Risk identification of a hospital laboratory pre-analytics through failure mode and effect analysis

2021 ◽  
Vol 12 (4) ◽  
pp. 31-38
Author(s):  
Debdatta Das ◽  
Krishna Pal ◽  
Sudip Roy ◽  
Moushumi Lodh
Keyword(s):  
Patient Safety ◽  
Failure Mode ◽  
Laboratory Testing ◽  
Failure Modes ◽  
Risk Identification ◽  
Post Intervention ◽  
Effect Analysis ◽  
Laboratory Errors ◽  
Corrective Actions ◽  
Analytical Phase

Background: Implementing an active system to identify, monitor and manage risk from laboratory errors can enhance patient safety and quality of care. Aims and Objectives: Failure Mode and Effect Analysis (FMEA) technique allows evaluating and measuring the hazards of a process malfunction, to decide where to execute improvement actions, and to measure the outcome of those actions. The aim of this study was to assess pre analytical phase of laboratory testing, mitigate risk and thereby increase patient safety. Materials and Methods: Steps followed in the study were: planning the study, selecting team members, analysis of the processes, risk analysis, and developing a risk reduction protocol by incorporating corrective actions. A Fault Tree Analysis diagram was used to plot the cascade of faults leading to the pre analytical errors. Risk Priority Number (RPN) was assigned. A minimum cut- off 40 RPN was considered for interventions and highest RPN errors were prioritized with corrective actions. Post intervention RPN score was calculated. Results: Eight failure modes had the highest RPN. Corrective actions were prioritized against these errors. RPN scores of test ordering error, sample collection error, transport errors, error in patient identification, site selection, urine samples not received, sample accessioning and sample processing errors decreased, post intervention. Conclusion: With thorough planning, we can use FMEA as a common standard to analyze risk in pre analytical phase of laboratory testing.

Use of failure mode effect analysis (FMEA) to improve medication management process

2017 ◽  
Vol 30 (2) ◽  
pp. 175-186 ◽  
Author(s):  
Khushboo Jain
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Medication Management ◽  
Management Process ◽  
Effect Analysis ◽  
Content Type ◽  
Complex Process ◽  
Mode Effect ◽  
Corrective Actions ◽  
Failure Mode Effect Analysis

Purpose Medication management is a complex process, at high risk of error with life threatening consequences. The focus should be on devising strategies to avoid errors and make the process self-reliable by ensuring prevention of errors and/or error detection at subsequent stages. The purpose of this paper is to use failure mode effect analysis (FMEA), a systematic proactive tool, to identify the likelihood and the causes for the process to fail at various steps and prioritise them to devise risk reduction strategies to improve patient safety. Design/methodology/approach The study was designed as an observational analytical study of medication management process in the inpatient area of a multi-speciality hospital in Gurgaon, Haryana, India. A team was made to study the complex process of medication management in the hospital. FMEA tool was used. Corrective actions were developed based on the prioritised failure modes which were implemented and monitored. Findings The percentage distribution of medication errors as per the observation made by the team was found to be maximum of transcription errors (37 per cent) followed by administration errors (29 per cent) indicating the need to identify the causes and effects of their occurrence. In all, 11 failure modes were identified out of which major five were prioritised based on the risk priority number (RPN). The process was repeated after corrective actions were taken which resulted in about 40 per cent (average) and around 60 per cent reduction in the RPN of prioritised failure modes. Research limitations/implications FMEA is a time consuming process and requires a multidisciplinary team which has good understanding of the process being analysed. FMEA only helps in identifying the possibilities of a process to fail, it does not eliminate them, additional efforts are required to develop action plans and implement them. Frank discussion and agreement among the team members is required not only for successfully conducing FMEA but also for implementing the corrective actions. Practical implications FMEA is an effective proactive risk-assessment tool and is a continuous process which can be continued in phases. The corrective actions taken resulted in reduction in RPN, subjected to further evaluation and usage by others depending on the facility type. Originality/value The application of the tool helped the hospital in identifying failures in medication management process, thereby prioritising and correcting them leading to improvement.

scholarly journals Failure Mode and Effect Analysis for a military nose landing gear project

2021 ◽  
Vol 13 (4) ◽  
pp. 205-212
Author(s):  
Ilie NICOLIN ◽  
Bogdan Adrian NICOLIN
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Military Training ◽  
Landing Gear ◽  
System Operation ◽  
Effect Analysis ◽  
Equipment Failures ◽  
Corrective Actions ◽  
Fmea Method ◽  
Product Description

