Control Charts Research Articles

Standardizing visual display of gastrointestinal pathology results for improved clinical interpretation.

Pathology is an essential component of disease diagnosis and management in pediatric gastroenterology. Pathology reports have not been standardized in some areas of pediatric gastrointestinal pathology and pathology reporting varies. Development of electronic medical record (EMR) pathology synoptic report templates (PSRT) enables pathology data collection in a specific format and can help standardize pathology reporting. We developed, implemented, and evaluated EMR PSRTs for eosinophilic esophagitis (EoE) and inflammatory bowel disease (IBD). PSRTs were developed by a multidisciplinary team of pediatric experts of allergy, gastroenterology, and pathology for both EoE and IBD based on available literature and validated scales. Likert surveys (range 1 low acceptance to 5 high acceptance) based on the Technology Acceptance Model assessed user acceptance of the developed PSRTs. The use of PSRTs was monitored via control charts. Overall, evaluation questionnaires achieved >80% response rates. Clinicians and pathologists reported moderate-to-high levels of Perceived Usefulness (median (interquartile range) for EoE PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 3.5 (3.5, 4.0); and IBD PSRT: clinicians 4.0 (3.0, 4.0) and pathologists 4.0 (4.0, 5.0)) and Perceived Ease of Use (EoE PSRT: clinicians 4.5 (4.0, 5.0) and pathologists 4.0 (4.0, 4.0); and IBD PSRT: clinicians 4.0 (4.0, 5.0) and pathologists 4.0 (4.0, 5.0)) of the developed PSRTs. Control charts demonstrated 100% utilization by 2-5 months from launch. We demonstrate successful implementation of synoptic reporting for both pediatric EoE and IBD pathology. EMR synoptic reporting provides standardization of pathology reporting and improved methods of pathology data presentation, which could potentially optimize provider efficiency, clinician interpretation of pathology results and disease trajectory, patient care, and clinician satisfaction.

CT Pulmonary Angiogram Clinical Pretest Probability Tool: Impact on Emergency Department Utilization

ObjectiveCurrently, computed tomographic pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in Emergency Departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE. MethodsThe study was performed at EDs in a large healthcare system and included 9 academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPA performed/number of ED visits) and secondary outcome was yield (percentage of CTPA positive for acute PE), comparing 12 months before (6/1/2021-5/31/2022) vs. 12 months after (6/1/2022-5/31/2023) a system-wide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control (SPC) charts were used to assess monthly trends in utilization and yield. ResultsAmong 931,677 visits to Emergency Departments, 28,101 CTPAs were performed on 24,675 patients. 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention; 13,276 among 476,639 visits (2.79%) post-intervention, a 14.51% relative decrease in CTPA utilization (chi-square, p<0.001). CTPA yield remained unchanged (1371/14825=9.25% pre- vs. 1184/13276=8.92% post-intervention; chi-square, p=0.34). Patients with COVID diagnosis prior to CTPA had higher probability of acute PE. SPC charts demonstrated seasonal variation in utilization (Friedman test, p=0.047). DiscussionImplementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months post-intervention.

Reducing Automated Dispensing Cabinet Overrides in the Perianesthesia Care Unit: A Quality Improvement Project

BackgroundAutomatic dispensing cabinets (ADCs) are used to store and dispense medications at the point of care. Medications accessed from an ADCs before pharmacist order verification are removed using override functionality. Bypassing pharmacist verification can lead to medication errors; therefore, The Joint Commission considers overrides acceptable only in limited scenarios. During an 18-month period, the override rate in our peri-anesthesia care unit (PACU) was 17%, with oral midazolam accounting for roughly 40% of overrides. A multidisciplinary quality improvement (QI) project was initiated with a goal to reduce overrides by 10% (17% to 15%) by December 31, 2021. MethodsKey drivers for reducing overrides included timely medication order entry, nursing practice to wait for verification, and timely pharmacist medication order verification. Interventions related to the latter two drivers included nursing education, individual interviews, and a workflow change involving nurse-to-pharmacy communication prior to medication overrides. Interventions were implemented in three Plan-Do-Study-Act cycles beginning in July 2021. Outcome metrics were average monthly percentage of total medication overrides and overrides for oral midazolam, which were analyzed using statistical process control (SPC) charts. ResultsFollowing interventions, the average monthly percentage of total medication overrides decreased from 17% to 8% in July 2021, and further to 4% in February 2022. Oral midazolam overrides decreased from 22% to 9% in July 2021, and further to 3% in February 2022. ConclusionsBoth total and oral midazolam overrides were reduced by changing nursing and pharmacy workflow. Reducing ADC overrides is a complex process balancing operational flow and safety efforts.

