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Analytical performance assessment and improvement by means of the Failure mode and effect analysis (FMEA).

ABSTRACT: Introduction:Laboratories minimize risks through quality control but analytical errors still occur. Risk management can improve the quality of processes and increase patient safety. This study aims to use the failure mode and effect analysis (FMEA) to assess the analytical performance and measure the effectiveness of the risk mitigation actions implemented. Materials and methods:The measurands to be included in the study were selected based on the measurement errors obtained by participating in an External Quality Assessment (EQA) Scheme. These EQA results were used to perform an FMEA of the year 2017, providing a risk priority number that was converted into a Sigma value (?FMEA). A root-cause analysis was done when ?FMEA was lower than 3. Once the causes were determined, corrective measures were implemented. An FMEA of 2018 was carried out to verify the effectiveness of the actions taken. Results:The FMEA of 2017 showed that alkaline phosphatase (ALP) and sodium (Na) presented a ?FMEA of less than 3. The FMEA of 2018 revealed that none of the measurands presented a ?FMEA below 3 and that ?FMEA for ALP and Na had increased. Conclusions:Failure mode and effect analysis is a useful tool to assess the analytical performance, solve problems and evaluate the effectiveness of the actions taken. Moreover, the proposed methodology allows to standardize the scoring of the scales, as well as the evaluation and prioritization of risks.

SUBMITTER: Guinon L

PROVIDER: S-EPMC7137999 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Analytical performance assessment and improvement by means of the Failure mode and effect analysis (FMEA).

Guiñón Leonor L Soler Anna A Gisell Díaz Mónica M Fernández Rosa María RM Rico Nayra N Bedini Josep Lluís JL Mira Aurea A Alvarez Luisa L

Biochemia medica 20200415 2

<h4>Introduction</h4>Laboratories minimize risks through quality control but analytical errors still occur. Risk management can improve the quality of processes and increase patient safety. This study aims to use the failure mode and effect analysis (FMEA) to assess the analytical performance and measure the effectiveness of the risk mitigation actions implemented.<h4>Materials and methods</h4>The measurands to be included in the study were selected based on the measurement errors obtained by pa ...[more]

PMID: 32292281

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Project description:Background We aim to assess the risks associated with total body irradiation (TBI) delivered using a commercial dedicated Co-60 irradiator, and to evaluate inter-institutional and inter-professional variations in the estimation of these risks. Methods A failure mode and effects analysis (FMEA) was generated using guidance from the AAPM TG-100 report for quantitative estimation of prospective risk metrics. Thirteen radiation oncology professionals from two institutions rated possible failure modes (FMs) for occurrence (O), severity (S), and detectability (D) indices to generate a risk priority number (RPN). The FMs were ranked by descending RPN value. Absolute gross differences (AGD) in resulting RPN values and Jaccard Index (JI; for the top 20 FMs) were calculated. The results were compared between professions and institutions. Results A total of 87 potential FMs (57, 15, 10, 3, and 2 for treatment, quality assurance, planning, simulation, and logistics respectively) were identified and ranked, with individual RPN ranging between 1–420 and mean RPN values ranging between 6 and 74. The two institutions shared 6 of their respective top 20 FMs. For various institutional and professional comparison pairs, the number of common FMs in the top 20 FMs ranged from 6 to 13, with JI values of 18–48%. For the top 20 FMs, the trend in inter-professional variability was institution-specific. The mean AGD values ranged between 12.5 and 74.5 for various comparison pairs. AGD values differed the most for medical physicists (MPs) in comparison to other specialties i.e. radiation oncologists (ROs) and radiation therapists (RTs) [MPs-vs-ROs: 36.3 (standard deviation SD = 34.1); MPs-vs-RTs: 41.2 (SD = 37.9); ROs-vs-RTs: 12.5 (SD = 10.8)]. Trends in inter-professional AGD values were similar for both institutions. Conclusion This inter-institutional comparison provides prospective risk analysis for a new treatment delivery unit and illustrates the institution-specific nature of FM prioritization, primarily due to operational differences. Despite being subjective in nature, the FMEA is a valuable tool to ensure the identification of the most significant risks, particularly when implementing a novel treatment modality. The creation of a bi-institutional, multidisciplinary FMEA for this unique TBI technique has not only helped identify potential risks but also served as an opportunity to evaluate clinical and safety practices from the perspective of both multiple professional roles and different institutions. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01894-3.

