SYSTEMS-LEVEL QUALITY IMPROVEMENT The Impact of Perioperative Data Science in Hospital Knowledge Management Márcia Baptista 1 & José Braga Vasconcelos 2,3 & Álvaro Rocha 4 & Rita Silva 5 & João Vidal Carvalho 6 & Helena Gonçalves Jardim 7 & António Quintal 8 Received: 10 August 2018 /Accepted: 8 January 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Conservative practices, such as manual registry have limited scope regarding preoperative, intraoperative and post- operative decision making, knowledge discovery, analytical techniques and knowledge integration into patient care. To maximize quality and value, perioperative care is changing through new technological developments. In this context, knowledge management practices will enable future transformation and enhancements in healthcare services. By performing a data science and knowledge management research in the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015, this paper describes its principal results. This study showed perioperative decision-making improvement by integrating data science tools on the perioperative electronic system (PES). Before the PES implementation only 1,2% of the nurses registered the preoperative visit and after 87,6% registered it. Regarding the patient features it was possible to assess anxiety and pain levels. A future conceptual model for perioperative decision support systems grounded on data science should be considered as a knowledge management tool. Keywords Perioperative data science . Knowledge management . Clinical decision support systems . Hospital information systems This article is part of the Topical Collection on Systems-Level Quality Improvement * Márcia Baptista marciabatista12@gmail.com José Braga Vasconcelos jose.braga.vasconcelos@uatlantica.pt Álvaro Rocha amrocha@dei.uc.pt Rita Silva ritamlbs@hotmail.com João Vidal Carvalho cajvidal@iscap.ipp.pt Helena Gonçalves Jardim hjardim@uma.pt António Quintal ajdomq@gmail.com 1 Information Technology Research Department, Santiago Compostela University, Santiago, Spain 2 Knowledge Management and Engineering Research Group, Universidade Atlântica, Barcarena, Portugal 3 Centro de Administração e Políticas Públicas (CAPP) da Universidade de Lisboa, Lisboa, Portugal 4 Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal 5 Bloco Operatório, Hospital Dr. Nélio Mendonça, Madeira, Portugal 6 Politécnico do Porto, ISCAP, CEOS.PP, S. Mamede de Infesta, Portugal 7 Health Higher School, Madeira University and The Health Sciences Research Unit: Nursing, Coimbra, Portugal 8 Universidade da Madeira, Madeira, Portugal Journal of Medical Systems (2019) 43:41 https://doi.org/10.1007/s10916-019-1162-3