Background Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce. Objectives Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery. Methods Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT). Results The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and
A reduction in the length of hospital stay and costs for the care of patients undergoing cardiac surgery according to the fast-track protocol was observed.
Goal: the main objective of this study is to analyze the behavior of the outpatient department of a large public hospital specialized in cardiology, understanding how the components of this system are related, in order to improve the hospital's performance. Design / Methodology / Approach: a case study was carried out in a public hospital specializing in cardiology with the aid of Modeling and Simulation of System Dynamics. Results: the result showed that variables such as doctor availability and average consultation time have great influence on the service capacity. Limitations of the investigation: the proceedings and times related to the medical staff are particular to each team and they are not standardized. However, in the system dynamics modeling these particularities cannot be included. Practical implications: for theory, there is the state-of-the-art development in terms of how to manage and regarding the methodologies should be applied in a complex referential model composed of several moderating variables, in order to obtain the best use of the available resources (human and material) of the hospital. For practice, the flow of patients in the hospital should be predicted and optimized, adding value to the services provided to its users. Originality / Value: the originality of the work is based on the unprecedented application of quantitative methods for solving problems in Brazilian hospitals.
This paper presents a simulation of an ambulatory processes using timed Petri net (TPN). The simulation considers the flow of patients in the biggest Brazilian cardiology hospital. The TPN is used as a decision support system (DSS) to improve the processes, to reduce the waiting time of the patients in the ambulatory and in this way to assure a high-quality service to the patients. Simulations were carried out using the software Visual Object Net++. This is a free software and therefore the presented solution is a low-cost solution. Providing a low-cost solution has a huge importance in this work since the hospital is kept from the government efforts and operates with limited financial resources. The patients’ flow in the hospital can be faced as a service and the modelling and optimization of these services bring more efficiency to the system as well as improve the human factors involved. The results proved that some changes could be made in the processes to improve the performance of the system.
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