This paper proposes the development of a pervasive Web application based on business intelligence clinical indicators created with the data stored into the health information systems of a Portuguese health institution in the last 3 years i.e. between the beginning of 2015 and the end of 2017. With this computational tool, it is principally intended to reduce the number of appointments, surgeries, and medical examinations that were not carried out in the hospital most likely due to forgetfulness since most patients who attend this health institution are elderly people and memory loss is very common with increasing age. Therefore, patients and/or their caregivers and family members are alerted via SMS in advance and appropriately by health professionals through the Web application. This alternative is cheaper, faster, and more customizable than sending those SMS using a smartphone. Advantages liked with the use of this solution also include decreasing losses concerning time, human resources, and money.

Over the past few years, the rapidly aging population has been posing several challenges to healthcare systems worldwide. Consequently, in Portugal, nursing homes have been getting a higher demand, and health professionals working in these facilities are overloaded with work. Moreover, the lack of health information and communication technology (HICT) and the use of unsophisticated methods, such as paper, in nursing homes to clinically manage residents lead to more errors and are time-consuming. Thus, this article proposes a proof of concept of a mobile health (mHealth) application developed for the health professionals working in a Portuguese nursing home to support them at the point-of-care, namely to manage and have access to information and to help them schedule, perform, and digitally record their tasks. Additionally, clinical and performance business intelligence (BI) indicators to assist the decision-making process are also defined. Thereby, this solution aims to introduce technological improvements into the facility to improve healthcare delivery and, by taking advantage of the benefits provided by these improvements, lessen some of the workload experienced by health professionals, reduce time-waste and errors, and, ultimately, enhance elders’ quality of life and improve the quality of the services provided.

Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

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