Chemotherapy planning and multi-appointment scheduling: formulations, heuristics and bounds

The number of new cancer cases is expected to increase by about 50% in the next 20 years, and the need for chemotherapy treatments will increase accordingly. Chemotherapy treatments are usually performed in outpatient cancer centers where patients affected by different types of tumors are treated. The treatment delivery must be carefully planned to optimize the use of limited resources, such as drugs, medical and nursing staff, consultation and exam rooms, and chairs and beds for the drug infusion. Planning and scheduling chemotherapy treatments involve different problems at different decision levels. In this work, we focus on the patient chemotherapy multi-appointment planning and scheduling problem at an operational level, namely the problem of determining the day and starting time of the oncologist visit and drug infusion for a set of patients to be scheduled along a short-term planning horizon. We use a per-pathology paradigm, where the days of the week in which patients can be treated, depending on their pathology, are known. We consider different metrics and formulate the problem as a multi-objective optimization problem tackled by sequentially solving three problems in a lexicographic multi-objective fashion. The ultimate aim is to minimize the patient's discomfort. The problems turn out to be computationally challenging, thus we propose bounds and ad-hoc approaches, exploiting alternative problem formulations, decomposition, and $k$-opt search. The approaches are tested on real data from an Italian outpatient cancer center and outperform state-of-the-art solvers.Comment: 28 pages, 3 figure

the allergen mus m 1 0102 cysteine residues and molecular allergology

Abstract Mus m 1.0102 is a member of the mouse Major Urinary Protein family, belonging to the Lipocalins superfamily. Major Urinary Proteins (MUPs) are characterized by highly conserved structural motifs. These include a disulphide bond, involved in protein oxidative folding and protein structure stabilization, and a free cysteine residue, substituted by serine only in the pheromonal protein Darcin (MUP20). The free cysteine is recognized as responsible for the onset of inter- or intramolecular thiol/disulphide exchange, an event that favours protein aggregation. Here we show that the substitution of selected cysteine residues modulates Mus m 1.0102 protein folding, fold stability and unfolding reversibility, while maintaining its allergenic potency. Recombinant allergens used for immunotherapy or employed in allergy diagnostic kits require, as essential features, conformational stability, sample homogeneity and proper immunogenicity. In this perspective, recombinant Mus m 1.0102 might appear reasonably adequate as lead molecule because of its allergenic potential and thermal stability. However, its modest resistance to aggregation renders the protein unsuitable for pharmacological preparations. Point mutation is considered a winning strategy. We report that, among the tested mutants, C138A mutant acquires a structure more resistant to thermal stress and less prone to aggregation, two events that act positively on the protein shelf life. Those features make that MUP variant an attractive lead molecule for the development of a diagnostic kit and/or a vaccine

A model to prioritize access to elective surgery on the basis of clinical urgency and waiting time

<p>Abstract</p> <p>Background</p> <p>Prioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times. In addition, administrative and standardized data on waiting lists are generally lacking in Italy, where no detailed national reports are available. This is true although since 2002 the National Government has defined implicit Urgency-Related Groups (URGs) associated with Maximum Time Before Treatment (MTBT), similar to the Australian classification. The aim of this paper is to propose a model to manage waiting lists and prioritize admissions to elective surgery.</p> <p>Methods</p> <p>In 2001, the Italian Ministry of Health funded the Surgical Waiting List Info System (SWALIS) project, with the aim of experimenting solutions for managing elective surgery waiting lists. The project was split into two phases. In the first project phase, ten surgical units in the largest hospital of the Liguria Region were involved in the design of a pre-admission process model. The model was embedded in a Web based software, adopting Italian URGs with minor modifications. The SWALIS pre-admission process was based on the following steps: 1) urgency assessment into URGs; 2) correspondent assignment of a pre-set MTBT; 3) real time prioritization of every referral on the list, according to urgency and waiting time. In the second project phase a prospective descriptive study was performed, when a single general surgery unit was selected as the deployment and test bed, managing all registrations from March 2004 to March 2007 (1809 ordinary and 597 day cases). From August 2005, once the SWALIS model had been modified, waiting lists were monitored and analyzed, measuring the impact of the model by a set of performance indexes (average waiting time, length of the waiting list) and Appropriate Performance Index (API).</p> <p>Results</p> <p>The SWALIS pre-admission model was used for all registrations in the test period, fully covering the case mix of the patients referred to surgery. The software produced real time data and advanced parameters, providing patients and users useful tools to manage waiting lists and to schedule hospital admissions with ease and efficiency. The model protected patients from horizontal and vertical inequities, while positive changes in API were observed in the latest period, meaning that more patients were treated within their MTBT.</p> <p>Conclusion</p> <p>The SWALIS model achieves the purpose of providing useful data to monitor waiting lists appropriately. It allows homogeneous and standardized prioritization, enhancing transparency, efficiency and equity. Due to its applicability, it might represent a pragmatic approach towards surgical waiting lists, useful in both clinical practice and strategic resource management.</p