Improving Antibiotic Stewardship in a Pediatric Long-term Care Facility.

Antimicrobial stewardship is little studied in pediatric long-term care facilities. We sought to determine whether empirical ciprofloxacin for febrile respiratory illnesses could be safely reduced in our pediatric long-term care facility. All patients living in the 45-bed facility were included. A 1-year educational intervention for antimicrobial stewardship was implemented. Days of ciprofloxacin therapy, infections, microbiology, hospitalizations, other antibiotic use, methicillin-resistant Staphylococcus aureus and Clostridioides difficile infections, and mortality were recorded at regular intervals retrospectively from 5 years before intervention and prospectively for 8 years after intervention. Data were analyzed using statistical process control charts. A majority of patients had tracheostomy tubes (96%) and ventilator dependence (58%). Ciprofloxacin use declined by 76% (17 to 4 days/1000 facility patient days). Antibiotic prescriptions for bacterial tracheitis decreased by 89% (38 to 4 courses per 6-month period). No increases in positive blood or urine cultures, hospitalizations, or need for hospital antibiotics were observed. An antimicrobial stewardship intervention in a pediatric long-term care facility led to decreases in ciprofloxacin use, bacterial tracheitis diagnoses, and overall antibiotic use without increasing negative outcomes.

Multivariate Exponentially Weighted Moving Average Control Chart under Neutrosophic Environment: A Bootstrap Approach

Multivariate Exponentially Weighted Moving Average Control Chart under Neutrosophic Environment: A Bootstrap Approach

Estimation of quality variables in a continuous train of reactors using recurrent neural networks-based soft sensors

The first stage in the industrial production of Styrene-Butadiene Rubber (SBR) typically consists in obtaining a latex from a train of continuous stirred tank reactors. Accurate real-time estimation of some key process variables is of paramount importance to ensure the production of high-quality rubber. Monitoring the mass conversion of monomers in the last reactor of the train is particularly important. To this effect, various soft sensors (SS) have been proposed, however they have not addressed the underlying complex dynamic relationships existing among the process variables. In this work, a SS based on recurrent neural networks (RNN) is developed to estimate the mass conversion in the last reactor of the train. The main challenge is to obtain an adequate estimate of the conversion both in its usual steady-state operation and during its frequent transient operating phases. Three architectures of RNN: Elman, GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) are compared to critically evaluate their performances. Moreover, a comprehensive analysis is conducted to assess the ability of these models to represent different operational modes of the train. The results reveal that the GRU network exhibits the best performance for estimating the mass conversion of monomers. Then, the performance of the proposed model is compared with a previously-developed SS, which was based on a linear estimation model with a Bayesian bias adaptation mechanism and the use of Control Charts for decision-making. The model proposed here proved to be more efficient for estimating the mass conversion of monomers, particularly during transient operating phases. Finally, to evaluate the methodology utilized for designing the SS, the same RNN architectures were trained to online estimate another quality variable: the mass fraction of Styrene bound to the copolymer. The obtained results were also acceptable.

A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering

Abstract Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.&amp;#xD;

Bayesian Method for Quality Control with Weibull Distribution

Weibull distribution is one of the continous probability density function. This distribution is known as a flexible distribution. One of it’s flexibility is can be transformed into other distributions such as exponential distribution depends on parameter selected. Similar to other distribution, Weibull distribution also characterized by cumulative distribution function, mean, variance and moment generating function. One of the well-known application of this distribution is in the field of quality control utilizing reliability data and the well-known tool in quality control is control chart. Therefore, reliability data is not follow normal distribution then Shewhart control chart cannot be applied. To solve this problem, control chart can be formed with the control limit is obtained by Bayesian method. In applying Bayesian method, prior distribution of shape parameter is assumed to be uniform distribution and variables for reliability is assumed to be invers of Weibull distribution. The prior distribution is combine with likelihood function then posterior distribution is obtained which in then used as the control limits by find it’s mean.

Exponentially weighted moving average chart using zero-inflated negative binomial distribution

Zero-inflated models are frequently used to deal with data having many zeros. A commonly used model for over-dispersed data containing zeros is known as the zero-inflated Poisson model. However, to account for the heterogeneity of counts that leads to excess variance besides inflation of zeros in the data using a more flexible model than the zero-inflated Poisson model, a zero-inflated negative binomial (ZINB) is suggested. In the present study, Shewhart and exponentially weighted moving average (EWMA) control charts are suggested to monitor the ZINB data. The charts are compared using the average run length and standard deviation of run length by using extensive Monte Carlo simulations. Besides a comprehensive simulation study assuming different settings of parameters of ZINB, a real data set is used to show the practicality of the proposed charts. The results indicate that the EWMA chart is better than the Shewhart chart.

