Tight Control Limits Research Articles

Evaluation of Microbiological Cleanliness of Machines/Equipment through Rinse Technique Using Statistical Process Control

To guarantee that patients receive safe therapy with predictable and acceptable medicinal properties, monitoring the quality standards in the healthcare sector and the pharmaceutical business, in particular, is essential. Medicinal products are no exception from these crucial characteristics and hence mitigation of the sources of microbial contamination is a mandatory strategy to avoid harming already-ill populations. Machines, equipment and tools that are used in the industry must be appropriately cleaned to ensure that they will not contaminate the product under processing. The study herein aimed to establish an evaluation system for cleaning efficiency through the rinse technique using Statistical Process Control (SPC) methodologies. A database was established and created for the recorded cleaning process over 20 months of the monitoring period. The control charts were constructed and evaluated from processed data using SPC software. A rare event control chart was used to track the cleaning process intervals with event probability estimated to be 0.080. Concerning the monitoring of the bioburden of rinse samples, the most appropriate fitting attribute chart is U type with Laney modification of over-dispersion to correct for tight control limits that increase alarming false points. Understanding the inspection property is inevitable for the right interpretation of trends.

A hybrid decision support system for heart failure diagnosis using neural networks and statistical process control

Timing and precision are two primary keys for myocardial infarction diagnosis. Even minor errors in diagnosis can dramatically increase the treatment process, increase treatment costs, and put the patient in dangerous health states. This paper presents a decision support system (DSS) based on neural networks and statistical process control charts for diagnosing and controlling myocardial infarction (MI) and continuously monitoring the patient’s blood pressure. To this aim, the data was collected from 175 medical documents of patients with MI and 92 recorded successful diagnoses of MI type. A group of patients was used to validate the effectiveness of the proposed system. The results prove that the proposed hybrid model can diagnose MI with high accuracy and precision compared to machine learning algorithms. Tighter control limits with a confidence level of L=2 (confidence interval of 95.45%) in the control determination stage and wider control limits with a confidence level of L=3 (confidence interval of 99.73%) in the condition determination stage led to higher overall accuracy. This method can help physicians make better decisions on diagnosing cardiovascular diseases.

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

This research discusses the comparison between two control charts classical & robust for both types (EWMA-SMQ) and (EWMA-SM) for the exponentially weighted moving average, which is showed that the robust (EWMA-SMQ) chart for the process is a superior alternative to the (EWMA-SM) chart when the outliers are present in the data. Generally, the (EWMA-SMQ) chart enables easier detection of outliers in the subgroups and is also more sensitive to other forms of out-of-control situations when outliers are present. Hence, the (EWMA-SMQ) becomes a preferred alternative to be taken and applied in quality control. The data used in this research represent the weights of the Al-Sabah Iraqi newspaper published by the Iraqi media networks. Twenty-five samples were taken and each sample consisted of five observations. These samples were taken at different production times, as the average weight of the newspaper was approximately (150) g. Through the application of the classical control chart (EWMA), exit four points for the upper and lower control limits, and the application of (EWMA-SM), (4) points are out of the upper and lower limits of the control; the control limits have been extended from the top and bottom sides of the (EWMA-SM) chart, making it less sensitive to the diagnosis of the shift in the mean process. The (EWMA-SMQ) chart detected extra points out of control. There were (7) points out of the upper and lower limits for the control, tight control limits of the (EWMA-SMQ) chart from the top and bottom sides. Due to this narrowness, three additional points were detected. Therefore (EWMA-SMQ) chart is more robust than the (EWMA-SM) control chart. This research aims to make a comparative study between the classical and robust for both types (EWMA-SM) & (EWMA-SMQ) for the exponentially weighted moving average control chart. The most important result of the research that the (EWMA-SMQ) chart is more suitable than the (EWMA-SM) & classical (EWMA) when outliers are present in the data. This gives importance to it to control the quality of the product. By this robust chart, the outliers must be detected, investigated and the special cause removed if possible. The presence of outliers will reduce the sensitivity of a control chart. The (EWMA-SMQ) control chart is more sensitive to out-of-control conditions when outliers are present in data. The limits computed from the estimate of the interquartile ranges for the (EWMA-SMQ) chart are less influenced by outliers than the (EWMA-SM) chart where the limits are computed based on the sample ranges. Thus the (EWMA-SMQ) chart is more robust than the (EWMA-SM) chart.

