## 1 Introduction  
One of the main elements of current welfare systems are health services, which represent one of the largest items in state budgets, so monitoring and predicting the necessary investment for medical and pharmaceutical services is a challenging and difficult task, but of great interest to both public and private institutions [1]. In the article, we propose to study how to improve the adoption of sound decisions in the management of hospital institutions, which includes a set of decisions aimed at achieving defined objectives to improve health provision and establish better methods to support these decisions using Artificial Intelligence methods [2, 3]. We need new instruments because health services are extremely complex and expensive, especially since professionals require a high level of education and experience [4] and due to the expense of the necessary technical means: real estate, medical and surgical examination equipment, and food and laundry services, among others. In addition, these services must be provided 24 hours a day, 7 days a week. Health care costs grow every year, and financial management can be seriously affected by many circumstances. Events such as the COVID-19 pandemic can devastate even the best financial planning. Predictive tools are, therefore, crucial for optimal management and decision making, and as [PERSON] et al. point out, this is especially important in the health sector. These authors focus on the analysis of the management of health resources in primary care centers [5]. However, it is an example of the interest in studying the issue in hospitals, more complex structures where it is especially important to have information in advance in order to anticipate and be able to optimize resources, which are always scarce but essential in improving patient care. The search for models to predict the management and prices of financial markets is a highly researched topic, despite its complexity and the fact it is difficult to predict over time [6]. So, we propose applying similar processes and techniques already investigated(such as machine learning) in health institutions, even though there are still few proposals on the issue [7]. Health services generate a large number of data, which in many cases can be used to extract higher-quality information, which allows better informed decisions to be made. A new approach using Artificial Intelligence (AI) can be used in this changing environment [8]. Modeling data and making predictions based on available data can help medical administrators and improve the decision process. AI and Big Data will likely become powerful differentiators for health institutions and will likely enable reaching new levels of precision in management, service improvement and user satisfaction. Hospital costs and resource management are widely discussed topics [1]. There are two main approaches for their analysis: bottom-up or top-down. The bottom-up method is generally more widely used and is a model where all hospital elements are objects (patients, services, etc.) and, in general, are structured hierarchically. IT technologies have been an essential tool for managing all aspects of health services. Artificial Intelligence has a long history [9]. It is a set of advanced mathematical technologies, which create data models and apply sophisticated algorithms on these data, building ingenious solutions to solve difficult problems [10]. They can identify data patterns and classify them, which allows large amounts of information to be processed. Recently, it has being used with increasing success, covering many aspects and improving the control and output of processes. At present, the line of research in which AI does not intervene is rare. In this paper, we use Artificial Neural Networks (ANNs), to implement the proposed solution, because there are studies that have shown interest in applying this method to health data.