Tactical and operational decisions for operating room planning: efficiency and welfare implications

In this paper, we evaluate the impact on welfare implications of a 0-1 linear programming model to solve the Operating Room (OR) planning problem, taking a patient perspective. In particular, given a General Surgery Department made up of different surgical sub-specialties sharing a given number of OR block times, the model determines, during a given planning period, the allocation of those blocks to surgical sub-specialties, i.e. the so called Master Surgical Schedule Problem (MSSP), together with the subsets of elective patients to be operated on in each block time, i.e. the so called Surgical Case Assignment Problem (SCAP). The innovation of the model is two-fold. The first is that OR allocation is "optimal" if the available OR blocks are scheduled simultaneously to the proper sub-specialty, at the proper time to the proper patient. The second is defining what "proper" means and include that in the objective function. In our approach what is important is not number of patients who can be treated in a given period but how much welfare loss, due to clinical deterioration or other negative consequences related to excessive waiting, can be prevented. In other words we assume a societal perspective in that we focus on "outcome" (health improving or preventing from worsening) rather than on "output" (delivered procedures). The model can be used both to develop weekly OR planning with given resources (operational decision), and to perform "what if" scenario analysis regarding how to increase the amount of OR time available for the entire department (tactical decision). The model performance is verified by applying it to a real scenario, the elective admissions of the General Surgery Department of the San Martino University Hospital in Genova (Italy). Despite the complexity of this NP-hard combinatorial optimization problem, computational results indicate that the model can solve all test problems within 600 s and an average optimality tolerance of less than 0,01%

Liste di attesa: problemi aperti

Partendo dalla constatazione che, a seguito del d.p.c.m. 16 aprile 2002, il tempo di erogazione delle prestazioni è ormai una delle dimensioni dei Livelli essenziali di assistenza, il presente lavoro si propone di affrontare il problema della liste di attesa dal punto di vista sia teorico sia operativo. Dal primo punto di vista, la presenza di liste di attesa è un problema strutturale dei sistemi sanitari pubblici, peraltro ineliminabile. Ritardare l’erogazione della prestazione consente, infatti, di governare la domanda senza escludere nessuno dal trattamento, recuperando così efficienza anche in assenza di meccanismo di prezzo. Dal punto di vista operativo, l’approccio seguito è quello di raggruppare i pazienti per classi omogenee di attesa. Lo sviluppo di un modello di simulazione a eventi discreti ha permesso di valutare a priori gli effetti di alternativi scenari di gestione delle liste in termini di efficienza (riduzione del tempo “equivalente” di attesa complessivo) e di equità (diminuzione delle differenze tra i tempi “equivalenti” delle diverse classi). I risultati ottenuti sono particolarmente sensibili ai coefficienti di priorità sui quali sono necessari ulteriori approfondimenti

A pre-assignment heuristic algorithm for the Master Surgical Schedule Problem (MSSP)

In this paper a 0\u20131 linear programming model and a solution heuristic algorithm are developed in order to solve the so-called Master Surgical Schedule Problem (MSSP). Given a hospital department made up of different surgical units (i.e. wards) sharing a given number of Operating Rooms (ORs), the problem herein addressed is determining the assignment among wards and ORs during a given planning horizon, together with the subset of patients to be operated on during each day. Different resource constraints related to operating block time length, maximum OR overtime allowable by collective labour agreement and legislation, patient length of stay (LOS), available OR equipment, number of surgeons, number of stay and ICU beds, are considered. Firstly, a 0\u20131 linear programming model intended to minimise a cost function based upon a priority score, that takes into proper account both the waiting time and the urgency status of each patient, is developed. Successively, an heuristic algorithm that enables us to embody some pre-assignment rules to solve this NP-hard combinatorial optimisation problem, is presented. In particular, we force the assignment of each patient to a subset of days depending on his/her expected length of stay in order to allow closing some stay areas during the weekend and hence reducing overall hospitalisation cost of the department. The results of an extensive computational experimentation aimed at showing the algorithm efficiency in terms of computational time and solution effectiveness are given and analysed