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A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists

A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists

Marisa Esteves, Márcia Esteves, António Abelha, José Machado
Copyright: © 2019 |Volume: 7 |Issue: 2 |Pages: 19
ISSN: 2155-5621|EISSN: 2155-563X|EISBN13: 9781522567516|DOI: 10.4018/IJPHIM.2019070102
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MLA

Esteves, Marisa, et al. "A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists." IJPHIM vol.7, no.2 2019: pp.14-32. http://doi.org/10.4018/IJPHIM.2019070102

APA

Esteves, M., Esteves, M., Abelha, A., & Machado, J. (2019). A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists. International Journal of Privacy and Health Information Management (IJPHIM), 7(2), 14-32. http://doi.org/10.4018/IJPHIM.2019070102

Chicago

Esteves, Marisa, et al. "A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists," International Journal of Privacy and Health Information Management (IJPHIM) 7, no.2: 14-32. http://doi.org/10.4018/IJPHIM.2019070102

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Abstract

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.

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