Utilizing the Failure Mode and Effects Analysis Tool to Assess and Address Risks Associated with Transitions in Care

2021 ◽  
Vol 24 (2) ◽  
pp. 47-52
Author(s):  
Sarah Corkey ◽  
Terry Holland
Keyword(s):  
Failure Mode ◽  
Analysis Tool ◽  
Transitions In Care

scholarly journals A Bayesian risk assessment of the COVID-19 pandemic using FMEA and a modified SEIR epidemic model

2021 ◽  
Vol 12 ◽  
pp. 14
Author(s):  
Yacine Koucha ◽  
QingPing Yang
Keyword(s):  
United Kingdom ◽  
Bayesian Inference ◽  
Failure Mode ◽  
Reproduction Number ◽  
Lessons Learned ◽  
Infectious Period ◽  
Analysis Tool ◽  
The United Kingdom ◽  
Action Measures ◽  
The Impact

The COVID-19 outbreak is of great concern due to the high rates of infection and the large number of deaths worldwide. In this paper, we considered a Bayesian inference and failure mode and effects analysis of the modified susceptible-exposed-infectious-removed model for the transmission dynamics of COVID-19 with an exponentially distributed infectious period. We estimated the effective reproduction number based on laboratory-confirmed cases and death data using Bayesian inference and analyse the impact of the community spread of COVID-19 across the United Kingdom. We used the failure mode and effects analysis tool to evaluate the effectiveness of the action measures taken to manage the COVID-19 pandemic. We focused on COVID-19 infections and therefore the failure mode is taken as positive cases. The model is applied to COVID-19 data showing the effectiveness of interventions adopted to control the epidemic by reducing the reproduction number of COVID-19. Results have shown that the combination of Bayesian inference, compartmental modelling and failure mode and effects analysis is effective in modelling and studying the risks of COVID-19 transmissions, leading to the quantitative evaluation of the action measures and the identification of the lessons learned from the governmental measures and actions taken in response to COVID-19 in the United Kingdom. Analytical and numerical methods are used to highlight the practical implications of our findings. The proposed methodology will find applications in current and future COVID-19 like pandemics and wide quality engineering.

FMECA Modeling - A New Approach

1999 ◽  
Author(s):  
Yizhak Bot
Keyword(s):  
Failure Mode ◽  
Concurrent Engineering ◽  
Analysis Tool ◽  
New Approach ◽  
Mode Effects ◽  
Full Support ◽  
Computerized Analysis ◽  
Engineering Environment ◽  
New Vision ◽  
Criticality Analysis

Abstract FMECA tools must be included into the designers tools suite in order to support the new vision for a concurrent engineering environment. To support this new vision we introduce a major enhancement to the Failure Mode Effects and Criticality Analysis (FMECA) to provide a more accurate, simpler, accessible and frequently used computerized analysis tool. We introduce new terminology to enhance the well-known standards, while assuring their full support. Using the new terminology, functional trees become very similar to the project trees and can be drawn as block diagrams, e.g. the time spent on constructing the functional trees is reduced by factors.

DRIFT: A Data-driven Failure Mode, Effects and Criticality Analysis Tool

2018 ◽  
pp. 285-290
Author(s):  
Davide Zanardi ◽  
Manuele Barbieri ◽  
Giovanni Uguccioni
Keyword(s):  
Failure Mode ◽  
Data Driven ◽  
Analysis Tool ◽  
Mode Effects ◽  
Criticality Analysis

scholarly journals Use of the failure mode and effect analysis tool in the clinical medication process in an intensive care unit

2021 ◽  
Vol 12 (3) ◽  
pp. 606
Author(s):  
Kamila M. SIDNEY ◽  
Elana F. CHAVES ◽  
Henrique M. COSTA ◽  
Geysa A. ROMEU ◽  
Marta F. FONTELES
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Drug Interaction ◽  
Failure Mode ◽  
Failure Modes ◽  
Anatomical Therapeutic Chemical ◽  
Analysis Tool ◽  
Cross Sectional ◽  
Multiprofessional Team ◽  
Medication Process