Failure Mode and Effect Analysis (FMEA) techniques were originally developed by the US Military and have been used as techniques for assessing the reliability and effects of equipment failures. However, the first notable applications of FMEA techniques are related to the impressive development of the aerospace industry in the mid-1960s. FMEA is a methodology for systematically analyzing the failure modes of a project, product or process, prioritizing their importance, identifying system failure mechanisms, analyzing potential failure modes and the effects of these failures, followed by corrective actions, which are applied in the stage of conceptual and detailed design of the product. All approaches to FMEA methods in the scientific literature converge to achieve three goals, namely: the ability to predict the type of failure that may occur, the ability to predict the effects of the failure on system operation, and the establishment of the steps to prevent failure and its effects on the system operation. The FMEA for the project of a nose landing gear analyzes the failure modes of the product and their effects in operation, as a consequence of project deficiencies and identifies or confirms critical functions. To apply the FMEA method to the project of the nose landing gear of a military training aircraft, the following steps need to be accomplished: product description and identification of components; identification of functions; identification of potential ways of failure; estimating the frequency of causes of failure; appreciation of the severity of effects; assessment of difficulties in detecting defects; calculation of the Risk Priority Number (RPN); establishing the measures and corrective actions for the analyzed project.

scholarly journals HEALTHCARE FAILURE MODE AND EFFECT ANALYSIS DESIGN FOR INDONESIA HOSPITAL LABORATORIES: A LITERATURE REVIEW

2021 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Zhafirah Salsabila ◽  
Masyitoh Masyitoh ◽  
Amal Chalik Sjaaf ◽  
Lia Gardenia Partakusuma
Keyword(s):  
Patient Safety ◽  
Failure Mode ◽  
Error Rate ◽  
Health Sector ◽  
Monitoring And Evaluation ◽  
Patient Safety Culture ◽  
Risk Identification ◽  
Background Error ◽  
Effect Analysis ◽  
Laboratory Staff

Background: Error rate in medical laboratories is very low. Only one error is identified every 330–1,000 events. The goal of laboratory services should outweigh patient safety in a well-structured manner. Healthcare Failure Mode and Effect Analysis (HFMEA) is a proactive preventive method for identifying and evaluating potential failure.Aims: This study identified factors affecting patient safety in hospital laboratories and described potential risk identification process using the HFMEA.Methods: This study was conducted between March-July 2020 and retrieved data from PubMed, Scopus, and Google Scholar. The data were generalized and extracted into Table 2 based on factors dealing with patient safety in hospital laboratories. This study performed a risk identification design based on the steps of HFMEA.Results: Out of 4,062 articles collected, only 8 articles between 2013–2020 were included for analysis. The highest error rate in laboratories occurred in the pre-analytic phase (49.2%–84.5%). The errors included clotted and inadequate specimen volume, and thus the specimens were rejected. Factors related to patient safety in laboratories were patient condition, laboratory staff performance (including training, negligence, and burnout), facilities, and accreditation.Conclusion: The HFMEA process used the result of hazard analysis with severity and probability criteria categorized into health sector. Decision tree analysis could determine the next step of the work process. The HFMEA must be adjusted to the equipment and technologies in each hospital laboratory. Leader’s commitment in monitoring and evaluation is required to maintain patient safety culture. More comprehensive data from Indonesian hospital laboratories are needed to generate more representative and applicable results.Keywords: error, HFMEA, laboratory, patient safety 

Failure mode and effect analysis (FMEA) to improve collaborative project-based learning: Case study of a Study and Research Path in mechanical engineering

2021 ◽  
pp. 030641902199904
Author(s):  
Elena Bartolomé ◽  
Paula Benítez
Keyword(s):  
Active Learning ◽  
Failure Mode ◽  
Mechanical Engineering ◽  
Failure Modes ◽  
Collaborative Study ◽  
Project Based Learning ◽  
Educational Process ◽  
Effect Analysis ◽  
Questions And Answers

Failure Mode and Effect Analysis (FMEA) is a powerful quality tool, widely used in industry, for the identification of failure modes, their effects and causes. In this work, we investigated the utility of FMEA in the education field to improve active learning processes. In our case study, the FMEA principles were adapted to assess the risk of failures in a Mechanical Engineering course on “Theory of Machines and Mechanisms” conducted through a project-based, collaborative “Study and Research Path (SRP)” methodology. The SRP is an active learning instruction format which is initiated by a generating question that leads to a sequence of derived questions and answers, and combines moments of study and inquiry. By applying the FMEA, the teaching team was able to identify the most critical failures of the process, and implement corrective actions to improve the SRP in the subsequent year. Thus, our work shows that FMEA represents a simple tool of risk assesment which can serve to identify criticality in educational process, and improve the quality of active learning.

