Statistical Control Charts Research Articles

Monitoring of high-yield and periodical processes in health care.

Statistical control charts have found valuable applications in health care, having been largely adopted from operations research in manufacturing. However, the most common types are not best-suited to monitor high-yield processes (outcomes comprising true/false fractions, 'near-zero') and periodical processes (characterized by sequences of single populations of finite sizes), but rather to monitor variable vital signs levels and, to a lesser degree, service performance indicators. We discuss control charts that are most suitable for fraction non-conforming measurements. We focus particularly on high-yield and periodical processes, i.e. range in which out-of-control conditions are expected and should be identified. For these conditions, we discuss control charts based on the family of hypergeometric distributions, explaining and comparing their application to more traditional alternatives with two health care case studies. We demonstrate that hypergeometric-type control charts provide higher sensitivity in timely identification of changing rare event fractions and are well-suited for monitoring of periodical processes, while remaining more resistant to false alarms, versus their alternatives.

A new approach for monitoring healthcare performance using generalized additive profiles

ABSTRACT Recent evidence suggests ever-increasing applications of statistical process control (SPC) in health data analysis. However, the diversity in numbers and types of included variables warrant new statistical control charts. This inclusion can be improved by profiles that monitor a describing functional relationship of the process. In this article, we proposed multiple generalized additive models (GAMs) for profile construction. GAMs permit complex fitting models with simultaneous inclusions of parametric and nonparametric terms. Therefore, GAMs can be applied in health data monitoring with a wide range of explanatory variables. We used two statistics to build control charts: (1) a commonly used univariate statistic in nonparametric profiles; (2) a new proposed multivariate statistic which enables the chart to track the role of each included element in the process changes. The statistics are compared according to their performance in monitoring monthly stroke types, including ischaemic and haemorrhagic strokes of patients with acute stroke in the Mashhad Stroke Incidence Study. Features of the proposed profile are discussed and suggestions are made about the utilized statistics in process monitoring. The results show the successful performance of GAMs in profile monitoring.

A Quality Initiative for Reducing Postoperative Hypothermia for Neonatal Intensive Care Unit Surgical Patients.

Introduction:The association between hypothermia in the neonatal intensive care unit (NICU) patients and morbidity and mortality is well described. Neonates are at higher risk of perioperative hypothermia when compared to older children. Previous studies showed that quality improvement tools reduced postoperative hypothermia in NICU patients, but none showed sustained improvement at incidence rates of <10%. As a single institution, we aimed to reduce the percentage of postoperative temperatures < 36°C in NICU patients from 10% to 6% over 6 months and sustain for 6 months.Methods:An interdisciplinary team created a key driver diagram and implemented interventions, including monthly reporting of postoperative hypothermia incidence to the anesthesiologists, individual feedback sessions with the anesthesiologists, use of a perioperative checklist, and continuous axillary temperature monitoring of the infant throughout the perioperative period. Data were collected retrospectively using a chart review of electronic medical records. The primary outcome was the percentage of hypothermic patients (T < 36°C) based on the first postoperative temperature taken in the NICU. We tracked this measure using a statistical control chart and evaluated it using Plan-Do-Study-Act cycles.Results:From February 1, 2016 to May 30, 2018, data were collected for 554 patients (pre-intervention: 242 and post-intervention: 312). The percentage of surgical patients who returned to the NICU hypothermic decreased from 9.7% to 2.5% (P < 0.002)—a change sustained for greater than 12 months.Conclusions:Quality improvement tools are useful in reducing postoperative hypothermia in NICU surgical patients and in maintaining these results.

Monitoring of drippers during wastewater application through statistical quality control

The use of alternative water sources for irrigation such as wastewaters, promotes innumerous benefits, but investigations must be conducted to minimize the negative effects of this technique. Clogging drippers are of the limitations. This study aimed to monitor the clogging of three models of labyrinth-type drippers subjected to irrigation with wastewater from treated domestic sewage, through statistical quality control using Shewhart charts. The drippers tested were as following: Dripper Streamline 16080 model (Netafim®); Taldrip model (Naadanjain®); and Dripper Tiran 16010 model (Netafim®). The system was installed with five lateral lines per model of dripper on a bench at the field in the Brazilian semi-arid region. The system was evaluated every 36 h of operation at eight collection points in each lateral line, totaling thirty-three evaluations at the end of the experiment, which corresponded to a total of 1188 h of operation. Dripper clogging was identified by the statistical control charts with 432, 540 and 360 h for the drippers Streamline 16080 model, Taldrip model and Tiran 16010 model, respectively, indicating the moment to apply a cleaning process. The monitoring through statistical quality control allowed simultaneously identifying the variability of the process and the reduction in flow rates, identifying the moment of clogging of the system and to carry out actions of unclog.

Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods.

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

Analysis of Production System Management of Ghana’s Food and Beverage Industry: Empirical evidence from Spare Parts Inventory Control, Production Quality and Maintenance Modeling

The production systems of various industries have increasingly become a strategic business management function that drives differentiation and competitive edge. Production plant maintenance is therefore an integral part of an entire production system management function, which should encapsulate spare part inventory control systems, total quality assurance, improvement measures and cost control measures. This project sought to understand the production process of firms in Ghana’s food and beverage industry, assess the spare part inventory management practices, production quality control practices and maintenance management strategies implemented in order to recommend a maintenance optimization modeling system towards ensuring an optimal operation cost and production quality. Using both the inductive and deductive research approaches, the research project was exploratory and explanatory in nature. Three major players in Ghana’s food and beverage industry: GIHOC-DCL (Ghana Industrial Holding Corporation Distillery Limited), Heathy-Life Ghana Limited (Healthy-Life) and Parlays Ghana Limited (Parlays) were purposively sampled as industrial case studies to generate data from documentary secondary data and survey based sources. The functionality tools available in MATLAB/Simulink software assisted in formulating the maintenance optimization problem, modeling, analyzing and deriving solutions. The study found that the food and beverage processing business in Ghana has a positive correlation with the main agricultural produce of the country and involves a series of activities including processing and preservation of meat, fish, fruit, vegetables, starch products, bread and sugar. Spare part inventory management and quality control tools mainly applied include Statistical Control Charts and Check Sheet rather than optimization models and software.

Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

Metro construction is normally carried out in complex engineering geological environment, so it can generate various risk events. In the process of metro construction, a scientific risk dynamic analysis is indispensable to reduce and control risks. In order to analyze the risk in metro construction more scientifically and reasonably, in this study, a new risk dynamic analysis method for metro construction is proposed using statistical process control. The method can analyse the risk level according to the process’s capacity index and identify the characteristics of risk variation according to the statistical control chart. The risk level and the characteristic of risks may vary with dynamical updating of monitoring data, so the conclusion of risk evaluation for a time interval can be drawn and corresponding safety measures can be ascertained. The method ushers statistical process control, so the random factors in risk evolution can be considered fully. Then, the method is applied to the risk analysis of shield construction under the Beijing-Tianjin intercity railway in Beijing Metro Line 8, a typical risk problem in the traffic construction. The variation of the risk level and the characteristic of risks can be evaluated reasonably because the dynamical randomness is considered. Moreover, whether risk control measures should be taken and what the effective measures are can be ascertained explicitly.

The application of quality control charts for identifying changes in time-series home energy data

Energy consumption in the home is heavily influenced by the occupants and the routines they adopt. Although these routines tend to be regarded as somewhat static in nature, more recent evidence from the social sciences suggests that patterns of consumption are actually more fluid and constantly evolve to accommodate the contingencies of everyday living. This makes detecting changes in patterns of activity and their impact on energy consumption difficult, particularly when these patterns are often invisible to the householder to begin with. Being able to identify when a change occurs, therefore, could be a powerful tool to establish the context of change and so to determine more appropriate corrective action to curb waste and create opportunities for greater flexibility in consumption. The growing adoption of smart meters and home energy monitoring provide a platform for numerical approaches, yet there is little work reported in the literature and none that have attempted to evaluate effectiveness of such methods applied to detect changes in behaviour using field monitoring data from family homes. This paper reports on the application of a Change Point Detection method based on statistical quality control charts applied to identify changes in activities a family home using typical monitoring data. The approach was found to be very effective, identifying 78% of the changes that occurred over a two-year period and hence the outlook for such methods is promising. The findings suggest that such techniques could significantly improve the quality of information provided in energy feedback and so could play a significant role in the pursuit of more efficient energy use in the home by adding value to monitoring systems and services.

