Rosita Guido

Operational research in the management of the operating theatre: a survey

Operating theatre represents one of the most critical and expensive hospital resources since a high percentage of the hospital admissions is due to surgical interventions. The main objectives are to guarantee the optimal utilization of medical resources, the delivery of surgery at the right time, the maximisation of profitability (i.e., patient flow) without incurring additional costs or excessive patient waiting time. The operating theatre management is a process very complex: the use of mathematical and simulation models, and quantitative techniques plays, thus a crucial role. The main aim of this paper is to provide a structured literature review on how Operational Research can be applied to the surgical planning and scheduling processes. A particular attention is on the published papers that present the most interesting mathematical (optimization and simulation) models and solution approaches developed to address the problems arising in operating theatres. Directions for future researches are also highlighted.

Optimization models for radiotherapy patient scheduling

The efficient radiotherapy patient scheduling, within oncology departments, plays a crucial role in order to ensure the delivery of the right treatment at the right time. In this context, generating a high quality solution is a challenging task, since different goals (i.e., all the activities are scheduled as soon as possible, the patient waiting time is minimized, the device utilization is maximized) could be achieved and a large set of constraints (i.e., every device can be used by only one patient at time, the treatments have to be performed in an exact time order) should be taken into account. We propose novel optimization models dealing with the efficient outpatient scheduling within a radiotherapy department defined in such a way to represent different real-life situations. The effectiveness of the proposed models is evaluated on randomly generated problems and on a real case situation. The results are very encouraging since the developed optimization models allow to overcome the performance of human experts (i.e., the number of patients that begin the radiotherapy treatment is maximized).

Non-block scheduling with priority for radiotherapy treatments

In this paper, a quite challenging operational problem within health care delivery has been considered: the optimal management of patients waiting for radiotherapy treatments. Long waiting times for radiotherapy treatments of several cancers are largely documented all over the world. This problem is mainly due to an imbalance between supply and demand of radiotherapy services, which negatively affects the effectiveness and the efficiency of the health care delivered. Within this context, the paper presents an innovative solution approach for effectively scheduling a set of patients waiting to start the radiotherapy plan. The proposed approach is based on a well tailored integer linear optimization program, modelling a non-block scheduling strategy, with the aim to minimize the mean waiting time or maximize the number of new scheduled patients. The model has been tested and evaluated by carrying out some numerical experiments on suitable use-case scenarios, and the obtained results demonstrate the effectiveness and reliability of the proposed approach.

Kernel based support vector machine via semidefinite programming: Application to medical diagnosis

Support vector machine (SVM) is a well sound learning method and a robust classification procedure. Choosing a suitable kernel function in SVM is crucial for obtaining good performance; the difficulty is how to choose a suitable data transformation for the given problem. To this end, multiple kernel matrices, each of them corresponding to a given similarity measure, can be linearly combined. In this paper, the optimal kernel matrix, obtained as linear combination of known kernel matrices, is generated using a semidefinite programming approach. A suitable model formulation assures that the obtained kernel matrix is positive semidefinite and is optimal with respect to the dataset under consideration. The proposed approach has been applied to some very important medical diagnostic decision making problems and the results obtained by carrying out preliminary numerical experiments demonstrated the effectiveness of the proposed solution approach.

An optimal decision-making approach for the management of radiotherapy patients

In this paper, novel integer programming formulations are developed for solving the optimal scheduling of patients waiting for radiotherapy treatment. In this specific clinical domain, the suitable management and control of a patients’ waiting list strongly affect both the quality of the therapeutical outcome, in terms of effectiveness, and the cost-saving use of the overall therapeutical resources, in terms of efficiency. The proposed models allow the best scheduling strategy to be devised by taking into account the quality of the health care service offered to the patient as well as the status and the preferences of the patient. The computational experiments, carried out on realistic scenarios and considering real data, are very promising and show the efficiency and robustness of the proposed models to address the problem under consideration.

