Prescriptive graph analytics on the digital transformation in healthcare through user-generated content
As they swiftly evolve and become widely adopted, new technologies are fundamentally transforming the landscape of business models. Digital transformation has become a top priority for business leaders, and the Covid-19 pandemic has significantly accelerated this trend. As a result, the healthcare i...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/67520 |
| Acceso en línea: | http://hdl.handle.net/10017/67520 https://dx.doi.org/10.1007/s10479-023-05495-z |
| Access Level: | acceso abierto |
| Palabra clave: | Healthcare Digital transformation Prescriptive analytics Natural language processing Topic modelling Informática Computer science |
| Sumario: | As they swiftly evolve and become widely adopted, new technologies are fundamentally transforming the landscape of business models. Digital transformation has become a top priority for business leaders, and the Covid-19 pandemic has significantly accelerated this trend. As a result, the healthcare industry is also rapidly evolving. The goal of this research is to understand the impact of digital transformation (DX) in the healthcare industry and identify the main trends, opportunities and challenges by leveraging user-generated content through Twitter analytics. The analysis of textual information allows for the generation of insights and prescriptive analytics from aggregated unstructured data. Between January 2017 and December 2021, 96,826 English-language tweets on digitisation and digital transformation in healthcare were collected. The method consisted of a series of experiments of co-occurrence network topic modelling, based on graph analytics and semantic analysis. The results are prescriptive on the development of patient-centric digital health. The results are linked to the development of personalised healthcare, mobile health (mhealth) and efficiencies derived from adopting technology, especially artificial intelligence, machine learning and cloud computing. However, certain challenges must be addressed to implement digital transformation strategies. These challenges include ensuring compliance with data privacy regulations, as well as managing the changes required in legacy systems and processes. |
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