Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making

Luigi Jesus Basile, Nunzia Carbonara, Roberta Pellegrino and Umberto Panniello

Technovation, 2023, vol. 120, issue C

Abstract: The pandemic has forced people to use digital technologies and accelerated the digitalization of many businesses. Using digital technologies generates a huge amount of data that are exploited by Business Intelligence (BI) to make decisions and improve the management of firms. This becomes particularly relevant in the healthcare sector where decisions are traditionally made on the physicians’ experience. Much work has been done on applying BI in the healthcare industry. Most of these studies were focused only on IT or medical aspects, while the usage of BI for improving the management of healthcare processes is an under-investigated field. This research aims at filling this gap by investigating whether a decision support system (DSS) model based on the exploitation of data through BI can outperform traditional experience-driven practices for managing processes in the healthcare domain. Focusing on the managing process of the therapeutic path of oncological patients, specifically BRCA-mutated women with breast cancer, a DSS model for benchmarking the costs of various treatment paths was developed in two versions: the first is experience-driven while the second is data-driven. We found that the data-driven version of the DSS model leads to a more accurate estimation of the costs that could potentially be prevented in the treatment of oncological patients, thus enabling significant cost savings. A more informed decision due to a more accurate cost estimation becomes crucial in a context where optimal treatment and unique clinical recommendations for patients are absent, thus permitting a substantial improvement of the decision making in the healthcare industry.

Keywords: Decision support system model; Decision making; Business intelligence; Healthcare; Oncology; Data-driven; Big data (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:120:y:2023:i:c:s0166497222000293

DOI: 10.1016/j.technovation.2022.102482

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