Applying the six-sigma methodology to determine the limits of quality control (QC) tests for a specific linear accelerator.

We aimed to show the framework of the six-sigma methodology (SSM) that can be used to determine the limits of QC tests for the linear accelerator (Linac). Limits for QC tests are individually determined using the SSM. The SSM is based on the define-measure-analyze-improve-control (DMAIC) stages to improve the process. In the "define" stage, the limits of QC tests were determined. In the "measure" stage, a retrospective collection of daily QC data using a Machine Performance Check platform was performed from January 2020 to December 2022. In the "analyze" stage, the process of determining the limits was proposed using statistical analyses and process capability indices. In the "improve" stage, the capability index was used to calculate the action limits. The tolerance limit was established using the larger one of the control limits in the individual control chart (I-chart). In the "control" stage, daily QC data were collected prospectively from January 2023 to May 2023 to monitor the effect of action limits and tolerance limits. A total of 798 sets of QC data including beam, isocenter, collimation, couch, and gantry tests were collected and analyzed. The Collimation Rotation offset test had the min-Cp, min-Cpk, min-Pp, and min-Ppk at 2.53, 1.99, 1.59, and 1.25, respectively. The Couch Rtn test had the max-Cp, max-Cpk, max-Pp, and max-Ppk at 31.5, 29.9, 23.4, and 22.2, respectively. There are three QC tests with higher action limits than the original tolerance. Some data on the I-chart of the beam output change, isocenter KV offset, and jaw X1 exceeded the lower tolerance and action limit, which indicated that a system deviation occurred and reminded the physicist to take action to improve the process. The SSM is an excellent framework to use in determining the limits of QC tests. The process capability index is an important parameter that provides quantitative information on determining the limits of QC tests.

Interventions to improve system-level coproduction in the Cystic Fibrosis Learning Network

BackgroundCoproduction is defined as patients and clinicians collaborating equally and reciprocally in healthcare and is a crucial concept for quality improvement (QI) of health services. Learning Health Networks (LHNs) provide...

Condition monitoring and warning of a belt drive system based on a logical analysis of data regression-based residual control chart

The belt drive system is commonly used to transmit power in different industrial systems to maintain high performance and safety. Online condition monitoring techniques (CMTs) are used to monitor the operational conditions of such systems. Vibration-based monitoring techniques (VMT) are among the CMTs that are used in the analysis and diagnosis of the state of a belt drive system. Machine learning techniques are integrated with the VMT based on Industry 4.0 aspects for vibration analysis and fault diagnosis. Most of these techniques are based on the collection of vibration data from the belt drive system under known normal and different known faulty operations. This enables a fault to be diagnosed when it is detected during the operation of a system. In this paper, a new condition monitoring and warning mechanism is proposed to monitor the operational conditions of a belt drive system. The mechanism is based on an integration of a logical analysis of data regression (LADR) with a residual control chart (RCC). It uses vibration data from the belt drive system under normal operation only. This mechanism exhibits better performance in fault detection and also in interpreting the root cause of the faults in a belt drive system. Experimental investigations on a belt drive test rig have been carried out to collect vibration data based on a design of experiment for operational factors during normal operation. The LADR-RCC is implemented to monitor the operation of the belt drive system and detect faulty states. The accuracy of LADR is compared with multiple linear regression-based RCC, support vector regression-based RCC and random forest-based RCC. The LADR-RCC demonstrates significant enhancements in fault detection. The advantage of LADR-RCC over other model-based RCC is that it finds the root cause of a fault that is experienced in the system.

Enhancement of Foley Catheter Assembly Section Quality using Six Sigma (A Case Study of an Indonesian Medical Device Company)

This study aims to enhance the quality of the Foley catheter assembly section at PT XYZ, an Indonesian medical device manufacturer, using Six Sigma methodology. PT XYZ currently faces an 8% defect rate in its Foley catheter assembly section, surpassing the acceptable 3% threshold. The current Sigma level of the Shrink process within the assembly section is 2.36, while the Balloon Vulcanizing and Assembly process stands at 2.68. The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework was used to identify and address root causes of defects. Data collection methods included interviews, questionnaires, and field observations, revealing key issues such as incorrect tray loading, machine calibration errors, operator skill variability, and inadequate handling techniques. Proposed improvements include updating Standard Operating Procedures (SOPs), implementing SCAMPER for calibration, comprehensive training programs, and incentive schemes. Additional measures involve template overlays for precise cutting, vendor maintenance, and control charts for consistency. The Control phase ensures sustainability through documentation, training, and monitoring using control charts and Failure Modes and Effects Analysis (FMEA). Implementing these solutions is expected to reduce the defect rate and elevate the Sigma level of both processes to 3, enhancing process efficiency and product quality. This will improve operational efficiency and customer satisfaction, providing a robust framework for PT XYZ to elevate its quality control and contribute to better healthcare device manufacturing in Indonesia.

