## 4 Discussion  
As [PERSON] et al. explain, an efficient management, analysis and interpretation of health Big Data can open new avenues for modern healthcare [14]. Considering our results, we think it is time to take vigorous steps to integrate the analysis of biomedical, healthcare and economic data for better financial management of healthcare organizations. Otherwise, although personal health data in general have been considered a promising tool for the management of the healthcare system as a whole, widespread acceptance has yet to be achieved. A significant obstacle often cited as delaying the implementation of AI systems is the concern regarding the ability to properly ensure the security and privacy of this sensitive information [15]. There are already several well-known commercial solutions in hospitals, such as Enterprise Resource Planning (ERP), with powerful features to connect and manage all the processes that occur in a hospital, from the providers to the end users, including financial management. Those tools allow every aspect of a health service, past or present, to be analyzed in detail. However, they mainly rely on managers' experience to make future projections. It is also possible to build tailor-made software, using algorithmic solutions to process data to help managers to make more informed decisions. The inconvenience of this kind of solutions is that they need to figure out hundreds of rules that link patients' illnesses with the resources needed for their treatments. Because all this information changes and new treatments appear every year, evolving this set of rules could be a cumbersome job. On the other hand, working with Neural Networks, in most cases, implies just new training with the new data and testing their results. There is a growing interest in applying machine learning techniques to financial forecasting in the health services environment. A good example is the research study published in the present year (2022) by [PERSON] et al. [7]. In this study, machine learning was applied to predict the costs that a patient would incur based on their diagnosis. They used Deep Neural Networks using assurance companies' data. The main difference with \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**DRG**} & **Linear ANN** & **DNN** & **Decision Tree** \\  & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline
39 & 85.04\% & 85.08\% & 80.35\% \\ \hline
64 & 79.48\% & 79.48\% & 66.67\% \\ \hline
178 & 83.68\% & 83.68\% & 68.40\% \\ \hline
473 & 83.55\% & 83.55\% & 59.57\% \\ \hline
536 & 83.75\% & 83.75\% & 72.68\% \\ \hline
690 & 80.04\% & 80.04\% & 67.27\% \\ \hline
811 & 80.14\% & 80.14\% & 68.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Decision Tree results and comparison with Sequential Model and Deep Neural Network. respect to our article is that their target was costs prediction and our work is focused on billing forecasting. Moreover, their scope of work was insurance companies, while we are focused on the hospital level. Both research efforts reflect the strength of Artificial Intelligence in this area. [PERSON] and [PERSON] also recognized that current machine-learning algorithms implemented in building structural health monitoring systems are successful tools for health care systems and the care of patients and that the specific application of ANNs improves the reliability and performance of the systems [31]. Neural Network software has evolved and matured; it has reliable outputs with consistent environment and tools. We tested two designs: a basic Sequential Model and a more advanced Deep Neural Network with similarly good results in both cases, but with a slight advantage of the DNN, at a price of more complexity and longer execution times. At a first glance, it could be thought that it is not worth using it, but a deeper analysis showed that the DNN predictions better fitted the shape of the data. The Sequential Model is of little use if the relation between the variables is not linear, which can be verified by comparing Figures 3a and 6a.