Project description:IntroductionParaspinal stereotactic body radiotherapy (SBRT) involves risks of severe complications. We evaluated the safety of the paraspinal SBRT program in a large academic hospital by applying failure modes and effects analysis.MethodsThe analysis was conducted by a multidisciplinary committee (two therapists, one dosimetrist, four physicists, and two radiation oncologists). The paraspinal SBRT workflow was segmented into four phases (simulation, treatment planning, delivery, and machine quality assurance (QA)). Each phase was further divided into a sequence of sub-processes. Potential failure modes (PFM) were identified from each subprocess and scored in terms of the frequency of occurrence, severity and detectability, and a risk priority number (RPN). High-risk PFMs were identified based on RPN and were studied for root causes using fault tree analysis.ResultsOur paraspinal SBRT process was characterized by eight simulations, 11 treatment planning, nine delivery, and two machine QA sub-processes. There were 18, 29, 19, and eight PFMs identified from simulation, planning, treatment, and machine QA, respectively. The median RPN of the PFMs was 62.9 for simulation, 68.3 for planning, 52.9 for delivery, and 22.0 for machine QA. The three PFMs with the highest RPN were: previous radiotherapy outside the institution is not accurately evaluated (RPN: 293.3), incorrect registration between diagnostic magnetic resonance imaging and simulation computed tomography causing incorrect contours (273.0), and undetected patient movement before ExacTrac baseline (217.8). Remedies to the high RPN failures were implemented, including staff education, standardized magnetic resonance imaging acquisition parameters, and an image fusion process, and additional QA on beam steering.ConclusionsA paraspinal SBRT workflow in a large clinic was evaluated using a multidisciplinary and systematic risk analysis, which led to feasible solutions to key root causes. Treatment planning was a major source of PFMs that systematically affect the safety and quality of treatments. Accurate evaluation of external treatment records remains a challenge.

Project description:To develop and implement a failure mode and effect analysis (FMEA)-based commissioning and quality assurance framework for dynamic multileaf collimator (DMLC) tumor tracking systems.A systematic failure mode and effect analysis was performed for a prototype real-time tumor tracking system that uses implanted electromagnetic transponders for tumor position monitoring and a DMLC for real-time beam adaptation. A detailed process tree of DMLC tracking delivery was created and potential tracking-specific failure modes were identified. For each failure mode, a risk probability number (RPN) was calculated from the product of the probability of occurrence, the severity of effect, and the detectibility of the failure. Based on the insights obtained from the FMEA, commissioning and QA procedures were developed to check (i) the accuracy of coordinate system transformation, (ii) system latency, (iii) spatial and dosimetric delivery accuracy, (iv) delivery efficiency, and (v) accuracy and consistency of system response to error conditions. The frequency of testing for each failure mode was determined from the RPN value.Failures modes with RPN > or = 125 were recommended to be tested monthly. Failure modes with RPN < 125 were assigned to be tested during comprehensive evaluations, e.g., during commissioning, annual quality assurance, and after major software/hardware upgrades. System latency was determined to be approximately 193 ms. The system showed consistent and accurate response to erroneous conditions. Tracking accuracy was within 3%-3 mm gamma (100% pass rate) for sinusoidal as well as a wide variety of patient-derived respiratory motions. The total time taken for monthly QA was approximately 35 min, while that taken for comprehensive testing was approximately 3.5 h.FMEA proved to be a powerful and flexible tool to develop and implement a quality management (QM) framework for DMLC tracking. The authors conclude that the use of FMEA-based QM ensures efficient allocation of clinical resources because the most critical failure modes receive the most attention. It is expected that the set of guidelines proposed here will serve as a living document that is updated with the accumulation of progressively more intrainstitutional and interinstitutional experience with DMLC tracking.

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Analytical performance assessment and improvement by means of the Failure mode and effect analysis (FMEA).

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Analytical performance assessment and improvement by means of the Failure mode and effect analysis (FMEA).

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