Precautionary Recommendation System in Risk Management: A FMEA-Based Approach


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NARLI M.

Risk Management and Healthcare Policy, cilt.18, ss.3437-3447, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18
  • Basım Tarihi: 2025
  • Doi Numarası: 10.2147/rmhp.s557778
  • Dergi Adı: Risk Management and Healthcare Policy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Directory of Open Access Journals
  • Sayfa Sayıları: ss.3437-3447
  • Anahtar Kelimeler: decision support system, FMEA, risk control measures, RPN, rule-based decision model
  • Çukurova Üniversitesi Adresli: Evet

Özet

Purpose: FMEA (Failure Mode and Effects Analysis), a widely used tool in risk management, lacks systematic guidance on selecting the type of precaution. This study introduces a rule-based FMEA. Although the Risk Priority Number (RPN), widely used in the literature, expresses the risk level quantitatively, it is insufficient for guiding the appropriate type of precaution. The proposed rule-based model uses a multi-dimensional rule system that considers numerical parameters (probability, severity, detectability, and RPN) and contextual variables. Material and Methods: The model, structured according to the Occupational Health and Safety precaution hierarchy, defines six precaution classes: elimination, substitution, engineering measures, training, administrative measures, and personal protective equipment (PPE). The model’s theoretical consistency, sensitivity, and practical applicability were tested in a neonatal intensive care unit (NICU). Results: The FMEA method identified 24 failure modes related to infections. Scenario-based sensitivity analyses revealed that contextual variables significantly influenced the recommended precautions. Administrative and training measures were the most frequently recommended, while PPE was consistently recommended for exposure-related risks. Expert evaluation indicated 95.8% agreement with the model outputs. Conclusion: These findings indicate that the development of a rule-based system can serve as a repeatable and explainable decision-support tool, especially in high-risk settings such as healthcare, which is the study’s most distinctive contribution.