Assessing the economic-statistical performance of an attribute SVSSI-np control chart based on genetic algorithms

The SVSSI scheme, featuring three variable sample sizes and two variable sampling intervals, considerably enhances the performance of control charts in detecting shifts. As a novel contribution, this study proposes an optimized design of the SVSSI-np control chart using genetic algorithms, illustrating its superior performance over other sampling schemes by considering economic and statistical perspectives in a unified model. The outcomes of implementing the SVSSI alongside other sampling schemes are assessed via a numerical example, demonstrating the influence of varying parameters such as process failure rate, shift value, and proportion of nonconforming items. Based on the obtained results, the SVSSI scheme outperforms other schemes by achieving over a 10% reduction in costs and over a 30% improvement in reducing time to signal and false alarm rates in most runs. The impact of inaccurately estimating the shift size is also investigated, revealing that incorrect estimates can lead to ineffective adjustments and improvements. Finally, optimal SVSSI designs are explored through comprehensive sensitivity analysis and robust optimization to address uncertainty, and the results of applying fractional factorial design are statistically analyzed. In summary, the SVSSI-np control chart demonstrates potential for enhanced cost reduction, process monitoring, and efficiency gains.

An adaptive multivariate functional EWMA control chart

In many modern industrial scenarios, measurements of the quality characteristics of interest are often required to be represented as functional data or profiles. This motivates the growing interest in extending traditional univariate statistical process monitoring (SPM) schemes to the functional data setting. This article proposes a new SPM scheme, which is referred to as adaptive multivariate functional EWMA (AMFEWMA), to extend the well-known exponentially weighted moving average (EWMA) control chart from the univariate scalar to the multivariate functional setting. The proposed method distinguishes itself by adaptively selecting the weighting parameter in the calculation of the EWMA statistic to enhance the sensitivity of the AMFEWMA control chart across a spectrum of potential out-of-control scenarios. Such adaptability is essential in industrial processes, where multivariate functional quality characteristics are also subject to varying degrees of change. The favorable performance of the AMFEWMA control chart over existing methods is assessed via an extensive Monte Carlo simulation. Its practical applicability is demonstrated through a case study in monitoring the quality of a resistance spot welding (RSW) process in the automotive industry through online observations of dynamic resistance curves, which are associated with multiple spot welds on the same car body and are recognized as highly representative of the RSW process quality. The proposed method is implemented in the R package funcharts, available online on CRAN.

Pengendalian Kualitas Produk Arummanis dengan Peta Kendali P dan Diagram Fishbone di UMKM Arummanis AT

UMKM arummanis AT is an UMKM that produces traditional food, namely arummanis or what is often called grandmother's hair. UMKM arummanis AT often experience defects during arummanis production, which has an impact on sales of the arummanis. The defect in AT's arummanis product is that the arummanis often clumps which makes it difficult to package and results in the arummanis being unfit for sale. The method used is a P control chart and also a fishbone diagram. The results of the research show that the highest proportion of defects is 0.00134 and the lowest proportion of defects is 0.00067 with a center line (CL) of 0.000912. The fishbone diagram shows the factors that cause defects in Arummanis products which are influenced by machine, method, human, environmental and material factors. The conclusion obtained in this research is that according to the P control line analysis, the research results show that 1 point of Arummanis error exceeds the control limit, which is then corrected. The solution to minimize the occurrence of defective products in Arummanis is by creating work SOPs, carrying out routine maintenance, maintaining cleanliness by creating a cleaning schedule, and also providing job training to workers. Keywords : Defective Products, Fishbone Diagram, P Control Chart, Quality.

