A data-driven approach to determine daily platelet order quantities at hospitals.

BACKGROUND: Determining the required daily number of platelet units in hospitals is a challenging task due to the high uncertainty in daily usage and short shelf life of platelets. STUDY DESIGN AND METHODS: We developed a linear prediction model to guide the daily ordering quantity of platelet units at a hospital that orders the required units from a central supplier. The predictive model relies on historical demand data and other information from the hospital's information system. The ordering strategy is to place an order at the end of each day to bring the platelet inventory to the predicted demand for the next day. Unlike typical prediction models, the quality of the predictions is measured with respect to the resulting inventory costs of wastage and shortage. We used data from two hospitals in Hamilton, Ontario from 2015 to 2016 to train our model and evaluated its performance based on the resulting wastage and shortage rates in 2017. RESULTS: In 2017, respectively 1915 and 4305 platelet units were transfused at the two hospitals, with daily average (SD) usage of 5.2 (3.7) and 11.8 (4.4). The expiry (estimated shortage) rates were 8.67% (13.86%), and 2.28% (8.48%) at the two hospitals, respectively. Our baseline model would have reduced the expiry (shortage) rates to 2.54% (4.01%) and 0.05% (0.44%) for the two hospitals, respectively. DISCUSSION: Guiding daily ordering decisions for platelets using our proposed model could lead to a significant reduction of wastage and shortage rates at hospitals.

Can hospitals compete on quality? Hospital competition.

In this paper, we consider two hospitals with different perceived quality of care competing to capture a fraction of the total market demand. Patients select the hospital that provides the highest utility, which is a function of price and the patient's perceived quality of life during their life expectancy. We consider a market with a single class of patients and show that depending on the market demand and perceived quality of care of the hospitals, patients may enjoy a positive utility. Moreover, hospitals share the market demand based on their perceived quality of care and capacity. We also show that in a monopoly market (a market with a single hospital) the optimal demand captured by the hospital is independent of the perceived quality of care. We investigate the effects of different parameters including the market demand, hospitals' capacities, and perceived quality of care on the fraction of the demand that each hospital captures using some numerical examples.

Light Pareto robust optimization for IMRT treatment planning.

BACKGROUND: Robust optimization (RO) has been proposed to mitigate breathing motion uncertainty during treatment in intensity-modulated radiation therapy (IMRT) planning for breast or lung cancer. RO is a pessimistic approach that implicitly trades off average-case for worst-case treatment plan quality. Pareto robust optimization (PRO) provides a mechanism for improving nonworst-case plan outcomes, but often remains overly conservative in the average case. PURPOSE: The goal of this study is to characterize the trade-off between the optimality of robust IMRT plans in the worst case and the treatment quality in nonworst-case realizations of breathing motion. We provide a light Pareto robust optimization (LPRO) method for IMRT and test its clinical viability for improving the average-case plan quality while preserving robustness, in comparison to RO and PRO plans. METHODS: Five clinical left-sided breast cancer patients were included in the study, each with an associated 4D-CT dataset approximating their breathing cycle. Using simulation, 50 different breathing patterns were generated for each patient. A first-stage optimization was solved with the objective of cardiac sparing while ensuring robustness on the target dose under breathing uncertainty. Next, a second-stage objective of overdose minimization was considered to improve plan quality in a controlled LPRO framework. For the simulated breathing scenarios, the trade-off between loss of average cardiac sparing at worst-case and the overdose to the breast was quantified by calculating the accumulated dose for each plan in each breathing scenario. Finally, the RO, PRO, and LPRO plans were each evaluated using eight clinical dose-volume criteria on the target and organs at risk. RESULTS: The LPRO models allowed for significantly sharper dose falloffs in the expected dose instances, relative to both RO and PRO models. Plans began looking valid for delivery with average allowances of as little as +0.1 Gy additional dose to the heart, and most patients experienced diminishing returns beyond +0.2 Gy. CONCLUSIONS: Without sacrificing robustness, the LPRO approach produces viable plans with true total-target irradiation. Furthermore, the plans produced were able to reduce the nonworst-case downside typical of RO, without the characteristic overdosing or average-case pessimism seen in prior models.