Nelson's Rules Research Articles

Implementing a Phase II Quality Control Protocol for a High Precision 137 Cs Dosimetry Calibration Irradiator.

In medical physics, rigorous quality assurance and quality control protocols are vital for precise dose delivery applications. In many health physics applications, the allowable uncertainty for various processes is often greater than that of medical physics due to looser safety ties. This results in less demand for quality control and uncertainty analyses, since these may not be needed. However, certain applications can benefit from a comprehensive quality control program, as it may yield important insights, such as air kerma monitoring in dosimetry calibrations for environmental and low-dose applications. By implementing a thorough quality control program tailored to specific contexts and needs, uncertainties associated with dose measurements can be quantified with greater accuracy and reliability. This proactive approach not only ensures the integrity of data collected but also enhances understanding of the measured doses. For these reasons, a comprehensive quality control initiative was implemented following documented failures in a 137 Cs dosimetry calibration irradiator. This involved systematic charge collection using NIST-traceable ion chambers to observe long-term changes. A Phase I quality control protocol was previously implemented, which employed Shewhart control charts and Nelson's rules to analyze various datasets subgrouped under different conditions. This study addresses the development of a Phase II protocol, which focuses more on uncertainty quantification of systematic errors and irradiator changes, and air kerma precision for dosimetry calibrations. A designed experiment was performed to identify how much systematic errors influence the air kerma. Emphasis was placed on stricter quality assurance protocols, continuous data collection, and additional control charts to monitor short-term changes, such as exponentially weighted moving average control charts. A pre-irradiation control process was implemented to verify that the total air kerma met the measurement quality objective and to show how various uncertainties were applied before calibration. This study indicates how uncertainty is applied given observed air kerma measurements from the irradiator. Ongoing efforts aim to streamline the quality control procedure, ensure consistent data collection, and assess its impact on dosimetry applications.

CPV Monitoring - Optimization of Control Chart Design by Reducing the False Alarm Rate and Nuisance Signal

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control (SPC) tools. In SPC, the control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

Implementation of a health system intervention to reduce time from presentation to surgical intervention for pediatric testicular torsion

Testicular salvage rates for torsion are time-dependent1. Door to detorsion time has been identified as an independent testicular survival factor2. We describe an initiative to reduce door to incision (DTI) time for pediatric testicular torsion. An institutional multidisciplinary quality improvement initiative with a primary outcome of reducing DTI time for pediatric testicular torsion was developed with multidisciplinary stakeholders. Several process and balancing measures were used as secondary outcomes to help interpret and verify the observed change in DTI time. Interventions were implemented in cycles. Initial interventions standardized assessment of suspected torsion by Emergency Medicine utilizing a validated scoring system. A threshold Testicular Workup for Ischemia and Suspected Torsion (TWIST) score led to parallel notification of essential services for rapid assessment and case prioritization3. Subsequently, bedside ultrasound in the Emergency Department was implemented. Progress was tracked in a live dashboard and analyzed with X-mR process control charts and Nelson rules. These tools are used in quality improvement and process control to demonstrate the significance of changes as they are being implemented, prior to when traditional hypothesis testing would be able to do so. We aimed to increase the proportion of cases with DTI times under 4h from 64% to >90% within one year. We observed 22 torsion cases prior to and 62 following initial implementation. The percentage of cases with DTI times under 4h improved from 64% to 95%. At week 29, a shift identified a significant change on the X chart, with reduction in mean DTI time from 221 to 147min. At the same time, a shift on the mR chart identified reduction in patient-to-patient variation. Mean time from arrival to Urology evaluation decreased from 140 to 56min, mean time from arrival to scrotal ultrasound decreased from 70 to 36min, and mean time from scrotal ultrasound to surgical incision decreased from 128 to 80min. These improvements highlight the two key successes of our project: application of the TWIST score and bedside ultrasound for rapid assessment of suspected testicular torsions, and parallel processing of the evaluation and management. Implementation of a protocol for pediatric testicular torsion increased the proportion of cases with DTI time <4h to 95%, decreased mean DTI time, and decreased variation. Our protocol provides a model to improve timeliness of care in treating pediatric testicular torsion.

It Pays to be Accurate: Improving Critical Care Documentation in a Pediatric Emergency Department.