Analisis Pengendalian Kualitas Produksi Beras dengan Menggunakan Metode SQC (Statistic Quality Control) di Kilang Padi Pukun Kuala Tanjung

The Pukun Kuala Tanjung rice factory is a rice factory located in Pematang Tobat village, Sei Suka subdistrict, Batubara district. The Pukun Kuala Tanjung rice factory has been operating for fifteen years in milling rice into rice. In operating the Pukun Kuala Tanjung rice factory, it can produce 25 tons of rice/week. In carrying out the rice production process at the Pukun Kuala Tanjung rice mill, we often find that the rice produced does not match the quality of production, including the color of the rice being less than good (yellow), rice that is not peeled and crushed, which can lead to a decrease in the quality for sale and even unsalability. rice on the market so that the Pukun Kuala Tanjung rice factory experiences losses in producing rice. The aim of this research is as follows: to know how to control the quality of rice using the SQC (Statistical Quality Control) method, to find out the quality of rice produced by the Pukun Kuala Tanjung rice factory is still in good condition. controlled or not. This research uses descriptive quantitative research both when collecting and processing data where the data collected is data on defective rice products and data processing will be carried out using the SQC (Statistica Quality Control) method. From the data processing that has been carried out in this research, the author draws the following conclusions: Rice quality control using the SQC (Statistical Quality Control) method can be seen as follows: Based on the control chart, it is known that defective products are not within the upper control limits (Upper Control Limit) and Lower Control Limit which can mean that a defective product requires repairs to be made to the defective product.

A Recently Formulated Individual Control Chart Designed for Quality Control Applications within the Health-care Domain

Quality control charts are based on data that is also used for various statistical analyses, such as regression, time series analysis, and other types of analyses. This implies that the data used to create these charts may contain outliers, potentially compromising their accuracy. To eliminate any potential noise or pollution from the data, a researcher proposed using wavelet shrinkage with a threshold and comparing the resulting data with Shewart's individual chart. Based on this, a new quality control chart was proposed and built using a universal approach for assessing the level of thresholding, representing the individual chart for wavelet haar with soft and hard thresholding. Following that, a comparison was performed between them as well as with Shewart's individual chart. This letter's most significant conclusion is that, in addition to using two real data points—triglyceride, which was recorded by a laboratory blood analyzer in Al-Jumhuri and at the specialized Center for Cardiology Hospital in Erbil Governorate—it may be possible to use wavelet shrinkage with a threshold to address issues of noise or pollution when Shewart's individual chart was made and used. Moreover, the wavelet method effectively reduces deviation, as demonstrated in both sets of data.

Performance analysis of agricultural machinery with aid digital agriculture

Digital agriculture has increasingly gained strength in the countryside, as it is considered a byproduct of growing globalization. As the search for production optimization, cost reduction, and productivity increase continues, these tools become the great allies of the producer in the final agricultural production process.Therefore, agricultural operations must also follow this evolution in the development of technological improvements, which carry positive aspects for large-scale production. In this context, the objective of this work was to evaluate the performance of seeders-fertilizers with different input distribution mechanisms. Since one of the biggest expenses in the production process is the acquisition of seeds and fertilizer, any savings in production costs become a significant advantage for the producer, who will have a greater net income. The experimental design used to evaluate the data followed the premises of Statistical Process Control (SPC), and the results were assessed using individual value control charts. It was concluded that the production process was negatively affected by the performance of the seeders-fertilizers, as in one area, more seeds were falling than necessary, increasing the cost per hectare and not adhering to the principles of cost minimization and profit maximization. Conversely, in the second area evaluated, the machinery's performance resulted in fewer seeds being deposited than planned. Consequently, according to the results, the producer lost the opportunity to produce 5.85 sc/ha without reducing costs in the operation, as the seeder simply did not deposit the seeds, thereby reducing the gain per area, which could have been more productive, increasing final profitability.

Analysis of Carton Packaging Quality Control Using Statistical Quality Control Methods at PT XYZ

This research focuses on analyzing the quality control of cardboard box packaging at PT XYZ using the Seven Tools method. The main problem faced by the company is the high level of defects in the cardboard box products produced, which has a negative impact on production efficiency and customer satisfaction. The aim of this research is to analyze the main causes of defects in cardboard box products and provide suggestions for improvements that can be implemented by companies. The method used in this research is Statistical Quality Control (SQC) which involves the use of Seven Tools, namely check sheets, Pareto charts, fishbone diagrams and control charts. Using these tools, data regarding defects is collected, analyzed, and interpreted to identify the main causal factors of quality problems. The analysis results show that certain factors, such as the quality of raw materials, production processes, and operator skills, have a significant contribution to the occurrence of defects. Based on these findings, this research proposes several improvements that can be implemented by PT XYZ to improve the quality of their cardboard box products. Proposed improvements include increasing control over the quality of raw materials, skills training for operators, and implementing stricter standard operational procedures in the production process. By implementing this proposed improvement, it is hoped that PT XYZ can reduce the level of defects in their products, increase production efficiency, and increase customer satisfaction.