Jonathan F. Bard

Kidney-paired donation programs assist patients in need of a kidney to swap their incompatible donor with another incompatible patient–donor pair for a suitable kidney in return. The kidney exchange problem (KEP) is a mathematical optimization problem that consists of finding the maximum set of matches in a directed graph representing the pool of incompatible pairs. Depending on the specific framework, these matches can come in the form of (bounded) directed cycles or directed paths. This gives rise to a family of KEP models that have been studied over the past few years. Several of these models require an exponential number of constraints to eliminate cycles and chains that exceed a given length. In this paper, we present enhancements to a subset of existing models that exploit the connectivity properties of the underlying graphs, thereby rendering more compact and tractable models in both cycle-only and cycle-and-chain versions. In addition, an efficient algorithm is developed for detecting violated constraints and solving the problem. To assess the value of our enhanced models and algorithm, an extensive computational study was carried out comparing with existing formulations. The results demonstrated the effectiveness of the proposed approach. For example, among the main findings for edge-based cycle-only models, the proposed (*PRE(i)) model uses a new set of constraints and a small subset of the full set of length-k paths that are included in the edge formulation. The proposed model was observed to achieve a more than 98% reduction in the number of such paths among all tested instances. With respect to cycle-and-chain formulations, the proposed (*ReSPLIT) model outperformed Anderson’s arc-based (AA) formulation and the path constrained-TSP formulation on all instances that we tested. In particular, when tested on a difficult sets of instances from the literature, the proposed (*ReSPLIT) model provided the best results compared to the AA and PC-based models.

This paper presents a two-stage approach for efficiently solving a weekly home healthcare scheduling and routing problem. Two new mixed-integer linear programming (MILP) models are proposed, where the first is used for making patient-therapist assignments over the week, and the second for deriving daily routes. In both MILPs, the objective function contains a hierarchically weighted set of goals. The major components of the full problem are continuity of care, downgrading, workload balance, time windows, overtime, and mileage costs. A new preprocessing procedure is developed to limit the service area of each therapist to a single group of overlapping patients. Once the groups are formed, weekly schedules are constructed with the MILPs. The overall objective is to minimize the number of unscheduled visits and total travel and service costs subject to the operational constraints mentioned above. Computational experiments are conducted with real data sets provided by a national home health agency. The results show that optimal solutions can be obtained quickly at both the assignment and routing stages and that they are comparable to the results obtained with a proposed integrated model. In either case, the corresponding schedules were better on all metrics when compared to the schedules used in practice.

The global home healthcare market is growing rapidly due to aging populations, advancements in healthcare technology, and patient preference for home-based care. In this paper, we study the multi-day planning problem of simultaneously deciding patient acceptance, assignment, routing, and scheduling under uncertain travel and service times. Our approach ensures cardinality-constrained robustness with respect to timely patient care and the prevention of overtime. We take into account a wide range of criteria including patient time windows, caregiver availability and compatibility, a minimum time interval between two visits of a patient, the total number of required visits, continuity of care, and profit. We use a novel systematic modeling scheme that prioritizes health-related criteria as hard constraints and optimizes cost and preference-related criteria as part of the objective function. We present a mixed-integer linear program formulation, along with a nested branch-and-price technique. Results from a case study in Austin, Texas demonstrate that instances of realistic size can be solved to optimality within reasonable runtimes. The price of robustness primarily results from reduced patient load per caregiver. Interestingly, the criterion of geographical proximity appears to be of secondary priority when selecting new patients and assigning them to caregivers.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

The paper presents a discrete-time mixed-integer linear programming (MILP) model for a generalised flexible job-shop scheduling problem as represented by a state-task network. The problem is characterised by reentrant flow, sequence-dependent changeover time, machine downtime, and skilled labour requirements. Two preprocessing procedures are proposed to reduce the size of the MILP model, and represent a major contribution of the research. The procedures reduce the number of assignment variables by exploiting job precedence and workforce qualifications. Machine availability for each task is determined as a function of possible start and end times, given duration, and maintenance schedule. The overall objective is to maximise the number of scheduled tasks while minimising their total finish time. Computational experiments are conducted with real and randomly generated instances. The results show that optimal solutions can be obtained for medium-size problems within a reasonable amount of time, primarily due to the use of the preprocessing procedures.