Fuzzy risk assessment for electro-optical target tracker

2016 ◽  
Vol 33 (6) ◽  
pp. 830-851 ◽  
Author(s):  
Soumen Kumar Roy ◽  
A K Sarkar ◽  
Biswajit Mahanty
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Linear Equations ◽  
Mode Analysis ◽  
Failure Behavior ◽  
Membership Functions ◽  
Design And Development ◽  
Effect Analysis ◽  
Content Type ◽  
Criticality Analysis

Purpose – The purpose of this paper is to evolve a guideline for scientists and development engineers to the failure behavior of electro-optical target tracker system (EOTTS) using fuzzy methodology leading to success of short-range homing guided missile (SRHGM) in which this critical subsystems is exploited. Design/methodology/approach – Technology index (TI) and fuzzy failure mode effect analysis (FMEA) are used to build an integrated framework to facilitate the system technology assessment and failure modes. Failure mode analysis is carried out for the system using data gathered from technical experts involved in design and realization of the EOTTS. In order to circumvent the limitations of the traditional failure mode effects and criticality analysis (FMECA), fuzzy FMCEA is adopted for the prioritization of the risks. FMEA parameters – severity, occurrence and detection are fuzzifed with suitable membership functions. These membership functions are used to define failure modes. Open source linear programming solver is used to solve linear equations. Findings – It is found that EOTTS has the highest TI among the major technologies used in the SRHGM. Fuzzy risk priority numbers (FRPN) for all important failure modes of the EOTTS are calculated and the failure modes are ranked to arrive at important monitoring points during design and development of the weapon system. Originality/value – This paper integrates the use of TI, fuzzy logic and experts’ database with FMEA toward assisting the scientists and engineers while conducting failure mode and effect analysis to prioritize failures toward taking corrective measure during the design and development of EOTTS.

scholarly journals Quality Indicators of the Pre-Analytical Phase

2012 ◽  
Vol 31 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Nada Majkić-Singh ◽  
Zorica Šumarac
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Quality Indicators ◽  
Task Force ◽  
Improve Patient Safety ◽  
Laboratory Data ◽  
Sample Collection ◽  
Laboratory Errors ◽  
Analytical Phase ◽  
Total Testing Process

Quality Indicators of the Pre-Analytical PhaseQuality indicatorsare tools that allow the quantification of quality in each of the segments of health care in comparison with selected criteria. They can be defined as an objective measure used to assess the critical health care segments such as, for instance, patient safety, effectiveness, impartiality, timeliness, efficiency, etc. In laboratory medicine it is possible to develop quality indicators or the measure of feasibility for any stage of the total testing process. The total process or cycle of investigation has traditionally been separated into three phases, the pre-analytical, analytical and post-analytical phase. Some authors also include a »pre-pre« and a »post-post« analytical phase, in a manner that allows to separate them from the activities of sample collection and transportation (pre-analytical phase) and reporting (post-analytical phase). In the year 2008 the IFCC formed within its Education and Management Division (EMD) a task force calledLaboratory Errors and Patient Safety (WG-LEPS)with the aim of promoting the investigation of errors in laboratory data, collecting data and developing a strategy to improve patient safety. This task force came up with the Model of Quality Indicators (MQI) for the total testing process (TTP) including the pre-, intra- and post-analytical phases of work. The pre-analytical phase includes a set of procedures that are difficult to define because they take place at different locations and at different times. Errors that occur at this stage often become obvious later in the analytical and post-analytical phases. For these reasons the identification of quality indicators is necessary in order to avoid potential errors in all the steps of the pre-analytical phase.

Understanding and managing interferences in clinical laboratory assays: the role of laboratory professionals

2020 ◽  
Vol 58 (3) ◽  
pp. 350-356 ◽  
Author(s):  
Martina Zaninotto ◽  
Mario Plebani
Keyword(s):  
Patient Safety ◽  
Laboratory Testing ◽  
Clinical Laboratory ◽  
Careful Evaluation ◽  
The Matrix ◽  
Analytical Phase

