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NUR 630 TOPIC 5 DQ 2

Sample Answer for NUR 630 TOPIC 5 DQ 2 Included After Question

Identify which one of the following approaches you would choose to assist in determining and measuring outcomes: FMEA, Pareto principle, and control charts. Describe the best approach and explain why you chose it. 

A Sample Answer For the Assignment: NUR 630 TOPIC 5 DQ 2

Title: NUR 630 TOPIC 5 DQ 2

Katherine 

There are different approaches to assist in determining and measuring outcomes. According to Liu et al. (2019), Failure Modes and Effects Analysis (FMEA) is a quality improvement (QI) too that identifies failure modes, Individual Failure Mechanisms (IFMs), and System Failure Mechanisms (SFMs). The FMEA also ranks failure mechanisms by priority and associates a risk level with each (Liu et al., 2019). 

The Pareto chart analyzes the contributing factors to an event or outcome and displays data on a bar graph (Institute for Healthcare Improvement, 2022). The Pareto principle involves the idea that a few essential factors may frequently occur and contribute to a result (Institute for Healthcare Improvement, 2022). 

Control charts track processes over a determined period, focusing on the points above or below the average (Shaikh, 2021). The control chart looks at the variation in the data, calculates the standard deviation, and includes the upper control limit (UCL) and the lower control limit (LCL), which are typically several standard deviations away from the average (Shaikh, 2021). According to Shaikh (2021), points beyond the UCL or LCL are “special causes,” while the points within are “common causes.” The control chart is the best tool to measure outcomes because it clearly shows the processes that are within control and out of control over time and displays results from the operations that are currently in place (Shaikh, 2021). The control chart makes positive or negative outcomes easy to identify and target for QI purposes (Shaikh, 2021).  

References 

Institute for Healthcare Improvement. (2022). Pareto chart. Ihi.Org. http://www.ihi.org/resources/Pages/Tools/ParetoDiagram.aspx 

Liu, Y., Shen, G. T., Zhao, Z. Y., & Wu, Z. W. (2019). Risk Assessment of Failure Mode and Effects Analysis (FMEA) Under Hesitant Fuzzy Information. Insight – Non-Destructive Testing and Condition Monitoring, 61(4), 214–221. https://doi.org/10.1784/insi.2019.61.4.214 

Shaikh, U. (2021). Strategies and approaches for tracking improvements in patient safety. Psnet.Ahrq.Gov. https://psnet.ahrq.gov/primer/strategies-and-approaches-tracking-improvements-patient-safety 

Response 

Hello Katherine, 

This is insightful Katherine. FMEA (failure mode and effects analysis) is a technique that helps you identify potential problems with a product or process, and it’s often used in the early stages of product development (Liu et al., 2019). The idea is to identify all the ways a product or process could fail, and then rank those failures according to how serious they would be. You can also come up with solutions for each failure mode (Yue et al., 2017). The Pareto principle, also known as the 80/20 rule, states that 80% of the effects come from 20% of the causes. In other words, it’s usually not worth trying to fix problems that affect only a small percentage of your customer base (Bhattacharjee et al., 2020). You should focus on fixing the biggest problems first. The control chart is the best tool to measure outcomes. It can be used to measure the stability of a process, and also to detect when a process has changed and may be producing different results. 

References 

Bhattacharjee, P., Dey, V., & Mandal, U. K. (2020). Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Safety Science, 132, 104967. https://doi.org/10.1016/j.ssci.2020.104967 

Liu, Y., Shen, G. T., Zhao, Z. Y., & Wu, Z. W. (2019). Risk Assessment of Failure Mode and Effects Analysis (FMEA) Under Hesitant Fuzzy Information. Insight – Non-Destructive Testing and Condition Monitoring, 61(4), 214–221. https://doi.org/10.1784/insi.2019.61.4.214 

Yue, J., Lai, X., Liu, L., & Lai, P. B. (2017). A new VLAD‐based control chart for detecting surgical outcomes. Statistics in medicine, 36(28), 4540-4547. https://doi.org/10.1002/sim.7362 

Deanna 

There are many tools that can make Continuous Quality Improvement (CQI) easier to track and display the data. Every system that is designed to achieve results needs to have the ability to show process improvement enabling the results to be conceptualized (Fondahn et al., 2016). As stated, there are many tools to track and monitor date in a CQI process, run charts, Control charts, the Pareto principle and FMEA.  