Use of e-Learning to Improve Perinatal Safety

To determine if blending existing educational strategies with the use of an online, assessment-driven learning platform could improve perinatal outcomes. Maternal morbidity and mortality are rising in the United States in contrast to global declines. Delays in diagnosis or treatment and failure to recognize patients at risk are preventable contributing factors. Deficits in staff knowledge or training are common causative elements in preventable maternal deaths. Rising numbers of local perinatal emergencies prompted action. Through interprofessional partnerships, the online learning platform was implemented across a multisite health system. Individualized learning paths were created within the platform on the basis of proficiency scores. Scores were further evaluated to guide a tiered educational follow-up plan. Simulation objectives and educational toolkits focused on practice-area gaps. To determine whether the change resulted in improvement, rates per 1,000 deliveries were calculated for obstetric hemorrhage, massive transfusion (≥4 units packed red blood cells), and maternal intensive care unit admission. Baseline data were collected from October 1, 2016, through March 31, 2017. The performance period was identified from April 1, 2017, through March 31, 2018. A 3% decrease in the average rate of obstetric hemorrhage was realized. Tighter control limits were noted during the performance period, indicating less variation and a more reliable process. A downward shift was identified in massive transfusion rates (≥4 units red blood cells), with 0 events recorded for 5 consecutive months. Average massive transfusion rates decreased by 8%, and the median rate decreased by 35%. Similarly, the average rate of maternal intensive care unit admission decreased by 48% and the median rate decreased by 77%. A downward shift was supported, with 0 intensive care unit admits for 6 of the last 7 months. An estimated savings of $162,262 was realized within the first 2 years. Data-driven education reduces waste by right-sizing training. Tracking clinical outcomes validates the impact on patient care. Intentional focus on closing gaps in knowledge and training in obstetric emergencies for nurses and providers may help reduce maternal morbidity and mortality.

SU‐D‐201‐04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control

Purpose:to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools.Methods:Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field‐of‐view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out‐of‐control and out‐of‐specification events were determined.Results:Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out‐of‐control events, the mean in‐control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy.Conclusion:The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in‐specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on‐going to determine if Agility performance can be optimized further using high‐frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.

Statistical process control charts for attribute data involving very large sample sizes: a review of problems and solutions

The use of statistical process control (SPC) charts in healthcare is increasing. The primary purpose of SPC is to distinguish between common-cause variation which is attributable to the underlying process,...

Operational Risk Management in Social Services Contracting

Governments are increasingly contracting with not-for-profit providers to deliver on policy objectives. These organisations are perceived to be cost-effective, flexible and close to their communities. Yet contracting out raises financial, service and reputational risks. Public sector funders receive a plethora of advice on how to balance their operational risks when contracting; however not-for-profit organisations state that funders’ tight control limits their autonomy and effectiveness. This research, into how funders assess and manage risk, links public sector risk management to a dichotomous risk theory. While some funders practised process-based risk management, others practice performance-based risk management. Central government advice, organisational culture, and experiences are the main contingent factors that appear to influence a funders’ risk management choices. As a dynamic model, these factors have their strongest affect when there is a risk failure, or when the factors combine so that public funders recognise their interdependence with private not-for-profit providers.

Control charts based on linear combinations of order statistics

The last 20 years have seen an increasing emphasis on statistical process control as a practical approach to reducing variability in industrial applications. Control charts are used to detect problems such as outliers or excess variability in subgroup means that may have a special cause. We describe an approach to the computation of control limits for exponentially weighted moving average control charts where the usual statistics in classical charts are replaced by linear combinations of order statistics; in particular, the trimmed mean and Gini's mean difference instead of the mean and range, respectively. Control limits are derived, and simulated average run length experiments show the trimmed control charts to be less influenced by extreme observations than their classical counterparts, and lead to tighter control limits. An example is given that illustrates the benefits of the proposed charts. parameters; see, for example, Hunter (1986) and Montgomery (1996). On the other hand, EWMA charts have been shown to be more efficient than Shewharttype charts in detecting small shifts in the process mean; see, for example, Ng & Case (1989), Crowder (1989), Lucas & Saccucci (1990), Amin & Searcy (1991) and Wetherill & Brown (1991). In fact, the EWMA control chart has become popular for monitoring a process mean; see Hunter (1986) for a good discussion. More recently, EWMA charts have been developed for monitoring process variability;

Quality control parameters for susceptibility tests with 30-micrograms netilmicin disks.

A nine-laboratory cooperative study was performed to evaluate quality control parameters for susceptibility tests with 30-micrograms netilmicin disks. The applicability of currently recommended zone-size limits was confirmed. However, alternative statistical methods suggested that tighter control limits might be more appropriate but would require better standardization of reagents, especially Mueller-Hinton agars.