AbstractThe recently raised concerns regarding biotin interference in immunoassays have increased the awareness of laboratory professionals and clinicians of the evidence that the analytical phase is still vulnerable to errors, particularly as analytical interferences may lead to erroneous results and risks for patient safety. The issue of interference in laboratory testing, which is not new, continues to be a challenge deserving the concern and interest of laboratory professionals and clinicians. Analytical interferences should be subdivided into two types on the basis of the possibility of their detection before the analytical process. The first (type 1) is represented by lipemia, hemolysis and icterus, and the second (type 2), by unusual constituents that are not undetectable before analysis, and may affect the matrix of serum/plasma of individual subjects. Type 2 cannot be identified with current techniques when performing the pre-analytical phase. Therefore, in addition to a more careful evaluation and validation of the method to be used in clinical practice, the awareness of laboratory professionals should be raised as to the importance of evaluating the quality of biological samples before analysis and to adopt algorithms and approaches in the attempt to reduce problems related to erroneous results due to specific or non-specific interferences.

Risk Evaluation Approach in Failure Mode and Effect Analysis of Automotive Headlamp Assembly

2014 ◽  
Vol 564 ◽  
pp. 72-76
Author(s):  
Shukriah Abdullah ◽  
Aziz Abdul Faieza
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Assessment Tool ◽  
Action Plan ◽  
Technical Problem ◽  
Assembly Process ◽  
Preventive Action ◽  
Effect Analysis ◽  
Evaluation Approach ◽  
Process Failure

Headlamp assembly entailed a complex assembly process and error in assembled can result in technical problem and higher reject rate at the end of the assembly process. A study has been conducted, in one of the automotive headlamp assembly in Malaysia, where there are numerous defect detected during the assembly process, such as metal spacing missing, wrong model housing, wrong sticker affix, wrong orientation with a total of 80% defects detected. Currently the headlamps are assembled with no dimensional control, results in high physical nonconformity product. The main objective of this project is to identify potential failure in headlamp assembly process. The approach used was risk assessment tool which is Process Failure Mode and Effect. This work also developed the corrective action plan for accurate ranking of Failure Modes by Risk Priority Number-based method and implement it to the process assembly. The result showed that there was increased of 5% in preventive action and 4% increment of the detection action

Failure Mode and Effect Analysis (FMEA) for Enhancing Reliability of Water Tube Boiler in Thermal Power Plant

2016 ◽  
Vol 8 (02) ◽  
pp. 79-86
Author(s):  
Kapil Dev Sharma ◽  
Shobhit Srivastava
Keyword(s):  
Power Plant ◽  
Failure Mode ◽  
Thermal Power Plant ◽  
Failure Modes ◽  
Thermal Power ◽  
Effect Analysis ◽  
Water Tube ◽  
Potential Failure ◽  
Fmea Method ◽  
Continuous Power

Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.

Cost consequence of failure in failure mode and effect analysis

2017 ◽  
Vol 34 (8) ◽  
pp. 1318-1342 ◽  
Author(s):  
Jeff Guinot ◽  
John W. Sinn ◽  
M. Affan Badar ◽  
Jeffrey M. Ulmer
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Cost Data ◽  
Ordinal Scale ◽  
Automotive Application ◽  
Manufacturing Cost ◽  
Potential Cost ◽  
Effect Analysis ◽  
Content Type ◽  
Cost Consequence

Purpose The purpose of this paper is to investigate the possibility of including the cost consequence of failure in the a priori risk assessment methodology known as failure mode and effect analysis (FMEA). Design/methodology/approach A model of the standard costs that are incurred when an electronic control module in an automotive application fails in service was developed. These costs were related to the Design FMEA ranking of the level of severity of the failure mode and the probability of its occurrence. Monte Carlo simulations were conducted to establish the average costs expected for each level of severity at each level of occurrence. The results were aggregated using fuzzy utility sets into a nine-point ordinal scale of cost consequence. The criterion validity of this scale was assessed with warranty cost data derived from a case study. Findings It was found that the model slightly underestimated the warranty costs that accrued, but the fit could be improved with adjustments dictated by actual usage conditions. Research limitations/implications Cost data used in the simulations were derived from government and academic surveys, analyses, and estimates of the manufacturing cost structure; and nominal costs for various quality issues experienced by Tier 2 automotive electronics supplier. Specificity is lacking. The sample size and the type of the failure modes used to validate the model are constrained by the number and type of products which have had demonstrable performance concerns over the past three years, with cost data available to the authors. The power of the validation is limited. The validation is considered a screening assessment. Practical implications This work relates the characterization of risk with its potential cost and develops a scaling instrument to allow the incorporation of cost consequence into an FMEA. Originality/value A ranking scale was developed that related severity and occurrence rank scores to a cost consequence rank that keys to a cost of quality figure (given as percent of sales) that would accompany a realization of the failure mode.

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