IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v194y2020ics0951832018307361.html
   My bibliography  Save this article

Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation

Author

Listed:
  • Pandya, Dhruv
  • Podofillini, Luca
  • Emert, Frank
  • Lomax, Antony J.
  • Dang, Vinh N.
  • Sansavini, Giovanni

Abstract

This paper develops the quantification framework of a new Human Reliability Analysis (HRA) method, for application to the radiotherapy domain. The method's reference data is obtained via expert judgment, due to the lack of domain-specific data. To avoid shortcomings of directly eliciting probabilities, experts are asked to assess the importance of specific factors for the failure probability, elicited on a qualitative scale. Each assessment is converted into statements about the order of magnitude of the probability value. The values are combined via an expert aggregation method, developed specifically for HRA. The paper includes an attempt to validate the elicitation against applicable Human Error Probability (HEP) values from existing HRA methods (thus, “convergent validation†). Indeed, some tasks are generic in nature and data can be assumed to be sector-independent (e.g. checking activities, interacting with interfaces, simple tasks such as identifying objects or characters/numbers). Differences in the values are identified and, when possible, linked to differences in the performance context characteristic of the field of application of the different methods.

Suggested Citation

  • Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
  • Handle: RePEc:eee:reensy:v:194:y:2020:i:c:s0951832018307361
    DOI: 10.1016/j.ress.2019.05.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832018307361
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2019.05.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    2. James Chang, Y. & Bley, Dennis & Criscione, Lawrence & Kirwan, Barry & Mosleh, Ali & Madary, Todd & Nowell, Rodney & Richards, Robert & Roth, Emilie M. & Sieben, Scott & Zoulis, Antonios, 2014. "The SACADA database for human reliability and human performance," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 117-133.
    3. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    4. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    5. Podofillini, L. & Dang, V.N., 2013. "A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 52-64.
    6. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    7. Ale, B.J.M. & Bellamy, L.J. & van der Boom, R. & Cooper, J. & Cooke, R.M. & Goossens, L.H.J. & Hale, A.R. & Kurowicka, D. & Morales, O. & Roelen, A.L.C. & Spouge, J., 2009. "Further development of a Causal model for Air Transport Safety (CATS): Building the mathematical heart," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1433-1441.
    8. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A classification scheme of erroneous behaviors for human error probability estimations based on simulator data," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 1-13.
    9. Kirwan, Barry & Gibson, W. Huw & Hickling, Brian, 2008. "Human error data collection as a precursor to the development of a human reliability assessment capability in air traffic management," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 217-233.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yan & Wang, Yu-Hao & Zhao, Xu & Tong, Rui-Peng, 2023. "Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario," Resources Policy, Elsevier, vol. 85(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    2. Morais, Caroline & Estrada-Lugo, Hector Diego & Tolo, Silvia & Jacques, Tiago & Moura, Raphael & Beer, Michael & Patelli, Edoardo, 2022. "Robust data-driven human reliability analysis using credal networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Greco, Salvatore F. & Podofillini, Luca & Dang, Vinh N., 2021. "A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    6. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    7. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    8. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    9. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Fam, Mei Ling & He, Xuhong & Konovessis, Dimitrios & Ong, Lin Seng, 2020. "Using Dynamic Bayesian Belief Network for analysing well decommissioning failures and long-term monitoring of decommissioned wells," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    11. Maturana, Marcos Coelho & Martins, Marcelo Ramos & Frutuoso e Melo, Paulo Fernando Ferreira, 2021. "Application of a quantitative human performance model to the operational procedure design of a fuel storage pool cooling system," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Abreu, Danilo T.M.P. & Maturana, Marcos C. & Droguett, Enrique Lopez & Martins, Marcelo R., 2022. "Human reliability analysis of conventional maritime pilotage operations supported by a prospective model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    13. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    14. Justin Pence & Zahra Mohaghegh, 2020. "A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1183-1211, June.
    15. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Liu, Peng & Qiu, Yongping & Hu, Juntao & Tong, Jiejuan & Zhao, Jun & Li, Zhizhong, 2020. "Expert judgments for performance shaping Factors’ multiplier design in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    17. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    18. Zarei, Esmaeil & Khan, Faisal & Abbassi, Rouzbeh, 2021. "Importance of human reliability in process operation: A critical analysis," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    19. Sezer, Sukru Ilke & Akyuz, Emre & Arslan, Ozcan, 2022. "An extended HEART Dempster–Shafer evidence theory approach to assess human reliability for the gas freeing process on chemical tankers," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    20. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:194:y:2020:i:c:s0951832018307361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.