Preparation of chemotherapy drugs: Planning policy for reduced waiting times

Masselink, Inge H.J.; Mijden, T.L.C. van der; Litvak, Nelly; Vanberkel, Peter T.

doi:10.1016/j.omega.2011.05.003

Cited by 29 publications

(23 citation statements)

References 14 publications

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“…Vidal, Sahin, Martelli, Berhoune, and Bonan (2010) and Masselink, van der Mijden, Litvak, and Vanberkel (2012) both address the decision of which chemotherapy drugs to produce in advance, rather than on demand directly before use. These drugs are prepared for specific patients, and so become useless if patients are too ill for treatment and the drugs expire before they can be used.…”

Section: Other Treatment-related Studiesmentioning

confidence: 99%

“…Following the study, a decision support tool, designed to assist in choosing the drugs to produce in advance, was adopted by other pharmacies in France. Masselink et al (2012) worked with a pharmacy that is attached to a chemotherapy unit in the Netherlands. Unlike Vidal et al (2010), they focus on the effect on patient waiting times of preparing some chemotherapy drugs in advance.…”

Section: Other Treatment-related Studiesmentioning

confidence: 99%

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Operational research techniques applied throughout cancer care services: a review

2018

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Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers. ARTICLE HISTORY

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Section: Other Treatment-related Studiesmentioning

confidence: 99%

Section: Other Treatment-related Studiesmentioning

confidence: 99%

Operational research techniques applied throughout cancer care services: a review

2018

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“…The model relates the realised capacity to the expected access time (f a ) and expected number of idle slots (f e ) for each patient type. The discrete time queuing model is presented in analogy by the ones presented in Masselink, van der Mijden, Litvak, and Vanberkel (2012), Kortbeek et al (2014) and van de Vrugt, Boucherie, Smilde, de Jong, and Bessems (2017). The state of the model is the size of the backlog of a specific patient type p at the end of day d, B dp .…”

Section: The Stochastic Scheduling Problemmentioning

confidence: 99%

Static and dynamic appointment scheduling to improve patient access time

et al. 2017

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Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2% of the capacity flexibly already increases the performance of the clinic significantly. ARTICLE HISTORY

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“…diagnostic imaging and pharmacy). In Masselink et al 2012) we investigate the impact of pharmacy policies on patient waiting time at the NCI. The study evaluated whether a reduction in waiting time resulting from medication orders being prepared in advance of patient appointments was justified, given that medications prepared in advance are wasted when patients arrive too sick for treatment.…”

Section: Designing and Managing Patient-centered Processes Of Carementioning

confidence: 99%

Commentaries to “The Vital Role of Operations Analysis in Improving Healthcare Delivery”

Vanberkel

Zonderland

Boucherie³

et al. 2012

M&SOM

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In the Dutch context we see similar problems as outlined in Linda Green's commentary and, due in part to the redesign of the healthcare financing structure in the Netherlands, we have also seen a tremendous increase in the demand for operations analysis. The major redesign of the financial structure is described below, but for now, it is sufficient to state that its principal purpose is to achieve better value for the money spent on healthcare. Achieving more value for money is certainly an area where operations analysis can play a leading role. As a result, the demand for both our research capacity and recent graduates has been increasing. Furthermore, since healthcare providers are truly engaged, implementations of our results and recommendations have likewise increased. In this paper, we discuss recent projects to build on Linda Green's commentary and to argue how to apply operations analysis in healthcare in a scientifically and practically relevant way.In several short years, our research group has gone from asking ourselves: "How do we find healthcare providers to partner with?" to "How do we keep up with all the partners' requests?" As have many researchers and research groups, we have developed many strong and fruitful partnerships but have also squandered some opportunities. To try and maximize the former, our approach to collaborating with healthcare providers has evolved over time and is now proving effective in finding and isolating scientifically interesting problems which, when solved, have a significant positive impact on healthcare providers operations. Our goal as a research group is to improve healthcare providers operations, as well as to further develop the science and techniques of operations research.At many conferences, it is common for the following questions to be discussed (both formally and informally): "How do we get healthcare providers engaged in our research?", "How do we get the healthcare providers to implement our results?", "How do we test our results across multiple hospitals?" Furthermore, many review papers question whether the promised contribution of applying operations analysis is being achieved in healthcare (Lagergren 1998), since reporting implementation results is rarely done in journal articles (Katsaliaki & Mustafee 2011). It follows that there is a need to discuss how best to work with healthcare providers.Given this need and our recent progress in this regard, the focus of this commentary is to share the lessons we have learned while building a network of collaborating healthcare providers across the Netherlands. We now partner with healthcare providers of various types, including academic hospitals, general hospitals, and specialty hospitals, rehabilitation centres, government, and information

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Preparation of chemotherapy drugs: Planning policy for reduced waiting times

Cited by 29 publications

References 14 publications

Operational research techniques applied throughout cancer care services: a review

Operational research techniques applied throughout cancer care services: a review

Static and dynamic appointment scheduling to improve patient access time

Commentaries to “The Vital Role of Operations Analysis in Improving Healthcare Delivery”

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