This paper presents original insights about the benefits of collaborative care in an internal medicine inpatient service at a Texas academic medical center. Collaborative care requires close and frequent coordination among all members of a patient care team - providers, case managers, social workers, specialists, and other clinical and professional staff. Hospitals and clinicians are concerned with whether the benefits accrued to patients and hospital operations outweigh the resource requirements imposed by this innovative integrated care approach. To investigate outcomes, the approach was implemented by one of its five teaching teams at the medical center hospital. Researchers then developed statistical models that suggested the effect of collaborative care on patient length of stay and discharge planning was favorable and statistically significant. Lasso and stepwise regression models were estimated that suggested a 9% decrease in expected length of stay –without increased readmissions – and an increased likelihood of being discharged before 2 pm to enable improved patient throughput and bed turnover. Additional patient survey data confirmed that patient satisfaction remained high for patients under the care of the collaborative care team. Discussions with hospitalists and nurses during and after implementation indicated that the new approach required a change in logistics and how patient care was performed. Our research suggests there is a corresponding ‘learning effect’ – the impact was more pronounced and significant once an attending physician had two or more separate rotations on the collaborative care team.

In this article, we study a coordinated scheduling problem with both Virtual Medicine patients (VM patients) and In-Clinic patients (IC patients) in a multi-disciplinary setting. The problem was motivated by appointment scheduling requirements in a multi-disciplinary clinic called an Integrated Practice Unit (IPU), which incorporates differing priorities, heterogeneous service time distributions, distinct cost structures and unique care paths in a multi-station network. We establish priority for IC patients and introduce time windows for VM patients to create flexibility. Recursion expressions are derived for a performance measure of interest, which balances revenue against clinic overtime and patient waiting time costs. We develop an approach where IC patients are scheduled first. To do so, we establish discrete convexity for a special tree-type directed network structure and generate near-optimal IC patient schedules for a more general acyclic directed network. Conditioned on IC patients’ schedule, we show that the VM patient scheduling problem has a discrete convexity property even in the presence of non-linear costs. Through numerical examples based on IPUs being implemented by the Dell Medical School at the University of Texas at Austin, we find that the introduction of VM patients can substantially improve system performance and patient access without adding resources.

<p>Supplementary Figure legend</p>

<p>Supplementary Fig 4. Intratumoral and plasma sunitinib concentrations</p>

<p>Supplementary Fig 3. Liver histology and body weights of mice on sunitinib dose escalation.</p>

<p>Supplementary Fig. 5. Histone marks expression associated with resistance to sunitinib</p>

<p>Supplementary Fig. 2. Tumor growth of RP-R-01 ccRCC xenograft treated with sunitinib and associated changes in EZH2, H3K27me3 and E-cadherin expression.</p>

<p>Supplementary Fig. 1. Characterization of RP-R-01 and RP-R-02 PDX models</p>

<p>Supplementary Fig. 6. Histone marks expression in sunitinib resistant RP-R-01 tumors</p>

<p>Supplementary Fig. 7. EZH2 and E-cadherin mRNA expression in 786-0shRNA scramble, 786-0shEZH2 (clone a and d), and 786-0 treated with GSK126</p>

<p>Supplementary Fig. 6. Histone marks expression in sunitinib resistant RP-R-01 tumors</p>

<p>Supplementary Fig 4. Intratumoral and plasma sunitinib concentrations</p>

<p>Supplementary Fig. 1. Characterization of RP-R-01 and RP-R-02 PDX models</p>

<p>Supplementary Fig. 7. EZH2 and E-cadherin mRNA expression in 786-0shRNA scramble, 786-0shEZH2 (clone a and d), and 786-0 treated with GSK126</p>