For a CQI project of measuring if hand hygiene can reduce post cesarean birth site infections, control charts is a useful tool to measure data (Fondahn et al., 2016). A control chart is similar to a run chart to monitor the status quo of a system.  The Control chart is also useful to study how a process can change overtime and the primary purpose is it display the variance in the existing system (Fondahn et al., 2016).   

In the example of monitoring hand hygiene to reduce C-section SSI’s it the ability to monitor the data of how an existing hand hygiene program can change when implemented in a stricter manner. The control chart would compare the control rate of proposed expectations wanted in SSIs to the actual data collected. In the case of SSIs, the control rate is zero this would be compared to the number of SSIs related to C-Sections. As well as a control rate of 100% expectation to have through hand hygiene, compared to the actual rate of hand hygiene performed through audits (Allen et al., 2009). The comparison of attitude, behavior would be compared to control rates. The implementation of the education and expectation would be compared before and after the change revealing if the process change made a difference. Control charts are used to separate common cause variations from special cause variations leading to showing if the change was an improvement or not.  

References 

Allen, L., Tariman, J., & Simonovich, S. (2009). The efficacy of a web-based, educational quality improvement project on hand hygiene to reduce post-cesarean birth surgical site infection. Journal of Nursing Practice Applications and Reviews of Research. https://doi.org/10.13178/jnparr.2021.11.02.1106 

Fondahn, E., Lane, M., & Vannucci, A. (2016). The Washington manual of patient safety and quality improvement [e-book]. Wolters Kluwer. 

Response  

This is insightful Deanna, the control chart is created by taking data points from a process, plotting them on a graph, and then using statistics to determine whether the data points are randomly distributed or not. If they are randomly distributed, then the process is stable and no changes need to be made (Woodall, 2017). If they are not randomly distributed, then this indicates that the process has changed and further investigation is needed. A control chart is used to monitor the stability of a process by tracking how the output of the process varies over time (Snee & Lancaster, 2020). It can be used to detect when there is a shift in the output of the process and determine whether that shift is due to random variation or a change in the process (Votto et al., 2020). For a CQI project of measuring if hand hygiene can reduce post cesarean birth site infections, control charts is a useful tool to measure data. 

References 

Snee, R. D., & Lancaster, P. A. (2020). Process Monitoring, Control and Improvement A Systematic Approach. https://www.researchgate.net/profile/Ron-Snee/publication/346728074_Process_Monitoring_Control_and_Improvement_A_Systematic_Approach/links/5fcfc6fa45851568d14d5e0a/Process-Monitoring-Control-and-Improvement-A-Systematic-Approach.pdf 

Votto, R., Lee Ho, L., & Berssaneti, F. (2020). Applying and assessing performance of earned duration management control charts for EPC project duration monitoring. Journal of Construction Engineering and Management, 146(3), 04020001. https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0001765 

Woodall, W. H. (2017). Bridging the gap between theory and practice in basic statistical process monitoring. Quality Engineering, 29(1), 2-15. https://doi.org/10.1080/08982112.2016.1210449 

TANYA 

FMEA is a QI tool developed in the automotive and aerospace engineering industries to identify, prioritize, and mitigate failures and errors of different system designs. FMEA has been adapted for use in health care settings to proactively assess and improve complex health care processes. The Joint Commission on Accreditation of Health Care Organizations recommends FMEA as a risk management model for U.S. healthcare organizations (Tamene et al., 2020). The Institute for Healthcare Improvement also includes FMEA in its QI Essentials Toolkit. Components of which are familiar to community health centers CHCs and routinely utilized in QI efforts. The main objective of FMEA is to identify potential failure modes this means ways in which something might fail. FMEA assess the causes and effects of the different failure modes, and develop and use strategies to eliminate or reduce the likelihood of the failure (Tamene et al., 2020). It is helpful in uncovering what failures have the greatest potential for detrimental effects on a system and devising performance improvements before system design or during implementation. Additionally, the Institute for Healthcare Improvement’s Toolkit supports conducting FMEA in the fast-paced and time-constrained environment of health care (Tamene et al., 2020). 

The Pareto principle, usually referred to as the 80-20 rule, suggests that across a continuum, errors are not contributed to systems equally. For example, for the specimen collection project, this was translated to the idea that approximately 80% of the end user technology usage errors were being caused by 20% of the clinical users. In areas involved with recognizing and reducing risk, the Pareto principle has successfully been used as a tool to identify and prioritize measures to improve safety. The Pareto principle suggests tailoring an intervention aimed at a narrow group of users generating the most system errors could considerably reduce error rates across the system (Fondahn et al., 2016). 

