Development and application of a fuzzy occupational health risk assessment model in the healthcare industry

Authors

  • Mohammad Hossein Chalak Health Promotion Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
  • Amin Kahani Department of Occupational Health Engineering, School of Health, kerman University of Medical Sciences, kerman, Iran
  • Ghasem Bahramiazar Faculty of Environment, University of Tehran, Tehran, Iran
  • Zohreh Marashi Department of Biomedical engineering, Semnan University, Semnan, Iran
  • Tsvetan Ivanov Popov College of Health Science and Technology, School of Geoscience, Physics, and Safety, University of Central Missouri, Warrensburg, United States
  • Sakineh Dadipoor Tobacco and Health research center, Hormozgan University of Medical Sciences, Bandar Abbas , Iran
  • Omran Ahmadi Department of Occupational Health Engineering, Faculty of Medical sciences, Tarbiat Modares University, Tehran, Iran

DOI:

https://doi.org/10.23749/mdl.v113i4.12800

Keywords:

Risk Assessment; Fuzzy set theory; Occupational health; Healthcare industry; Fuzzy inference system(FIS) ; Risks

Abstract

Background: Hazards of the workplace and their impacts on the healthcare industry affect the quality of patient care and safety and impose high costs on the healthcare industry. Occupational health in this industry requires proper identification of hazards and managing the related risks. In this study, the researchers attempted to develop an easy-to-use and high applicability occupational health risk assessment model with a fuzzy approach to evaluate risks more precisely. Methods: In this study, a fuzzy inference system (FIS) was designed and applied to develop a risk assessment model. Conclusions: This study showed that the developed model could be applied as a practical model for evaluating occupational health risks. The weight of each risk criterion was used to calculate the risk level by adopting a fuzzy approach. The risk assessment results construed using the fuzzy set theory provided a broad picture of risks and could work adequately in the presence of inaccurate and insufficient data to calculate the risk. This model calculates risk levels and provides us with the dispersion and distribution of the calculated value of the risk number.

Downloads

Download data is not yet available.

References

Mohanty A, Kabi A, Mohanty AP. Health problems in healthcare workers: A review. J Family Med Prim Care. 2019;8(8):2568-2572.

Workplace Safety & Health. The National Institute for Occupational Safety and Health (NIOSH), 2017, Available from: https://www.cdc.gov/niosh/topics/healthcare/chemical.html.

Unruh L, Asi Y. Determinants of workplace injuries and violence among newly licensed RNs. Workplace Health Saf. 2018;66(10);482-492.

Kudryavtsev SS, Yemelin PV, Yemelina NK. The Development of a Risk Management System in the Field of Industrial Safety in the Republic of Kazakhstan. Saf Health Work. 2018;9(1):30-41.

Halford CD. Implementing Safety Management Systems in Aviation. 2016, Routledge.

Cinar U, Cebi S. A novel approach to assess occupational risks and prevention of hazards: the house of safety and prevention. J Intell Fuzzy Syst. 2022;42:517-528.

Van Duijne FH, van Aken D, Schouten EG. Considerations in developing complete and quantified methods for risk assessment. Saf Sci. 2008;46(2):245-254.

Petrović DV, Tanasijevic M, Milic V, et al. Risk assessment model of mining equipment failure based on fuzzy logic. Expert Syst Appl. 2014;41(18):8157-8164.

Zegordi H, Reazee NK, Nazari A, Honari CF. Provide a model for risk reduction in power plant project based multi-objective optimization approach and fuzzy analytic hierarchy process. Energy Econ. 2011;31:161-95.

Villemeur A. Reliability, Availability, Maintainability and Safety Assessment, Assessment, Hardware, Software and Human Factors. Vol. 2., 1992, Wiley.

Karasan A, Bolturk E, Kahraman C. An integrated methodology using neutrosophic CODAS & fuzzy inference system: Assessment of livability index of urban districts. J Intell Fuzzy Syst. 2019;36:5443-5455.

Debnath J, Biswas A, Sivan P, Nirmalya San K, Sahu S. Fuzzy inference model for assessing occupational risks in construction sites. Int J Ind Ergon. 2016;55:114-128.

Gürcanli GE, Müngen U. An occupational safety risk analysis method at construction sites using fuzzy sets. Int J Ind Ergon. 2009;39(2):371-387.

