Introduction

The health industry is undergoing enormous transformations with the pressure to reduce costs and improve the quality and efficiency of healthcare services (Wu, Kao, & Sambamurthy, 2016). The exponential growth of health data, the pressures to make continuous investments and the necessity to provide integrated care services meeting the healthcare needs of patients, are all good reasons for Hospital Information Systems adopt Data Analytics (DA) and thus, ensure reliable and efficient services. This situation becomes even more demanding because this enormous volume of health data does not only come from traditional interviews, hospitalizations, and medical tests in a hospital or outpatient clinic, but it involves data that patients collect themselves using wearables for telemonitoring and data that healthy people collect using a wide variety of health and wellbeing apps (Roesems-Kerremans, 2016). In addition, the emergence of new technologies followed by genetic information, has contributed to the increase of clinical data collected. Process and analyse all this information, allows to identify health patterns that can contribute to cure and prevention of diseases, besides improving patient safety and quality of life. In short, improving the efficiency, quality and savings of health systems. Therefore, new opportunities are emerging based on the rapid evolution of Big Data technologies and the enormous availability of data that organizations can capture (Raguseo, 2018). Data management and Analytics is critical in health information systems. Data management includes processes and technologies to acquire, store, prepare and retrieve data for analysis. Analytics, refers to techniques used to analyse and acquire intelligence from Big Data (Gandomi & Haider, 2015). The investigation suggests that organizations using DA, when managing decision-making processes, are more productive and profitable than those who do not (Mathews, 2015). However, it is not clear at the moment, to what extent organizations are already implementing Analytics, as there are still many challenges in this area (Lismont, Vanthienen, Baesens, & Lemahieu, 2017). In this sense, organizations that intend to increase the use of DA to optimize costs, profitability, productivity and quality should consider strategic investments in this field. Healthcare organizations are clearly no exception to this rule. Within the healthcare field, Hospitals have followed three stages of data computerization and management, namely: data collection, data sharing and (more recently and gradually) data analysis (Sanders, Burton, & Protti, 2013). The collection, storage and analysis of health data have been, are and will remain, fundamental procedures to providing efficient healthcare services, and their importance is increasing in line with the growing amount of health data collected every day (Roesems-Kerremans, 2016).