In the last years, the increase of the average waiting times in waiting lists has been an issue felt in several health institutions worldwide. Therefore, this problematic situation creates the need to define and implement new administrative measures in order to improve the management of these organizations. In this context, this research project arose in an attempt to support the decision-making process in waiting lists, namely medical appointments and surgeries, in a hospital located in the north of Portugal. Hereupon, a pervasive business intelligence platform was designed and developed using recent technologies such as React, Node.js, and MySQL. The proposed information technology artifact allows the efficient and easy identification in real-time of average waiting times outside the outlined patterns. Thus, the aim is to enable the reduction of average waiting times through the analysis of business intelligence indicators in order to ensure patients' satisfaction by taking necessary and adequate measures.
TopIntroduction
Over the past few years, business intelligence (BI) has increasingly become a major interest to health information and communication technology (HICT) professionals due to its undeniable applicability in the electronic health record (Bonney, 2013). In short, it is a process of extraction, collection, storage, processing, analysis, and access to data from information systems in order to support and improve the decision-making process (Chaudhuri, Dayal, & Narasayya, 2011; Hočevar & Jaklič, 2010).
On the other hand, there has been a steady increase of waiting lists in health institutions worldwide and, subsequently, their average waiting times (Ballini et al., 2015; Barros, 2008; Miyanji et al., 2015; Moscelli, Siciliani, & Tonei, 2016; Odorico, 2014). A potential explanation for this increase could be the significant advances in the processes used in surgeries, namely anaesthesia procedures (Barros, 2008). These advances have greatly improved the efficiency and safety of the surgical procedures offered by health institutions. Consequently, there have been important increases in the demand for the realization of surgeries.
Nonetheless, the existence of huge waiting lists in hospitals can be seen as a result of the inability of a given healthcare system to respond to the requests of patients (O. R. F. Oliveira, 2012). Therefore, administrative measures are necessary to improve the management of organizations since delays can cause serious adverse consequences to patients’ health (Ballini et al., 2015; Barros, 2008; Miyanji et al., 2015; Moscelli et al., 2016; Odorico, 2014).
In this context, this research project used as a case study a Portuguese hospital located in the north of the country. In this health institution, there was not a clinical decision support system (CDSS) implemented that could use the clinical data stored in the health information systems in order to create clinical and performance BI indicators regarding waiting lists, namely medical appointments and surgeries. These indicators can be very useful to support health professionals in their decision-making process.
Thus, after a succinct analysis of the clinical data available and several meetings with health professionals of the hospital, a pervasive business intelligence platform was designed and developed. The definitions of its features was mainly based on the data collection conducted through these meetings. Its design and development processes also included the construction of a data warehouse (DW) of waiting lists with the data stored in its health information systems. Then, indicators were created from such data and integrated into the Web-based healthcare solution.
The core module of the BI platform is the Business Intelligence module that enables the visualization of clinical and performance indicators. Therefore, it is possible to visualize many indicators that can assist health professionals in the evaluation of a given healthcare process but also to help them monitor and evaluate the quality provided by the organization according to different parameters, including the medical services delivered to patients. Thereby, it can alert its users and guide them through the improvement process of the medical services provided.
Additionally, it is relevant to note that the Web-based healthcare solution presented and discussed in this manuscript is a novel version of a solution already previously proposed (Marisa Esteves, Miranda, & Abelha, 2018). Nevertheless, the research methodologies followed and software development technologies used were upgraded due to recent scientific advances. Promising new results were also achieved since several new indicators were created.
Finally, this section introduces the research project conducted and sums up the main motivations behind it. The next section of this manuscript presents the state of the art associated with this study, that is, the section “Background”. Thereafter, in the section “Research Strategies and Methods”, the research methodologies adopted are briefly described, namely the design science research (DSR), Kimball lifecycle, and proof of concept (PoC) methodologies. The section “Software Development Technologies” presents and discusses each technology chosen to develop the system. Thereafter, the results are presented in the section “Results”, including the architecture of the Web application and the BI indicators created. This section is followed by the “Proof of Concept” section, which consists essentially in a brief discussion and strengths, weaknesses, opportunities, and threats (SWOT) analysis in order to defend the feasibility and viability of the proposed solution. The “Conclusion and Future Work” section concludes this manuscript with the main contributions of this study and insights regarding potential future work.