Beriha GS, Patnaik B, Mahapatra SS, Padhee S. Assessment of safety performance in Indian industries using fuzzy approach. Expert Syst Appl. 2012;39(3):3311-3323.

Shapiro AF, Koissi M. Risk assessment applications of fuzzy logic. Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 2015.

Zhou LF, Tian F, Zou H, et al. Research Progress in Occupational Health Risk Assessment Methods in China. Biomed Environ Sci. 2017;30(8):616-622.

Brooke I. A UK scheme to help small firms control health risks from chemicals: toxicological considerations. Ann Occup Hyg. 1998;42(6):377-390.

Bullock WH, Ignacio JS. A strategy for assessing and managing occupational exposures. 2006, AIHA.

Schinkel J, Ritchie P, Goede H, et al. The Advanced REACH Tool (ART): incorporation of an exposure measurement database. Ann Occup Hyg. 2013;57(6):717-727.

Landberg HE, Berg P, Andersson L, et al. Comparison and evaluation of multiple users’ usage of the exposure and risk tool: Stoffenmanager 5.1. Ann Occup Hyg. 2015;59(7):821-835.

Z590.3-2011. Prevention through Design: Guidelines for Addressing Occupational Hazards and Risks in Design and Redesign Processes. The American Society of Safety Professionals(ASSP), 2011. Available from: https://kipdf.com/asse-z-prevention-through-design-guidelines-for-addressing-occupational-hazards-_5ad25feb7f8b9ab9418b461f.html.

Samantra C, Datta S, Mahapatra SS. Analysis of occupational health hazards and associated risks in fuzzy environment: a case research in an Indian underground coal mine. Int J Inj Control Saf Promot. 2017;24(3):311-327.

Ilbahar E, Karasan A, Cebi S, et al. A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf Sci. 2018;103:124-136.

Acuner O, Cebi S. An effective risk-preventive model proposal for occupational accidents at shipyards. Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, 2016;67(1):67-84.

Gul M, Ak MF, Guneri AF. Occupational health and safety risk assessment in hospitals: A case study using two-stage fuzzy multi-criteria approach. Hum Ecol Risk Assess. 2017;23(2):187-202.

Chalak MH, Bahramiazar G, Rasaee J, et al. Occupational health risk assessment at healthcare institutions: Developing a semi-quantitative risk method. Int J Risk Saf Med. b2021(Preprint):1-14.

Acuner O. An effective risk-preventive model proposal for occupational accidents at shipyards. brodogradnja [Internet]. 2016;67(1):67-84.

Beriha GS, Patnaik, Mahapatra SS, Padhee S. Assessment of safety performance in Indian industries using fuzzy approach. Expert Syst Appl. 2012;39(3):3311-3323.

Acosta-Prado JC, Lazo JG, Tafur-Mendoza AA. Application of fuzzy logic in the relationship between information and communication technologies and economic performance. J Intell Fuzzy Syst. 2021;40:1727-1737.

Tsai TN, Yeh JH. Identification and risk assessment of soldering failure sources using a hybrid failure mode and effect analysis model and a fuzzy inference system. J Intell Fuzzy Syst, 2015;28:2771-2784.

Vosoughi S, Chalak MH, Rostamzadeh S, et al. A cause and effect decision making model of factors influencing falling from height accidents in construction projects using Fuzzy-DEMATEL technique. Iran Occup Health, 2019;16(2):79-93.

Ross, TJ. Fuzzy logic with engineering applications. 2005, John Wiley & Sons.

Vosoughi S, Chalak MH, Yarahmadi R, et al. Identification, Selection and Prioritization of Key Performance Indicators for the Improvement of Occupational Health (Case Study: An Automotive Company). J UOEH. 2020;42(1):35-49.

Manuele FA. Advanced safety management focusing on Z10 and serious injury prevention. 2008, Wiley Online Library.

Rakhshani F, Heidari M, Barati S. Prevalence of needlestick injuries among the healthcare professionals in Zahedan medical Sciences university. Iran J Epidemiology. 2009;4(3):87-91.

Nasiri E, Mortazavi Y, Siyamian H, Shaban Khani. Prevalence of needle stick and sharp object injuries among Educational and non Educatinal nursing staff of Mazandran University. Iranian J Infect Dis Trop Med. 2005; 10(29)43-46.

Downloads

Published

25-08-2022

Issue

Section

Original articles