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Pesqueira, A., Sousa, M. J., Rocha, Á. & Sousa, M. (2020). Data science in pharmaceutical industry. In Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira (Ed.), Trends and innovations in information systems and technologies: WorldCIST 2020. (pp. 144-154). Budva: Springer.

A. M. Pesqueira et al.,  "Data science in pharmaceutical industry", in Trends and innovations in information systems and technologies: WorldCIST 2020, Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira, Ed., Budva, Springer, 2020, vol. 1159, pp. 144-154

@inproceedings{pesqueira2020_1745740127557,
	author = "Pesqueira, A. and Sousa, M. J. and Rocha, Á. and Sousa, M.",
	title = "Data science in pharmaceutical industry",
	booktitle = "Trends and innovations in information systems and technologies: WorldCIST 2020",
	year = "2020",
	editor = "Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira",
	volume = "1159",
	number = "",
	series = "",
	doi = "10.1007/978-3-030-45688-7_15",
	pages = "144-154",
	publisher = "Springer",
	address = "Budva",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-45688-7"
}

TY  - CPAPER
TI  - Data science in pharmaceutical industry
T2  - Trends and innovations in information systems and technologies: WorldCIST 2020
VL  - 1159
AU  - Pesqueira, A.
AU  - Sousa, M. J.
AU  - Rocha, Á.
AU  - Sousa, M.
PY  - 2020
SP  - 144-154
SN  - 2194-5357
DO  - 10.1007/978-3-030-45688-7_15
CY  - Budva
UR  - https://link.springer.com/book/10.1007/978-3-030-45688-7
AB  - Data Science demand from Medical Affairs (MA) functions in the pharmaceutical industry are exponentially increasing, where business cases around more modern execution of activities and strategic planning are becoming a reality. MA is still lagging in terms of implementing data science and big data technology in the current times, which means a reflecting immaturity of capabilities and processes to implement these technologies better. This paper aims to identify possible gaps in the literature and define a starting point to better understand the application of Data Science for pharmaceutical MA departments through the identification and synthesis of data science criteria used in MA case studies as presented in the scientific literature. We applied a Systematic Literature Review of studies published up to (and including) 2017 through a database search and backward and forward snowballing. In total, we evaluated 2247 papers, of which 11 included specific data science methodologies criteria used in medical affairs departments. It was also made a quantitative analysis based on data from a questionnaire applied to Takeda, a Pharma organization. The findings indicate that there is good evidence in the empirical relation between Data Technostructure and Data Management dimensions of the Data Science strategy of the organization.
ER  -