Maximizing Operating Room Performance Using Portfolio Selection

The operating room (OR) is responsible for most hospital admissions and is one of the most cost and work intensive areas in the hospital. From recent trends, we observe an ironic parallel increase among expenditure and waiting time. Therefore, improving OR scheduling has become obligatory, particularly in terms of patient flow and benefit. Most of the hospitals rely on average patient arrivals and processing times in OR planning. But in practice, variations in arrivals and processing times causes high instability in OR performance. Our model of optimization provides OR schedules maximizing patient flow and benefit at a fixed level of risk using portfolio selection. The simulation results show that the performance of the OR has a direct relationship with the risk

BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process

Operating room (OR) scheduling is important. Because of increasing demand for surgical services, hospitals must provide high quality care more efficiently with limited resources. When constructing the OR schedule, it is necessary to consider the availability of downstream resources, such as intensive care unit (ICU) and post anaesthesia care unit (PACU). The unavailability of downstream resources causes blockings between every two consecutive stages. In this paper we address the master surgical schedule (MSS) problem in order to minimize blockings between two consecutive stages. First, we present a blocking minimization (BM) model for the MSS by using integer programming, based on deterministic data. The BM model determines the OR block schedule for the next day by considering the current stage occupancy (number of patients) in order to minimize the number of blockings between intraop and postop stages. Second, we test the effectiveness of our model under variations in case times and patient arrivals, by using simulation. The simulation results show that our BM model can significantly reduce the number of blockings by 94% improvement over the base model. Scheduling patient flow across the 3-stage periop process can be applied to work flow scheduling for the s-stage flow shop shop production in manufacutirng, and also Smoothing patient flow in periop process can be applied to no-wait flow shop production

Stochastic BI-Level Optimization Models for Efficient Operating Room Planning

Within a hospital, the operating room (OR) department has the largest cost and revenue. Because of the aging population, the demand for surgical services has been increasing sharply in recent years. At the other hand, the rate of OR capacity expansion is lower than the rate of increasing demand. As a result, OR managers must leverage their resources by efficient OR planning. OR planning is challenging because of multiple competing\conflicting objectives such cost minimization and throughput maximization. Inherent uncertainty in the surgical procedures and patients arrivals complicate the decision making process. This increases the risk of non-realization of the system objectives. In this paper, stochastic bi-level optimization models were formulated to optimize total cost and throughput of ORs under the presence of uncertainties in patient arrivals and case times. Newsvendor model and chance-constrained optimization method were used to optimize multiple objectives under the presence of uncertainties. Using historical data, a simulation model was established to validate the results of optimization models. Using statistical process control (SPC) stability of each model was investigated. Using bi-level optimization, we addressed managerial preferences over total cost and throughput. Optimizing one objective may lead to compromise on the optimality of the other objective, which generates trade-offs. Using a trade-off balancing model, we found solutions that minimize the sum of deviations from the best solutions for both total cost and throughput. Trade-off balancing optimization models may lead to better solutions, compared to traditional multi-objective optimization models. The results of this paper are applicable to manufacturing systems, where managers face multiple objectives and uncertainties in the system

Scheduling the hospital-wide flow of elective patients

In this paper, we address the problem of planning the patient flow in hospitals subject to scarce medical resources with the objective of maximizing the contribution margin. We assume that we can classify a large enough percentage of elective patients according to their diagnosis-related group (DRG) and clinical pathway. The clinical pathway defines the procedures (such as different types of diagnostic activities and surgery) as well as the sequence in which they have to be applied to the patient. The decision is then on which day each procedure of each patient’s clinical pathway should be done, taking into account the sequence of procedures as well as scarce clinical resources, such that the contribution margin of all patients is maximized. We develop two mixed-integer programs (MIP) for this problem which are embedded in a static and a rolling horizon planning approach. Computational results on real-world data show that employing the MIPs leads to a significant improvement of the contribution margin compared to the contribution margin obtained by employing the planning approach currently practiced. Furthermore, we show that the time between admission and surgery is significantly reduced by applying our models