A control chart is a graph used to study how a process changes over time. It is also called a Shewhart chart, a statistical process control chart. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit, and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent or in control or if it is out of control, affected by special causes of variation causing it to be unpredictable (Fondahn et al., 2016). This multipurpose data collection and analysis tool can be used by a variety of industries and is considered one of the seven basic quality tools. 

Control charts for variable data are used in pairs. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range, or the width of the distribution. If your data were shots in target practice, the average is where the shots are clustering, and the range is how tightly they are clustered. Control charts for attribute data are used separately or individually (Fondahn et al., 2016). 

Although each model approach can be used to assist in determining and measuring outcomes, the Failure mode and effects analysis (FMEA) can be used best to determine the QI projects predicable obstacles. Obstacles can be avoided or manageable if they are planned in advance. The FMEA is one such proactive system that was originally developed by the US military. This tool evaluates the processes, identifies the potential failures, and develops methods to prevent potential failures from happening (Fondahn et al., 2016). The Joint Commission has required accredited health care organizations to develop similar risk management strategies. FMEA reviews the steps of the process to identify how and where it might fail. For example, the preoperative clinic staff can view all of the steps involved in the patient discharge process. One important step to this process is obtaining preoperative labs (Fondahn et al., 2016). Key components to the FMEA are answering these three questions related to obtaining preoperative labs. First question is what could go wrong? (Failure modes) example, the nurses cannot draw patient blood. Next question, why would the failure happen? (Failure causes) examples: patient has a difficult venous access. Patient has a port and nurse may not be trained to access the port, provider is inexperienced, or patient refuses. Finally, what would be the consequence of each failure? (Failure effects) example of failure effects would be delay in clinic discharge and boarding next patient, longer patient waiting time and patient dissatisfaction, and potential delay of surgery are all examples of foreseeable FMEA outcomes (Fondahn et al., 2016). 

References 

Fondahn, E., Fer, T. M. D., Lane, M., & Vannucci, A. (2016). The washington manual® of patient safety and quality improvement. Wolters Kluwer Health. 

Tamene, M., Morris, A., Feinberg, E., & Bair-Merritt, M. H. (2020). Using the quality improvement (QI) tool Failure Modes and Effects Analysis (FMEA) to examine implementation barriers to common workflows in integrated pediatric care. Clinical Practice in Pediatric Psychology, 8(3), 257–267. https://doi-org.lopes.idm.oclc.org/10.1037/cpp0000365 

Response  

This is insightful Tanya, FMEA is a quality improvement tool that can be used to identify potential sources of errors or defects in a process. It is typically used during the design phase of a project, but can also be employed during manufacturing or service delivery (Doshi & Desai, 2017). FMEA involves listing all potential failure modes for a process, along with the potential effects of each failure mode. The aim is to then identify and implement controls or corrective actions that will eliminate or mitigate the risks associated with each failure mode. When used effectively, FMEA can be an invaluable tool for reducing errors and defects in any process (Kholif et al., 2018). Failure mode and effects analysis is a powerful quality improvement tool that can be used to identify potential failure modes in a process and determine the associated effects. Additionally, FMEA can be used to prioritize quality improvement initiatives based on the potential severity of the identified failure modes (Jain, 2017). When used correctly, FMEA can be an extremely valuable asset in any organization’s quality improvement arsenal. 

References 

Doshi, J., & Desai, D. (2017). Application of failure mode & effect analysis (FMEA) for continuous quality improvement-multiple case studies in automobile SMEs. International Journal for Quality Research, 11(2), 345. http://www.ijqr.net/journal/v11-n2/7.pdf 

Jain, K. (2017). Use of failure mode effect analysis (FMEA) to improve medication management process. International Journal of Health Care Quality Assurance. https://www.emerald.com/insight/content/doi/10.1108/IJHCQA-09-2015-0113/full/html 

Kholif, A. M., Abou El Hassan, D. S., Khorshid, M. A., Elsherpieny, E. A., & Olafadehan, O. A. (2018). Implementation of model for improvement (PDCA‐cycle) in dairy laboratories. Journal of Food Safety, 38(3), e12451. https://doi.org/10.1111/jfs.12451 

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