Clinician documentation is highly variable, and awareness of documentation requirements remains low despite post-training experience. At our hospital, critical care (CC) documentation was inconsistent. Our aim was to increase appropriate CC attestations from 51% to 90% for status asthmaticus, anaphylaxis, and diabetic ketoacidosis in the pediatric emergency department by December 2021. A physician team developed a key driver diagram. Retrospective baseline data using International Classification of Diseases, Ninth and Tenth Revision codes were obtained from January 2018 to September 2020, after which data were followed prospectively in consecutive groups of 20 encounters. Statistical process control charts were used to analyze data. Nelson rules were used to detect special cause variation. Primary outcome was the inclusion of appropriate CC attestations. Interventions included education, CC attestation templates, and provider feedback. We also tracked charges for the 3 diagnoses studied. Process measures included template use. Balancing measure was refusal of payment by insurers. P-charts were used to analyze primary outcome and process measures. X-bar charts were used to analyze charges. Baseline data represented 706 encounters with 51% including CC documentation. Following clinician education and release of the CC template, special cause variation was detected, and centerline shifted to 88.1% (Fig 2). Average charges per encounter increased from $4527 to $5385. There was no reported refusal of payment. We successfully achieved improvements in CC documentation in the 3 diagnoses of interest through education and process changes in documentation, leading over $1 million in new charges over the past 15 months.

Condition Monitoring of Internal Combustion Engines in Thermal Power Plants Based on Control Charts and Adapted Nelson Rules

In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on the supervisory system data. The statistical model is validated through comparisons with entries of the plant logbook. It is demonstrated that the results obtained with the proposed statistical model match perfectly with the entries of the logbook, showing our model to be a promising tool for making decisions concerning maintenance in the plant.

Intelligent Fault Detection, Diagnosis and Health Evaluation for Industrial Robots

The focus of this study is development of an intelligent fault detection, diagnosis and health evaluation system for real industrial robots. The system uses principal component analysis based statistical process control with Nelson rules for online fault detection. Several suitable Nelson rules are chosen for sensitive detection. When a variation is detected, the system performs a diagnostic operation to acquire features of the time domain and the frequency domain from the motor encoder, motor current sensor and external accelerometer for fault diagnosis with a multi-class support vector machine. Additionally, a fuzzy logic based robot health index generator is proposed for evaluating the health of the robot, and the generator is an original design to reflect the health status of the robot. Finally, several real aging-related faults are implemented on a six-axis industrial robot, DRV90L7A6213N by Delta Electronics, and the proposed system is validated effectively by the experimental results.

Analysis of Long-Term Quality Control Data for a 137Cs Dosimetry Calibration Source.

Strict quality assurance programs are required for many radiological applications, but these seldom exist for verifying dosimetry calibration sources. After initial characterization of a dosimetry calibration facility, quality control procedures are recommended to ensure the early detection of any changes or malfunctions. These also result in refined knowledge about average dose rate and experimental variations in dose delivery. This paper describes the implementation of a phase I quality control protocol for a 137Cs dosimetry calibration source and includes an analysis of the resulting data collected over a 24-mo period. During this time, substantial data was collected to establish trial control limits. Air kerma rate measurements were obtained using an ion chamber and were adjusted for decay, corrected for ambient temperature, pressure and humidity, and then analyzed using quality control charts. Three variations of rational subgrouping methods were used in order to find assignable causes of error, and Nelson's Rules were followed to detect any non-random statistical variations. Measurements were subgrouped according to same-day measurements in order to detect positional errors as well as atmospheric correction errors. Additionally, measurements were subgrouped according to analogous experimental setups in order to detect failure in equipment or incorrect settings. Both were analyzed using the X-bar and R chart method. Similarly, individuals and moving ranges charts were used to carefully examine each position in order to observe any situational errors that may occur which include timing, positional, or interference errors. Each method was successful in identifying unique out-of-control data points that occurred during the phase I application of forming control limits. Over the 24-mo period, enough data points were deemed in-control to establish reliable trial limits. Future experiments will include the phase II application of gaining more reliable measurements in order to fine-tune the limits, as well as performing a designed experiment, where variables are purposefully changed in order to test the variation of the data.

Statistical Quality and Process Control in Biopharmaceutical Manufacturing-Practical Issues and Remedies.

