Reducing failures in daily medical practice: Healthcare failure mode and effect analysis combined with computer simulation

Leeftink, A.G.; Visser, J.; Laat, J.M de; Meij, N.T.M. van der; Vos, Jorga; Valk, Gerlof D.

doi:10.1080/00140139.2021.1910734

Cited by 4 publications

(5 citation statements)

References 34 publications

(42 reference statements)

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“…Use of the FMEA method has garnered considerable attention in the medical field [6,7]. In this study, we applied the FMEA method to manage the risks associated with needlestick injury among dental outpatient medical staff.…”

Section: Discussionmentioning

confidence: 99%

“…The Failure Mode and Effects Analysis (FMEA) method stands out as a risk management tool designed to identify, analyze, evaluate, and control possible risks, aiming to prevent adverse events. Renowned for its simplicity, adaptability, and capacity to prioritize significant risk factors [4,5], FMEA has gained recognition as an important prospective risk analysis approach, endorsed by the International Organization for Standardization (ISO) Technical Committee since 2008 and widely embraced in medical risk management across developed nations [6,7]. While FMEA has been applied to enhance needlestick injury protection among outpatient dental medical staff, scant literature exists on Am J Transl Res 2024;16 (5): [1969][1970][1971][1972][1973][1974][1975][1976] its specific application in preventing needlestick injuries among oral healthcare personnel [8,9].…”

Section: Introductionmentioning

confidence: 99%

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FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries

2024

Am J Transl Res

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Objective: To explore the application value of the Failure Mode and Effects Analysis (FMEA) method in the risk management of needlestick injuries among oral healthcare personnel. Methods: A total of 37 healthcare workers from the dental department of Zhujiang Hospital, Southern Medical University, were selected as study subjects. Routine risk management procedures were followed from January 2021 to December 2021, serving as the control group, while FMEA-based risk management was implemented from January 2022 to December 2022, representing the research group. The Risk Priority Number (RPN) was calculated, and interventions were implemented for the top five identified failure modes. The RPN score, incidence of needlestick injuries, healthcare personnel's knowledge and awareness levels, prevention behavior, and rate of satisfaction with management were compared between the two groups. Results: FMEA-based risk management identified weak knowledge of protection, disorganized placement of sharp instruments, failure to adhere to operational standards, improper operational procedures, and insufficient regulations for preventing needlestick injuries as the top five failure modes. The RPN scores for these modes were significantly lower in the research group (P<0.05). The research group also experienced a lower frequency and incidence of needlestick injury (P<0.05), along with higher levels of healthcare knowledge, awareness of prevention, and prevention behavior (P<0.05). Additionally, satisfaction with management was higher in the research group compared to the control group (P<0.05). Conclusion: FMEA-based risk management can improve the ability of oral healthcare personnel to prevent needlestick injury, reduce the occurrence of such incidents, and enhance satisfaction with management. This approach holds promise for wider adoption.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries

2024

Am J Transl Res

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“…These all add complexity to this picture. All these factors must converge to improve outcomes and put the patient’s needs at the center [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…As human behavior and the design of the system play key roles in patient safety, healthcare systems should constantly perform risk assessment, prioritization, and mitigation regarding medical care processes [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Failure in Medical Practice: Human Error, System Failure, or Case Severity?

et al. 2022

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The success rate in medical practice will probably never reach 100%. Success rates depend on many factors. Defining the success rate is both a technical and a philosophical issue. In opposition to the concept of success, medical failure should also be discussed. Its causality is multifactorial and extremely complex. Its actual rate and its real impact are unknown. In medical practice, failure depends not only on the human factor but also on the medical system and has at its center a very important variable—the patient. To combat errors, capturing, tracking, and analyzing them at an institutional level are important. Barriers such as the fear of consequences or a specific work climate or culture can affect this process. Although important data regarding medical errors and their consequences can be extracted by analyzing patient outcomes or using quality indicators, patient stories (clinical cases) seem to have the greatest impact on our subconscious as medical doctors and nurses and these may generate the corresponding and necessary reactions. Every clinical case has its own story. In this study, three different cases are presented to illustrate how human error, the limits of the system, and the particularities of the patient’s condition (severity of the disease), alone or in combination, may lead to tragic outcomes There is a need to talk openly and in a balanced way about failure, regardless of its cause, to look at things as they are, without hiding the inconvenient truth. The common goal is not to find culprits but to find solutions and create a culture of safety.

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“…37,38 After the application in the aerospace industry, back in 1960, it has been extensively used in several sectors, like, manufacturing, healthcare, chemical, food, and so forth. [39][40][41][42][43] In traditional FMEA, FMs, their causes, and effects are first tabulated and then experts are asked to evaluate each of the FMs to each risk parameter, namely, probability of occurrence (P), severity of consequences (S), and ease of detectability (D), usually on a 10-point crisp scale. Next, risk priority number (RPN), representing the risk of each FM, is computed by the product of P, S, and D. Finally, Pareto analysis is performed to investigate FMs.…”

Section: Failure Mode and Effect Analysismentioning

confidence: 99%

Dual hesitant Z‐ number (DHZN), correlated distance, and risk quantification

et al. 2021

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Information reliability and uncertainty are two important characteristics that need to be taken care of in any kind of decision-making process. The hesitancy of the human mind is one of the major factors that introduce uncertainty in decision-making. To include the reliability and to resolve the issue of hesitancy in a single framework, in this study, two novel concepts, namely, dual hesitant Z-number (DHZN) and correlated distance measure between two DHZNs (CD-DHZN) are proposed. DHZN = (A h , B h ), is a judicious integration of hesitant fuzzy sets and Z-number. Here, A h is the expert's opinion and B h is the reliability of the expert's opinion. Further, the randomness of the information brings uncertainty to the decision-making process. To circumvent this issue, DHZN is quantified with Cloud model theory and fuzzy envelope. To enhance the information utilization and to reduce the information loss problem while quantifying CD-DHZN, the correlation and the underlying hidden probabilistic relationship between the two parts of a DHZN are captured using the Pearson correlation coefficient, the maximum entropy principle, and Hellinger distance. Then, the extended failure mode and effect analysis (E-FMEA) is proposed with the help of DHZN, CD-DHZN, and VlseKriterijuska Optimizacija IKomoromisno Resenje technique for prioritization of risks. Similarly, extended bow-tie (E-BT) is also proposed for the quantification of basic events (BEs), top event, and accident scenarios. Two case studies with systematic experimental investigations are presented and results are compared with other existing techniques. The results show that the proposed models are able to effectively prioritize the failure modes in E-FMEA and quantify the BEs in E-BT. The results confirm the feasibility and applicability of the proposed models. Sensitivity analysis is also performed to ensure the plausibility and robustness of the proposed model.

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Reducing failures in daily medical practice: Healthcare failure mode and effect analysis combined with computer simulation

Cited by 4 publications

References 34 publications

FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries

FMEA-based risk management improves the ability of oral healthcare personnel to prevent needlestick injuries

Failure in Medical Practice: Human Error, System Failure, or Case Severity?

Dual hesitant Z‐ number (DHZN), correlated distance, and risk quantification

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