-criteria kidney exchange

Kidney exchange is an increasing modality for transplanting end stage renal disease patients with an incompatible living donor. Typically, the aim is to find an allocation of donors to patients that is optimal with respect to multiple hierarchical criteria. This paper presents an iterative branch-and-price algorithm for clearing such multi-criteria kidney exchanges with large patient-donor pools. Using a polynomial pricing procedure, the algorithm accomodates not only for cycles of incompatible pairs but also for long chains initiated by unspecified donors. Such chains are increasingly common and important in clinical practice, but, as we show, cannot be efficiently dealt with using existing depth-first pricing procedures. Our algorithm also supports individual rationality constraints required for multi-center coordination. Using Dutch kidney exchange data, we show the effect of long term multi-criteria optimization with our algorithm