Statistical quality and process controls (SQC and SPC) are used for monitoring, trending, and ultimately improving biopharmaceutical manufacturing processes and operations. The purpose of this paper is to highlight characteristic features of bioprocess data and their impact on typical SQC and SPC applications, specifically control charts for individual observations (I-chart) and estimation of process performance index (Ppk). Simulated data were used in an attempt to mimic bioprocess data by inducing inhomogeneity, nonstationarity, autocorrelation, and outliers. The first specific part highlights the roles of within and overall standard deviation (SD) estimates for 3σ limits and their impacts on frequently applied sensitizing rules for control charts, i.e. Nelson's rules 1-4. The second part deals with the often-asked question of how many observations are required for estimation of robust 3σ limits. In the third part, five popular approaches for treating censored data (results below or equal to limit of quantification, ≤LOQ) were compared and their impact on 3σ limits and Ppk estimates were assessed. The final section summarizes the typical issues faced by the practitioner in the application of SQC and SPC and provides remedies for setting up robust and efficient control charts for biopharmaceutical process monitoring. Overall, this study shows that process monitoring and subsequent assessment without taking into consideration this atypical nature of biopharmaceutical process can lead to increased false alarm rates, thus impacting the batch release or even possibility of rejecting good batches.

Development of a Graphic Information System Applied to Quality Statistic Control in Production Processes

One of the advantages that organizations have when using an Information System is the control of their activities. This article develops an Information System that will allow an organization to graphically obtain the real results of a production process by applying Nelson's eight rules to determine if any measured variable is out of control. The software architecture pattern used is the Model View Controller (MVC) to keep the functionality of the application separate. The front-end, that is, the part that interacts with the users, was developed in ASP.NET as a web platform to provide the required services, JavaScript, HTML 5, Razor and Bootstrap. The back-end, which is the part that processes the entry of the front-end and performs the calculations, operations, communication with the database and reading of files, was developed with the C sharp programming language, the SQL Server database management system and the entity framework. As a result, the system sends an e-mail as an alarm with an explanation of what has happened when it detects that some measured variable is out of control by applying Nelson's rules. This allows the organization to make effective decisions in the processes involved.

Clinical Decision Support to Improve Dosing Weight Use in Infants with Neonatal Abstinence Syndrome.

Opioid abuse in the United States is a public health emergency. From 2000 to 2009, prenatal maternal opiate use increased from 1.19 to 5.63 per 1,000 births, with up to 80% of in utero opioid-exposed infants requiring pharmacotherapy. This study aimed to increase the percentage of neonatal abstinence syndrome (NAS) medication orders based on birth weight (BW) in neonates admitted to a neonatal intensive care unit with a principal diagnosis of NAS from 29% to 90%, within 4 months of project initiation, and to sustain this for 6 months. This project occurred at an academic medical center with 5,000 deliveries per year and a 49-bed Level III neonatal intensive care unit. We used the Institute for Healthcare Improvement methodology, largely focusing interventions on clinical decision support (CDS) tools. We plotted all measures on Shewhart charts, and Nelson rules differentiated special versus common cause variation. The percent of orders based on BW increased from 29% to 78% after implementing multiple interventions focused primarily on CDS. However, this later decreased to 48% as workarounds began. There was also a significant decrease in the length of stay variability, which persisted throughout the project. CDS is a helpful tool to guide prescribing behavior; however, workarounds can negate its usefulness. Standardized use of BW for weight-based NAS medication prescribing can decrease the length of stay variability. Further studies are needed using a human factors approach to minimize workarounds in CDS and potentially decrease the length of stay in neonates with NAS.

Modified short-run statistical process control for test and measurement process

The key characteristics of test and measurement (T&M) manufacturing are short-run, multi-product families and testing at multi-stations. These characteristics render statistical process control (SPC) inefficacious because inherently meagre data do not warrant meaningful control limits. Measurement errors increase the risks of false acceptance and rejection, thereby leading to such consequences as unnecessary process adjustments and loss of confidence in SPC. This study presents a modified SPC model that incorporates measurement uncertainty from guard bands into the $$ \overline{\mathrm{Z}} $$ and W charts, thereby addressing the implications of short runs, multi-stations and measurement errors on SPC. The implementation of this model involves two phases. Phase I retrospective analysis computes the input parameters, such as the standard deviation of the measurement uncertainty, measurement target and estimate of the population standard deviation. Thereafter, five-band setting and sensitivity factor are proposed to estimate process standard deviation to maximise the opportunity to detect the assignable causes with low false-reject rate. Lastly, the $$ \overline{\mathrm{Z}} $$ and W charts are generated in Phase II using standardised observation technique that considers the measurement target and estimated process standard deviations. Run tests based on Nelson rules interpret the charts. Validation was performed in three case studies in an actual industry.

