Continuous cost growth in the healthcare sector is one of the most critical challenges. Several scientists are trying to provide solutions that will include quality of care and cost reduction. First, computerization and then technological digitization has made it possible to store an increasing amount of data and leverage it to improve quality and reduce costs. Although the topic is relevant, no article has expressed how data quality resulting from healthcare innovation can be crucial for lowering healthcare costs. Using the bibliometric approach and benefiting from Zupic and Cater methodological paper, the analysis investigates 159 peer-reviewed English papers. Additionally, the Bibliometrix R package is used in the data analysis part. The results confirm a multidisciplinary literature stream with a few unrelated process variables. We provide evidence of authors, journals, keywords, geographic areas of reference and a framework that links the two research streams. Finally, we argue that a structured data quality process helps value healthcare data adequately and reduces costs. Moreover, quality is fundamental before applying advanced data analytics through big data analytics, IoT and artificial intelligence applications.

Data quality for health sector innovation and accounting management: a twenty-year bibliometric analysis

Silvana Secinaro;Valerio Brescia;Davide Calandra;Paolo Biancone
2021

Abstract

Continuous cost growth in the healthcare sector is one of the most critical challenges. Several scientists are trying to provide solutions that will include quality of care and cost reduction. First, computerization and then technological digitization has made it possible to store an increasing amount of data and leverage it to improve quality and reduce costs. Although the topic is relevant, no article has expressed how data quality resulting from healthcare innovation can be crucial for lowering healthcare costs. Using the bibliometric approach and benefiting from Zupic and Cater methodological paper, the analysis investigates 159 peer-reviewed English papers. Additionally, the Bibliometrix R package is used in the data analysis part. The results confirm a multidisciplinary literature stream with a few unrelated process variables. We provide evidence of authors, journals, keywords, geographic areas of reference and a framework that links the two research streams. Finally, we argue that a structured data quality process helps value healthcare data adequately and reduces costs. Moreover, quality is fundamental before applying advanced data analytics through big data analytics, IoT and artificial intelligence applications.
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http://riviste.paviauniversitypress.it/index.php/ea/article/view/2079
Data quality; Health sector; Costs; Innovation; Accounting; Management
Silvana Secinaro, Valerio Brescia, Davide Calandra, Paolo Biancone
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1829298
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