Taming the complexity of biological pathways through parallel computing

Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.

An optimal decision making model for supporting week hospital management

Week Hospital is an innovative inpatient health care organization and management, by which hospital stay services are planned in advance and delivered on week-time basis to elective patients. In this context, a strategic decision is the optimal clinical management of patients, and, in particular, devising efficient and effective admission and scheduling procedures, by tackling different requirements such as beds’ availability, diagnostic resources, and treatment capabilities. The main aim is to maximize the patient flow, by ensuring the delivery of all clinical services during the week. In this paper, the optimal management of Week Hospital patients is considered. We have developed and validated an innovative integer programming model, based on clinical resources allocation and beds utilization. In particular, the model aims at scheduling Week Hospital patients’ admission/discharge, possibly reducing the length of stay on the basis of an available timetable of clinical services. The performance of the model has been evaluated, in terms of efficiency and robustness, by considering real data coming from a Week Hospital Rheumatology Division. The experimental results have been satisfactory and demonstrate the effectiveness of the proposed approach.

Kernel-based Support Vector Machine classifiers for early detection of myocardial infarction

In this paper, we describe the development of kernel-based Support Vector Machine (SVM) classifiers to aid the early diagnosis of acute myocardial infarction (AMI). In particular, we have to recognize if a chest pain, complained by the patient, may be considered the sign of a myocardial infarction or it is the evidence of some other causes. This is a quite difficult medical decision problem, since chest pain is characterized by low specificity (typical values between 30% and 40%) as a symptom associated with myocardial infarction. Moreover, in order to make an objective and accurate diagnosis, the physician has to evaluate a large set of data coming from the patient. These aspects motivated the use of machine learning methodologies, with the aim to support the physician and increase the quality of the diagnostic decision. To this end, we formulated the medical decision problem as a supervised binary classification problem (AMI class and not AMI class), by developing a training set with 242 cases (130 in the AMI class and 112 in the not AMI class), each case characterized by a set of 105 features. We also considered a feature selection procedure, by selecting 25 of the 105 features. By the framework of generalized SVM model, we tested and validated the behavior of three kernel functions: Polynomial, Gaussian and Laplacian. By running a 10-fold cross validation procedure, the performance of the best tested classifier was 97.5%. By the same 10-fold cross validation procedure, we tested linear and quadratic discriminant analysis classifiers, with testing correctness of 86.8% and 94%, respectively. The numerical results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem.

Beneficial effects of iodized salt prophylaxis on thyroid volume in an iodine deficient area of southern Italy

Objective and subjects  Goitre prevalence in school‐age children is an indicator of the severity of iodine deficiency disorders (IDD) in an endemic area. The aims of the present study were (i) to provide ultrasound thyroid volume (TV) reference values in a healthy population of school‐children aged 11–14 year living in iodine‐sufficient areas of Calabria region (ii) to assess both goitre prevalence and urinary iodine (UI) concentration in all children aged 11–14 year from four mildly iodine‐deficient areas in which we have carried out a program of salt iodization and (iii) to evaluate the efficacy of the iodoprophylaxis in an adult population living in a small village of the same endemic area.

A multi-objective block scheduling model for the management of surgical operating rooms: New solution approaches via genetic algorithms

In this paper, a multi-objective optimization model tackling the optimal planning and scheduling of surgical operations is proposed. The model determines the assignment of time slots to the surgical teams and schedules the elective inpatient surgical operations on the basis of clinical priorities. The proposed multi-objective approach takes into account and suitable balances some strategic and conflicting goals, related to the improvement of resources utilization and considering patients priority value. In order to find the Pareto frontier, a metaheuristic approach based on the efficient implementation of genetic algorithms has been proposed. This approach allows to obtain the set of efficient solutions within reasonable computational time. Some preliminary experiments based on real-life data are reported. The results demonstrate the effectiveness of the proposed approach, even though more extensive testing is necessary in order to finally assess and validate its impact to health care systems.