# Forecast of Medical Costs in Health Companies Using Models Based on Advanced Analytics
## 1 Introduction  
Healthcare is one of the largest industries and services of the global economy, one that has been significantly increasing until it becoming one of the biggest challenges of our time [1]. According to the World Health Organization (WHO), healthcare represented 7.56% of Europe's gross domestic product (GDP) in 2015 [2]. In 2018, the total healthcare expenditure of the United States was 16.8% of its GDP (the highest in the world) (WHO-GDP) [2]. The national healthcare expenditure of the United States in 2018 was USD 3.8 trillion, but forecasts show that these costs will increase up to USD 6.2 trillion dollars by 2028 [3]. Among others, one reason for this increase is the misuse of medication and the duplication of procedures by doctors [4]. In Colombia, according to the National Government, public health issues have been prioritized to guarantee equality; thus, for 2020, the budget was USD 8 billion, with an 8.12% increase since 2019, when it was USD 7.45 billion [5, 6]. In this sense, the public health sector became one of the national sectors with the highest allocation of resources in the national budget. To be in line with the General Health and Social Security System (SGSSS), the Keralty organization, one of the main actors in the Colombian health system [7], designed theintegrated care model from a four-goal perspective: (i) the value generated by interventions relative to health results; (ii) the experience of assisting people; (iii) cost-efficiency (sustainability, adequate and smart use of resources); and (iv) the experience and involvement of health teams that perform interventions--all this within a framework in which the focus is the care provided to people and the individual and aggregated results obtained for the incurred costs (efficiency). The Colombian health system has two regimes: public and private. The Public Social Security and Health Regime (General Health and Social Security System, SGSSS) provides universal health coverage to the entire Colombian population and access to basic quality healthcare through the payment of fair premiums. The efficiency and quality of the service are the foremost priorities: the regime intends to improve health conditions by allocating resources for primary care, prevention in rural and vulnerable areas, and making sure that all health services meet the highest possible standards based on the available resources [8]. The general social security system has two regimes: contributive and subsidized. The contributive regime covers formal workers, pensioners, and independent workers, while the subsidized plan covers any other person who cannot afford it [9; 10]. In the private health regime, people voluntarily choose a private and supplemental health insurance policy once they have fulfilled their economical obligation to contribute to the SGSSS [9]. In the private health regime, prepaid health service companies finance the risk that a person may face when getting sick. This means that a person voluntarily selects a healthcare plan to pay in advance for any type of expenses related to an eventual sickness. This means that the client agrees to pay a fee for the service, and the company must issue a financing contract with the coverage conditions of the plan and the corresponding rate. Both the public and private regimes require the anticipation of medical costs to facilitate their planning. The implementation of advanced analytics projects allows the different companies of the Keralty group to anticipate potential changes in medical costs [7]. This article presents two proposals based on the exploration of variables such as comorbidities, seniority, residence, age, gender, and economic situation, among others. It is critical to understand how to project costs. First, prediction through LSTM networks and the use of grouping by characteristics allows segmenting the population to project the costs of a particular population using LSTM networks. LSTM stands for \"long short-term memory\", introduced as an improved RNN algorithm in 1997 [11].