Perioperative Data Science: A Research Approach for Building Hospital Knowledge

  • Márcia Baptista
  • José Braga Vasconcelos
  • Álvaro Rocha
  • Rita Lemos
  • João Vidal Carvalho
  • Helena Gonçalves Jardim
  • António Quintal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Perioperative care is changing through advances in technology with the aim of maximizing quality and value. Future transformation in care will be enabled by data and consequently by knowledge. This paper describes a knowledge management and data science research project and its results based on a study applied to the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015. Conservative practices, such as manual registry, are limited in their scope for preoperative, intraoperative and postoperative decision making, discovery, extent and complexity of data, analytical techniques, and translation or integration of knowledge into patient care. This study contributed to the perioperative decision making process improvement by integrating data science tools on the perioperative electronic system (PES) assembled. 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.


Perioperative data science Knowledge management Clinical decision support systems Hospital information systems 


  1. 1.
    Menachemi, N., Collum, T.H.: Benefits and drawbacks of electronic health record systems. Risk Manag. Healthc. Policy 4, 47–55 (2011)CrossRefGoogle Scholar
  2. 2.
    World Health Organization (WHO).: Management of patient information: trends and challenges in Member States: based on the findings of the second global survey on eHealth. Global Observatory for eHealth series, vol. 6 (2012)Google Scholar
  3. 3.
    Vedula, S.S., et al.: Surgical data science: the new knowledge domain. Innov. Surg. Sci. 2(3), 109–121 (2017). Accessed 20 Apr 2017CrossRefGoogle Scholar
  4. 4.
    St. Jacques, J., Minear, N.: Improving perioperative patient safety through the use of information technology. In: Henriksen, K., Battles, J.B., Keyes, M.A., et al. (eds.) Advances in Patient Safety: New Directions and Alternative Approaches, vol. 4, Technology and Medication Safety. Rockville (MD): Agency for Healthcare Research and Quality, US (2008)Google Scholar
  5. 5.
    Khalifa, M., Alswailem, O.: Hospital information systems (HIS) acceptance and satisfaction: a case study of a tertiary care hospital. Procedia Comput. Sci. 63(2015), 198–204 (2015)CrossRefGoogle Scholar
  6. 6.
    Doebbeling, B.N., Burton, M.M., Wiebke, E.A., Miller, S., Baxter, L., Miller, D., Pekny, J.: Optimizing perioperative decision making: improved information for clinical workflow planning. In: AMIA Annual Symposium Proceedings, pp. 154–163 (2012)Google Scholar
  7. 7.
    Sweeney, P.: The effects of information technology on perioperative nursing. AORNJ 92(5), 528–540 (2010)CrossRefGoogle Scholar
  8. 8.
    Bhavnani, S., Muñoz, D., Bagai, A.: Data science in healthcare: implications for early career investigators. circulation: cardiovascular quality and outcomes. 9, CIRCOUTCOMES.116.003081 (2016).
  9. 9.
    Kakabadse, K.A.: From tacit knowledge to knowledge management: leveraging invisible assets (2001)Google Scholar
  10. 10.
    Cardoso A.: sdGoogle Scholar
  11. 11.
    Plessis, M.D.: Drivers of knowledge management in the corporate environment. Int. J. Inf. Manag. 25, 193–202 (2005)CrossRefGoogle Scholar
  12. 12.
    Gupta, B., Iyer, L., Aronson, J.: Knowledge management: practices and challenges. Ind. Manag. Data Syst. 100(1), 17–21 (2000)CrossRefGoogle Scholar
  13. 13.
    Morr, C., Subercaze, J.: Knowledge management in healthcare. In: IGI Global, pp. 490–510 (2010)Google Scholar
  14. 14.
    Rocha, A., Rocha, B.: Adopting nursing health record standards. Inform. Health Soc. Care 39(1), 1–14 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Carvalho, J.V., Rocha, Á., van de Wetering, R., Abreu, A.: A maturity model for hospital information systems. J. Bus. Res. 1–12 (2017, in press)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Márcia Baptista
    • 1
  • José Braga Vasconcelos
    • 2
    • 3
  • Álvaro Rocha
    • 4
  • Rita Lemos
    • 5
  • João Vidal Carvalho
    • 6
  • Helena Gonçalves Jardim
    • 7
  • António Quintal
    • 8
  1. 1.Information Technology Research DepartmentSantiago Compostela UniversitySantiago de CompostelaSpain
  2. 2.Knowledge Management and Engineering Research GroupUniversidade AtlânticaBarcarenaPortugal
  3. 3.Centro de Administração e Políticas Públicas (CAPP) da Universidade de LisboaLisbonPortugal
  4. 4.Departamento de Engenharia InformáticaUniversidade de CoimbraCoimbraPortugal
  5. 5.Bloco Operatório, Hospital Dr. Nélio MendonçaMadeiraPortugal
  6. 6.Politécnico do Porto, ISCAP, CEOSPortoPortugal
  7. 7.Health Higher SchoolMadeira University and the Health Sciences Research Unit: NursingCoimbraPortugal
  8. 8.Universidade da MadeiraMadeiraPortugal

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