Objective: To describe failure modes and establish contingency measures related to the clinical medication process using medical prescriptions of patients admitted to an Intensive Respiratory Therapy Unit (UTIR), using the Failure Mode and Effects Analysis (FMEA) tool. Methods: This is a descriptive and cross-sectional study carried out in an Intensive Care Unit of a public hospital in Fortaleza, Brazil, from November/2015 to March/2016. Study population included adults aging ≥ 18 years in intensive care at the UTIR. The study included the medical prescriptions released on Mondays, Wednesdays, and Fridays. The study was divided in five phases: situational diagnosis, formation of a multiprofessional team, assessment of failure modes (FM), monitoring of FM and calculation of the priority coefficient (PC). In the FM assessment, scoring of the three indicators of the FMEA was used within a range of 1-10, whereas a score of 10 characterized the most concerning situation. Therefore, the indicators gravity (G), prevalence (P) and detection (D) were analyzed. The study was carried out with an active interaction between the subjects of the group and several in-person and virtual sessions were performed. Drugs used in the study were categorized for therapeutic class, according to the Anatomical Therapeutic Chemical Classification System. Data analysis was performed using Microsoft Office Excel® 2013 software. Results: 301 prescriptions were analyzed, with the identification of 452 FMs, which related mostly to systemic antibacterials (21.6%, n = 8), psycholeptics (13.5%, n = 5) and antithrombotic agents (10.8%, n = 4). FMs were divided in eleven categories, from which “drug interaction” (36.8%; n = 14), “dose adjustment” (21.1%, n = 8) and “food-drug interaction” (7.9%, n = 3) were the most frequent. The PC of the detected FMs varied between 28 and 294, and 42.1% (n = 16) of them presented PC above 100. Median of the indicators G (6 – min: 3; max: 9), D (7 – min: 3; max 7) and priority coefficient (72 – min: 28; max: 294) indicate that FM had generally moderate gravity, low prevalence and low detection. For the majority of FMs (72.7%, n = 28), the chosen conduct was ‘not to accept’ and the established contingency measure included a sentinel event notification. Conclusion: The use of FMEA enabled the identification, classification, and prioritization of risks of the clinical medication process in the UTIR. This study indicates the need to implement measures that increase safety in the clinical practice of the study Intensive Care Unit.

scholarly journals Using Healthcare Failure Mode and Effect Analysis Tool to Review the Process of Ordering and Administrating Potassium Chloride and Potassium Phosphate

2005 ◽  
Vol 8 (sp) ◽  
pp. 73-80 ◽  
Author(s):  
Rosmin Esmail ◽  
Cheryl Cummings ◽  
Deonne Dersch ◽  
Greg Duchscherer ◽  
Judy Glowa ◽  
...  
Keyword(s):  
Potassium Chloride ◽  
Failure Mode ◽  
Potassium Phosphate ◽  
Analysis Tool ◽  
Effect Analysis

Analytical electron microscopy of grain boundaries in Al-Cu metallizations

1992 ◽  
Vol 50 (2) ◽  
pp. 1684-1685
Author(s):  
J. R. Michael ◽  
A. D. Romig ◽  
D. R. Frear
Keyword(s):  
Electron Microscopy ◽  
Integrated Circuits ◽  
Failure Mode ◽  
Current Density ◽  
Thermal History ◽  
Analytical Electron Microscopy ◽  
Cu Metallization ◽  
Analytical Electron ◽  
Interconnect Lines

Al with additions of Cu is commonly used as the conductor metallizations for integrated circuits, the Cu being added since it improves resistance to electromigration failure. As linewidths decrease to submicrometer dimensions, the current density carried by the interconnect increases dramatically and the probability of electromigration failure increases. To increase the robustness of the interconnect lines to this failure mode, an understanding of the mechanism by which Cu improves resistance to electromigration is needed. A number of theories have been proposed to account for role of Cu on electromigration behavior and many of the theories are dependent of the elemental Cu distribution in the interconnect line. However, there is an incomplete understanding of the distribution of Cu within the Al interconnect as a function of thermal history. In order to understand the role of Cu in reducing electromigration failures better, it is important to characterize the Cu distribution within the microstructure of the Al-Cu metallization.

Ethnogeriatrics: Development and Application of the Cohort Analysis Tool

2014 ◽  
Vol 21 (2) ◽  
pp. 37-52
Author(s):  
Melen McBride
Keyword(s):  
Healthcare Professionals ◽  
Cohort Analysis ◽  
Analysis Tool ◽  
Speech Language Pathology ◽  
Clinical Tool ◽  
Research Activities ◽  
Key Concepts ◽  
Practice Teaching ◽  
Language Pathology ◽  
Significant Factors

Ethnogeriatrics is an evolving specialty in geriatric care that focuses on the health and aging issues in the context of culture for older adults from diverse ethnic backgrounds. This article is an introduction to ethnogeriatrics for healthcare professionals including speech-language pathologists (SLPs). This article focuses on significant factors that contributed to the development of ethnogeriatrics, definitions of some key concepts in ethnogeriatrics, introduces cohort analysis as a teaching and clinical tool, and presents applications for speech-language pathology with recommendations for use of cohort analysis in practice, teaching, and research activities.

“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

2011 ◽  
Vol 21 (2) ◽  
pp. 44-54
Author(s):  
Kerry Callahan Mandulak
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Clinical Utility ◽  
Acoustic Analysis ◽  
Moment Analysis ◽  
Analysis Tool ◽  
Spectral Moment ◽  
Clinical Measure ◽  
Perceptual Analysis ◽  
Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

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