A Quality Improvement Intervention to Improve Inpatient Pediatric Asthma Controller Accuracy.

Our objective was to investigate if a rigorous quality improvement (QI) intervention could increase accuracy of pediatric asthma controller medications on discharge from an inpatient hospitalization. Our interprofessional QI team developed interventions such as improving documentation and creating standardized language to ensure patients were discharged on an appropriate asthma controller medication and improve assessment of asthma symptom control. Each week of 2015-2016, the first 5 patients discharged with status asthmaticus from the pediatric wards were reviewed for documentation of the 6 asthma control questions and accuracy of the discharge controller therapy. Correct discharge medication was defined as being prescribed the age-appropriate medication and dose on the basis of baseline controller therapy, compliance with baseline medication, and responses to asthma control assessment. The weekly proportion of control questions that were accessed and correct controller medications that were prescribed were analyzed by using Nelson rules and interrupted time series. A total of 240 preintervention and 252 postintervention charts were reviewed. The primary outcome of the median proportion of patients discharged on appropriate controller therapy improved from 60% in preintervention data to 80% in the postintervention period. The process measure of proportion of asthma control questions that were assessed improved from 43% in the preintervention period to 98% by the final months of the intervention period. Both of these changes were statistically significant as per Nelson's rules and interrupted time series analyses (P = .02 and P < .001, respectively, for postintervention break). An interdisciplinary QI team successfully improved the accuracy of asthma controller therapy on discharge and the inpatient assessment of asthma control questions.

Complex Control Chart Interpretation

Identification of the assignable causes of process variability and the restriction and elimination of their influence are the main goals of statistical process control (SPC). Identification of these causes is associated with so called tests for special causes or runs tests. From the time of the formulation of the first set of such rules (Western Electric rules) several different sets have been created (Nelson rules, Boeing AQS rules, Trietsch rules). This paper deals with the comparison analysis of these sets of rules, their basic statistical properties and the mistakes accompanying their application using SW support. At the end of this paper some recommendations for the correct application of the runs tests are formulated.

USING REVERSE ARRANGEMENT TEST TO DETECT NON-MONOTONIC TRENDS FOR SEMICONDUCTOR MANUFACTURING AND RELIABILITY TESTS

The limitation of the trend test in SPC is reviewed. The current Nelson's run test for the six or seven consecutive increasing or decreasing points cannot detect non-monotonic increasing or decreasing trend that is frequently encountered in industries. This paper proposes the reverse arrangement test (RAT) to replace the current SPC trend test rule in order to detect both non-monotonic and monotonic increasing or decreasing trends. The theory of RAT is reviewed and we point out the errors in the tables from a well-known and most frequently referenced paper on RAT. The corrected tables of accumulated probabilities for each total reverse arrangement for N = 3 to 12 are presented. The false alarm rate from RAT is verified with excellent accuracy by 108data points simulating stable normal distributed process. Real examples from the semiconductor manufacturing illustrate that 7 non-monotonic increasing points can be detected by RAT with the same false alarm rate as for the 6 monotonic increasing trend, while sometimes none of the current WECO and Nelson's rules can detect such abnormal pattern. Applications of RAT in IC SPC and WLRC (Wafer Level Reliability Control) show RAT is much more powerful than conventional monotonic trend tests in detecting nonconformance.

A fuzzy reasoning based diagnosis system for X control charts

This paper describes a new diagnosis system, which is based on fuzzy reasoning to monitor the performance of a discrete manufacturing process and to justify the possible causes. The diagnosis system consists chiefly of a knowledge bank and a reasoning mechanism. The knowledge bank provides knowledge of the membership functions of unnatural symptoms that are described by Nelson's rules on X control charts and knowledge of cause-symptom relations. We develop an approach called maximal similarity method (MSM) for knowledge acquisition to construct the fuzzy cause-symptom relation matrix. Through the knowledge bank, the diagnosis system can first determine the degrees of an observation fitting each unnatural symptom. Then, using the fuzzy cause-symptom relation matrix, we can diagnose the causes of process instability. In conclusion we provide a numerical example to illustrate the system.