Cognition, Technology & Work

, Volume 20, Issue 3, pp 477–488 | Cite as

Supporting decision-making in patient risk assessment using a hierarchical fuzzy model

  • Alessandro Jatobá
  • Hugo Cesar Bellas
  • Isabella Koster
  • Catherine M. Burns
  • Mario Cesar R. Vidal
  • Cláudio Henrique S. Grecco
  • Paulo Victor R. de Carvalho
Original Article


In this paper, we present a hierarchical fuzzy model to support patient triage in primary health care. In developing countries like Brazil, public health must usually cover degraded territories; thus, allocating patients to health services is very hard due low availability, enormous demands, and the complexity of assessing patient conditions—which must account for more then physical aspects of patients, but their social conditions as well. This approach combines the fuzzy set theory under the AHP framework in order to illustrate the inherent imprecision in the evaluation of patient risk. Fieldwork was conducted in a primary healthcare facility in Brazil to demonstrate the applicability of the proposed approach. The proposed approach represents criterion in the formation of patients’ risk scores encompassing important aspects of primary care triage such as the structure of families, the conditions of residences, exposure to urban violence, and other aspects of patients’ lives, taking the risk assessment beyond the simple evaluation of symptoms and physiological conditions. Our approach focuses on enforcing decisions of public health workers by improving the awareness of patients’ conditions, which we believe will make the employment of triage criteria uniform and capable of showing tendencies on patients’ risks, as well as avoiding bias in patient triage.


Decision-making Analytical hierarchy process Fuzzy logic Risk assessment Primary health care 



We would like to thank the family healthcare professionals who participated in this study and the staff of the Germano Sinval Faria Health Care Center and School/Oswaldo Cruz Foundation led by Dr. Emilia Correia.


This research has been partially funded by the Science without Borders Program/Brazilian National Council for Scientific and Technological Development and by the Group of Ergonomics and New Technologies/Federal University of Rio de Janeiro.


  1. Ashour OM, Okudan, GE, Smith CA (2010) An improved triage algorithm for emergency departments based on fuzzy AHP and utility theory. In: Johnson A, Miller J (eds) Proceedings of the 2010 industrial engineering research conferenceGoogle Scholar
  2. Bartolin R, Bouvenot G, Soula G, Sanchez E (1982) The fuzzy set theory as a biomedical diagnostic aid. Sem Hop 58(22):1361–1365Google Scholar
  3. Gharahighehi A, Kheirkhah AS, Bagheri A (2016) Improving performances of the emergency department using discrete event simulation DEA and the MADM methods. Digital Health 2:1–14. CrossRefGoogle Scholar
  4. Gong Z, Lin Y, Yao T (2012) Uncertain fuzzy preference relations and their applications. Springer, BerlinzbMATHGoogle Scholar
  5. Grecco CH, Consenza CA, Dos Santos IJ, Carvalho PV (2014) Safety culture assessment: a fuzzy model for improving safety performance in a radioactive installation. Prog Nuclear Energy 70:71–83CrossRefGoogle Scholar
  6. Jatoba A, Bellas HC, Bonfatti RJ, Burns CM, Vidal MR, Carvalho PR (2016) Designing for patient risk assessment in primary health care: a case study. Cognit Technol Work 18(1):215–231. CrossRefGoogle Scholar
  7. Klein G (1997) An overview of naturalistic decision making applications. In: Zsambok CE, Klein G (eds) Naturalistic decision making. Lawrence Erlbaum Associates, Mahwah, pp 49–59Google Scholar
  8. Lee C, Ru C, Yeung C, Choy K, Ip W (2015) Analyze the healthcare service requirement using fuzzy QFD. Comput Ind 74:1–15CrossRefGoogle Scholar
  9. Manchester Triage Group (2005) Emergency triage. Wiley, ManchesterGoogle Scholar
  10. Moumjid N, Gafni A, Brémond A, Carrère M-O (2007) Shared decision making in the medical encounter: Are we all talking about the same thing? Med Decis Mak 27(5):539–546CrossRefGoogle Scholar
  11. Rahimi SA, Jamshidi A, Ruiz A, Ait-kadi D (2016) A new dynamic integrated framework for surgical patients’ prioritization considering risks and uncertainties. Decis Support Syst 88:112–120. CrossRefGoogle Scholar
  12. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281MathSciNetCrossRefzbMATHGoogle Scholar
  13. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26CrossRefzbMATHGoogle Scholar
  14. Saay RW (1987) The analytic hierarchy process: what it is and how it is used. Math Model 9(3–5):161–176MathSciNetGoogle Scholar
  15. Samuel O, Omisore M, Ojokoh B (2013) A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever. Expert Syst Appl 40:4164–4171CrossRefGoogle Scholar
  16. Sanchez A (1998) Fuzzy logic and inflammatory protein variations. Clin Chim Acta 270:31–42CrossRefGoogle Scholar
  17. Savassi LC, Carvalho HR, Mariano FM, Lamberti C, Mendonça MF, Yamana G, Pereira R (2012a) Proposal of a protocol for individual risk classification for home care in primary health. J Manag Prim Health Care 3(2):151–157Google Scholar
  18. Savassi L, Lage J, Coelho F (2012b) Systematization of a stratification questionnaire for family risk: Coelho-Savassi’s Family Risk Scale. J Manag Prim Health Care 3(2):179–185Google Scholar
  19. Schmidt T, Wiil UK (2015) Identifying patients at risk of deterioration in the Joint Emergency Department. Cognit Technol Work 17(4):529–545. CrossRefGoogle Scholar
  20. Strauss A, Corbin J (1998) Basics of qualitative research: techniques and procedures for developing grounded theory, 2nd edn. Sage, Thousand OaksGoogle Scholar
  21. Yu PL, Chiang CY (2002) Decision making, habitual domains and information technology. Int J Info Tech Decis Mak 1(1):5–26CrossRefGoogle Scholar
  22. Zadeh LA (1965) Fuzzy sets. Inf Control 1(8):338–353CrossRefzbMATHGoogle Scholar
  23. Zadeh LA (1975a) Fuzzy logic and approximate reasoning. Synthese 30(3–4):407–428CrossRefzbMATHGoogle Scholar
  24. Zadeh LA (1975b) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Alessandro Jatobá
    • 1
  • Hugo Cesar Bellas
    • 1
  • Isabella Koster
    • 1
  • Catherine M. Burns
    • 2
  • Mario Cesar R. Vidal
    • 3
  • Cláudio Henrique S. Grecco
    • 4
  • Paulo Victor R. de Carvalho
    • 4
  1. 1.Fundação Oswaldo Cruz – FIOCRUZRio de JaneiroBrazil
  2. 2.University of WaterlooWaterlooCanada
  3. 3.Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia – COPPEUniversidade Federal do Rio de Janeiro – UFRJRio de JaneiroBrazil
  4. 4.Instituto de Engenharia Nuclear – IENComissão Nacional de Energia Nuclear – CNENRio de JaneiroBrazil

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