<p>Supplementary Fig. 2. Tumor growth of RP-R-01 ccRCC xenograft treated with sunitinib and associated changes in EZH2, H3K27me3 and E-cadherin expression.</p>

<p>Supplementary Fig 3. Liver histology and body weights of mice on sunitinib dose escalation.</p>

<p>Supplementary Fig. 5. Histone marks expression associated with resistance to sunitinib</p>

<p>Supplementary Figure legend</p>

<div>Abstract<p>Sunitinib is considered a first-line therapeutic option for patients with advanced clear cell renal cell carcinoma (ccRCC). Despite sunitinib's clinical efficacy, patients eventually develop drug resistance and disease progression. Herein, we tested the hypothesis whether initial sunitinib resistance may be transient and could be overcome by dose increase. In selected patients initially treated with 50 mg sunitinib and presenting with minimal toxicities, sunitinib dose was escalated to 62.5 mg and/or 75 mg at the time of tumor progression. Mice bearing two different patient-derived ccRCC xenografts (PDX) were treated 5 days per week with a dose-escalation schema (40–60–80 mg/kg sunitinib). Tumor tissues were collected before dose increments for immunohistochemistry analyses and drug levels. Selected intrapatient sunitinib dose escalation was safe and several patients had added progression-free survival. In parallel, our preclinical results showed that PDXs, although initially responsive to sunitinib at 40 mg/kg, eventually developed resistance. When the dose was incrementally increased, again we observed tumor response to sunitinib. A resistant phenotype was associated with transient increase of tumor vasculature despite intratumor sunitinib accumulation at higher dose. In addition, we observed associated changes in the expression of the methyltransferase EZH2 and histone marks at the time of resistance. Furthermore, specific EZH2 inhibition resulted in increased <i>in vitro</i> antitumor effect of sunitinib. Overall, our results suggest that initial sunitinib-induced resistance may be overcome, in part, by increasing the dose, and highlight the potential role of epigenetic changes associated with sunitinib resistance that can represent new targets for therapeutic intervention. <i>Mol Cancer Ther; 14(2); 513–22. ©2014 AACR</i>.</p></div>

<div>Abstract<p>Sunitinib is considered a first-line therapeutic option for patients with advanced clear cell renal cell carcinoma (ccRCC). Despite sunitinib's clinical efficacy, patients eventually develop drug resistance and disease progression. Herein, we tested the hypothesis whether initial sunitinib resistance may be transient and could be overcome by dose increase. In selected patients initially treated with 50 mg sunitinib and presenting with minimal toxicities, sunitinib dose was escalated to 62.5 mg and/or 75 mg at the time of tumor progression. Mice bearing two different patient-derived ccRCC xenografts (PDX) were treated 5 days per week with a dose-escalation schema (40–60–80 mg/kg sunitinib). Tumor tissues were collected before dose increments for immunohistochemistry analyses and drug levels. Selected intrapatient sunitinib dose escalation was safe and several patients had added progression-free survival. In parallel, our preclinical results showed that PDXs, although initially responsive to sunitinib at 40 mg/kg, eventually developed resistance. When the dose was incrementally increased, again we observed tumor response to sunitinib. A resistant phenotype was associated with transient increase of tumor vasculature despite intratumor sunitinib accumulation at higher dose. In addition, we observed associated changes in the expression of the methyltransferase EZH2 and histone marks at the time of resistance. Furthermore, specific EZH2 inhibition resulted in increased <i>in vitro</i> antitumor effect of sunitinib. Overall, our results suggest that initial sunitinib-induced resistance may be overcome, in part, by increasing the dose, and highlight the potential role of epigenetic changes associated with sunitinib resistance that can represent new targets for therapeutic intervention. <i>Mol Cancer Ther; 14(2); 513–22. ©2014 AACR</i>.</p></div>