Waiting time for radiotherapy in women with cervical cancer

ABSTRACT OBJECTIVE To describe the waiting time for radiotherapy for patients with cervical cancer. METHODS This descriptive study was conducted with 342 cervical cancer cases that were referred to primary radiotherapy, in the Baixada Fluminense region, RJ, Southeastern Brazil, from October 1995 to August 2010. The waiting time was calculated using the recommended 60-day deadline as a parameter to obtaining the first cancer treatment and considering the date at which the diagnosis was confirmed, the date of first oncological consultation and date when the radiotherapy began. Median and proportional comparisons were made using the Kruskal Wallis and Chi-square tests. RESULTS Most of the women (72.2%) began their radiotherapy within 60 days from the diagnostic confirmation date. The median of this total waiting time was 41 days. This median worsened over the time period, going from 11 days (1995-1996) to 64 days (2009-2010). The median interval between the diagnostic confirmation and the first oncological consultation was 33 days, and between the first oncological consultation and the first radiotherapy session was four days. The median waiting time differed significantly (p = 0.003) according to different stages of the tumor, reaching 56 days, 35 days and 30 days for women whose cancers were classified up to IIA; from IIB to IIIB, and IVA-IVB, respectively. CONCLUSIONS Despite most of the women having had access to radiotherapy within the recommended 60 days, the implementation of procedures to define the stage of the tumor and to reestablish clinical conditions took a large part of this time, showing that at least one of these intervals needs to be improved. Even though the waiting times were ideal for all patients, the most advanced cases were quickly treated, which suggests that access to radiotherapy by women with cervical cancer has been reached with equity.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Consequences of the Prolonged Waiting Time for Patients Candidates for Heart Surgery

OBJECTIVE - To assess mortality and the psychological repercussions of the prolonged waiting time for candidates for heart surgery. METHODS - From July 1999 to May 2000, using a standardized questionnaire, we carried out standardized interviews and semi-structured psychological interviews with 484 patients with coronary heart disease, 121 patients with valvular heart diseases, and 100 patients with congenital heart diseases. RESULTS - The coefficients of mortality (deaths per 100 patients/year) were as follows: patients with coronary heart disease, 5.6; patients with valvular heart diseases, 12.8; and patients with congenital heart diseases, 3.1 (p<0.0001). The survival curve was lower in patients with valvular heart diseases than in patients with coronary heart disease and congenital heart diseases (p<0.001). The accumulated probability of not undergoing surgery was higher in patients with valvular heart diseases than in the other patients (p<0.001), and, among the patients with valvular heart diseases, this probability was higher in females than in males (p<0.01). Several patients experienced intense anxiety and attributed their adaptive problems in the scope of love, professional, and social lives, to not undergoing surgery. CONCLUSION - Mortality was high, and even higher among the patients with valvular heart diseases, with negative psychological and social repercussions.

Incorporating waiting time in competitive location models: formulations and heuristics

In this paper we propose a metaheuristic to solve a new version of the Maximum Capture Problem. In the original MCP, market capture is obtained by lower traveling distances or lower traveling time, in this new version not only the traveling time but also the waiting time will affect the market share. This problem is hard to solve using standard optimization techniques. Metaheuristics are shown to offer accurate results within acceptable computing times.

Probabilistic maximal covering location-allocation models with constrained waiting time or queue length for congested systems

When dealing with the design of service networks, such as healthand EMS services, banking or distributed ticket selling services, thelocation of service centers has a strong influence on the congestion ateach of them, and consequently, on the quality of service. In this paper,several models are presented to consider service congestion. The firstmodel addresses the issue of the location of the least number of single--servercenters such that all the population is served within a standard distance,and nobody stands in line for a time longer than a given time--limit, or withmore than a predetermined number of other clients. We then formulateseveral maximal coverage models, with one or more servers per service center.A new heuristic is developed to solve the models and tested in a 30--nodesnetwork.

Incorporating waiting time in competitive location models: Formulations and heuristics

In this paper we propose a metaheuristic to solve a new version of the Maximum CaptureProblem. In the original MCP, market capture is obtained by lower traveling distances or lowertraveling time, in this new version not only the traveling time but also the waiting time willaffect the market share. This problem is hard to solve using standard optimization techniques.Metaheuristics are shown to offer accurate results within acceptable computing times.

Fronts with continuous waiting-time distributions: Theory and application to virus infections

We generalize to arbitrary waiting-time distributions some results which were previously derived for discrete distributions. We show that for any two waiting-time distributions with the same mean delay time, that with higher dispersion will lead to a faster front. Experimental data on the speed of virus infections in a plaque are correctly explained by the theoretical predictions using a Gaussian delay-time distribution, which is more realistic for this system than the Dirac delta distribution considered previously [J. Fort and V. Méndez, Phys. Rev. Lett.89, 178101 (2002)]

Small- and waiting-time behaviour of the thin-film-equation

We consider the small-time behavior of interfaces of zero contact angle solutions to the thin-film equation. For a certain class of initial data, through asymptotic analyses, we deduce a wide variety of behavior for the free boundary point. These are supported by extensive numerical simulations. © 2007 Society for Industrial and Applied Mathematics

Reducing voter waiting time : how to allocate voting machines to the polls.

Mode of access: Internet.

Optimization of waiting time at toll plazas

Toll plazas have several toll payment types such as manual, automatic coin machines, electronic and mixed lanes. In places with high traffic flow, the presence of toll plaza causes a lot of traffic congestion; this creates a bottleneck for the traffic flow, unless the correct mix of payment types is in operation. The objective of this research is to determine the optimal lane configuration for the mix of the methods of payment so that the waiting time in the queue at the toll plaza is minimized. A queuing model representing the toll plaza system and a nonlinear integer program have been developed to determine the optimal mix. The numerical results show that the waiting time can be decreased at the toll plaza by changing the lane configuration. For the case study developed an improvement in the waiting time as high as 96.37 percent was noticed during the morning peak hour.

Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.