Business Intelligence applied to Emergency Medical Services in the Lombardy region during SARS-CoV-2 epidemic

Business Intelligence applied to Emergency Medical Services in the Lombardy region during SARS-CoV-2 epidemic

Authors

  • Giuseppe Maria Sechi Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • Maurizio Migliori Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • Gabriele Dassi Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • Andrea Pagliosa Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • Rodolfo Bonora Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • Aurea Oradini-Alacreu School of Public Health, Faculty of Medicine, University Vita-Salute San Raffaele, Milan – Italy http://orcid.org/0000-0003-2975-6859
  • Anna Odone School of Public Health, Faculty of Medicine, University Vita-Salute San Raffaele, Milan – Italy http://orcid.org/0000-0002-5657-9774
  • Carlo Signorelli School of Public Health, Faculty of Medicine, University Vita-Salute San Raffaele, Milan – Italy
  • Alberto Zoli Azienda Regionale Emergenza Urgenza, Lombardy, Milan – Italy
  • AREU COVID-19 Response Team Azienda Regionale Emergenza Urgenza, Lombardy, Milan

Keywords:

Business Intelligence; Emergency Medical Services; SARS-CoV-2; COVID-19; coronavirus; Italy

Abstract

Background and aim of the work: On the 21st of February, the first patient was tested positive for SARS-CoV-2 at Codogno hospital in the Lombardy region. From that date, the Regional Emergency Medical Services (EMS) Trust (AREU) of the Lombardy region decided to apply Business Intelligence (BI) to the management of EMS during the epidemic. The aim of the study is to assess in this context the impact of BI on EMS management outcomes. Methods: Since the beginning of the COVID-19 outbreak, AREU is using BI daily to track the number of first aid requests received from 112. BI analyses the number of requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview. Moreover, BI allows identifying the numerical trend of episodes in each municipality (increasing, stable, decreasing). Results: AREU decides to reallocate in the territory the resources based on real-time data recorded and elaborated by BI. Indeed, based on that data, the numbers of vehicles and personnel have been implemented in the municipalities that registered more episodes and where the clusters are supposed to be. BI has been of paramount importance in taking timely decisions on the management of EMS during COVID-19 outbreak.  Conclusions: Even if there is little evidence-based literature focused on BI impact within the health care, this study suggests that BI can be usefully applied to promptly identify clusters and patterns of the SARS-CoV-2 epidemic and, consequently, make informed decisions that can improve the EMS management response to the outbreak.

References

Azienda Regionale Emergenza Urgenza. http://www.areu.lombardia.it/

Chen H, Chiang R.H.L., and Storey V.C., Business Intelligence and Analytics: From Big Data to Big Impact, MIS Quarterly, 36(4), 2012, pp. 1165-1188.

Loewen L, Roudsari A. Evidence for Business Intelligence in Health Care: A Literature Review. Stud Health Technol Inform. 2017;235:579-583. Review.

Remuzzi A., Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020 Mar

Spiteri G, Fielding J, Diercke M, Campese C, Enouf V, Gaymard A, Bella A, Sognamiglio P, Sierra Moros MJ, Riutort AN, Demina YV, Mahieu R, Broas M, Bengnér M, Buda S, Schilling J, Filleul L, Lepoutre A, Saura C, Mailles A, Levy-Bruhl D, Coignard B, Bernard-Stoecklin S, Behillil S, van der Werf S, Valette M, Lina B, Riccardo F, Nicastri E, Casas I, Larrauri A, Salom Castell M, Pozo F, Maksyutov RA, Martin C, Van Ranst M, Bossuyt N, Siira L, Sane J, Tegmark-Wisell K, Palmérus M, Broberg EK, Beauté J, Jorgensen P, Bundle N, Pereyaslov D, Adlhoch C, Pukkila J, Pebody R, Olsen S, Ciancio BC. First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020. Euro Surveill. 2020 Mar;25(9).

Grasselli G., Pesenti A., Cecconi M. Critical case utilization for the COVID-19 outbreak in Lombardy, Italy. JAMA Online first March 13, 2020.

Spina S, Marrazzo F, Migliari M, Stucchi R, Sforza A, Fumagalli R. The response of Milan’s Emergency Medical System to the COVID-19 outbreak in Italy. Lancet 2020; 395:e49-e50

D.G.R. nº1964 del 06-07-2011. Soccorso sanitario extraospedaliero- aggiornamento n. DGR 37434/1998, n. 45819/1999, n. 16484/2004 e n. 1743/2006

McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2020. doi: 10.1016/S2589-7500(20)30054-6

Buckee C. Improving epidemic surveillance and response: big data is dead, long live big data. Lancet Digit Health. 2020 Mar. doi: 10.1016/S2589-7500(20)30059-5

George DB, Taylor W, Shaman J, Rivers C, Paul B, O'Toole T, Johansson MA, Hirschman L, Biggerstaff M, Asher J, Reich NG. Technology to advance infectious disease forecasting for outbreak management. Nat Commun. 2019 Sep 2;10(1):3932. doi: 10.1038/s41467-019-11901-7.

Ashrafi N, Kelleher L, & Kuilboer J-P. (2014). The impact of business intelligence on healthcare delivery in the USA. Interdisciplinary Journal of Information, Knowledge, and Management, 9, 117-130.

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Published

11-05-2020

Issue

Section

ORIGINAL INVESTIGATIONS/COMMENTARIES - SPECIAL COVID19

How to Cite

1.
Sechi GM, Migliori M, Dassi G, Pagliosa A, Bonora R, Oradini-Alacreu A, et al. Business Intelligence applied to Emergency Medical Services in the Lombardy region during SARS-CoV-2 epidemic. Acta Biomed [Internet]. 2020 May 11 [cited 2024 Jul. 14];91(2):39-44. Available from: https://www.mattioli1885journals.com/index.php/actabiomedica/article/view/9557