Improving Patient Access to Chemotherapy Treatment

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Introduction Because of cancer becoming to the leading cause of death, the demand of services in cancer institution is steady increasing in recent years. Certain of researchers indicated that patients spend much more time on waiting, either in waiting to range an appointment or waiting at cancer institutions. Therefore, the paper “Improving Patient Access to Chemotherapy Treatment at Duke Cancer Institute” (Woodall etc, 2013) is aim to improve to the patient flow in their institution, which also focus on to apply in other cancer institutions. Initially, writers obtain some basic information about Duke Cancer Institute on the information flow among different departments. The departments include clinic, radiology, central laboratory, oncology treatment center (OTC) and pharmacy; the nurse’s types include full-time and part-time. In order to optimize and simulate the processes to meet patients demand and allocate resources, the researchers provide three models to achieve their objectives. As these three models are being analysis step-by-step and the later model is based on previous results. The first model is ‘discrete-event simulation model’, which is aim to predict patient waiting time and acquire the information on resources utilization among different departments. Nevertheless, researchers identified that the most serious bottleneck is in OTC, because of nurses are unavailable during the treatment process. Therefore researchers decided to key focus on OTC, and made assumptions on maximum patients’ number for each nurse. As a result that OTC is subject to time variability, improve the shift time schedules and working length of nurses will be the better way to distribute nurse’s supply with demand of patients. Based on the first mode, then they raised to using ‘mixed-integer programming model’ for nurse shifting, to release the bottleneck in OTC. The schedules shifting include daily, weekly and monthly, nurse type include full-time and part-time. This method is used to concentrate on predetermine the amount of nurses on weekly and monthly schedules. The full-time nurse type will be including 10-hours and 8-hours per day. The nurses’ allocation of weekly and monthly is based on daily patients’ demand. As a result, those analysts determine to change one or two full-time nurses into same ability levels of part-time nurses, which is more suitable for OTC peak time demand and reduce resources overuse when there don’t have too much patients. The final model is based on the previous model to further relieve the bottleneck by optimize the start time and end time on daily nurse shifting. This method is focus on the daily nurse shifting.

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