Am I out of control? The application of statistical process control charts to children's surgery

AimsTo illustrate the construction of statistical control charts and show their potential application to analysis of outcomes in children's surgery. Patients and methodsTwo datasets recording outcomes following esophageal atresia repair and intestinal resection for Crohn's disease maintained by the author were used to construct four types of charts. The effects of altering the target signal, the alarm signal and the limits are illustrated. The dilemmas in choice of target rate are described. Simulated data illustrate the advantages over hypothesis testing. ResultsThe charts show the author’s institutional leak rate for esophageal atresia repair may be within acceptable limits, but that this is dependent on the target set. The desirable target is contentious. The leak rate for anastomoses following intestinal resection for Crohn's disease leak is also within acceptable limits when compared to published experience, but may be deteriorating. The charts are able to detect deteriorating levels of performance well before hypothesis testing would suggest a systematic problem with outcomes. ConclusionsStatistical process control charts can provide surgeons with early warning of systematic poor performance. They are robust to volume–outcome influences, since the outcome is tested sequentially after each procedure or patient. They have application in a specialty with low frequencies of operations such as children's surgery. Type of studyDiagnostic test. Level of evidenceLevel II.

Data-driven fault detection methods for detecting small-magnitude faults in anaerobic digestion process.

Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.

28 QUALITY OF CARE INITIATIVE IMPROVES OUTCOMES FOR PATIENTS WITH INFLAMMATORY BOWEL DISEASE

Abstract Introduction There is significant variation in processes and outcomes of care for patients with inflammatory bowel disease (IBD), suggesting opportunities to improve quality of care. Recent efforts to define quality measures for IBD have identified emergency room (ER) visits, hospitalizations, corticosteroid use, and opioid use as indicators of care quality. We hypothesized that IBD care could be improved through a structured quality improvement (QI) program. Methods We utilized the Breakthrough Series Collaborative approach developed by the Institute for Healthcare Improvement to improve care for adults with IBD. We identified primary and secondary drivers of urgent care need for patients including those at high risk for ER use, and a multi-stakeholder panel developed 19 practice change ideas that could influence those drivers. Between January 2018 and May 2019, clinical sites participating in a QI collaborative across the United States tested and implemented various change ideas, shared ongoing results during coached monthly webinars, and participated in 3 in-person meetings to learn QI methods and share best practices. Patient-reported outcomes (PROs) were collected at clinical visits, including recent ER use and hospitalizations, use of steroids and narcotics, and measures of care utilization. Providers rated whether patients were at high risk for urgent care needs. Site performance on key measures were monitored using statistical control charts, with assessment for common cause (due to chance) variation and special cause (non-random) variation. Results We collected data prospectively from 20,382 discrete visits at twenty-six participating clinical practices (14 academic/university, 12 private/community). Disease type included Crohn’s disease (58%), ulcerative colitis (39%), and other (3%); 54% were female. During the 15-month project period, improvement with special cause variation was noted across multiple measures. Collaborative-wide decreases were seen in ER utilization (18% to 14%, relative reduction of 22%; Figure), hospitalization (14% to 11%, relative reduction of 21%), steroid use (14% to 10%, relative reduction of 29%), and narcotic utilization (8% to 4%, relative reduction of 50%). Successful change ideas tested by sites included proactive maintenance of a “high risk” patient list, reserved outpatient visits for urgent needs, “morning-after” contact with patients who went to the ER, patient education about how and when to get help, and proactively scheduling earlier follow-up for high risk patients. Conclusions Outcomes of IBD care were improved using a structured QI program that facilitates small changes in practice structure, sharing of best practices across sites, and ongoing feedback. Spread of successful change ideas may facilitate broad improvement in IBD care and significant cost savings when applied to a large population. Changes in Key Measures Over Time Statistical Process Control Chart Showing Monthly Proportion of Patients Reporting Recent ER Utilization

Identification of exchange rate shocks with compositionaldata and written press

The evolution of exchange rates results from events that affect different countries differently, that is, asymmetric shocks. Being value ratios, exchange rates constitute what is known as compositional data. The compositional data analysis methodology transforms exchange rates in a way that ensures the validity of their subsequent statistical analysis. In this paper we submit the daily exchange rates of the US dollar, the yen, the pound sterling, the euro, the Brazilian real, and the yuan to transformation by centred log-ratios. We then use the residuals from a Box-Jenkins analysis to estimate shocks and represent them in statistical control charts for a simple visual identification of both significant revaluations and devaluations, and periods of greatest volatility, which we then relate to news stories in the written press.

Sensor-Based Recurrence Analysis of Energy Efficiency in Machining Processes

Manufacturing industry incurs a large portion of energy consumption and carbon emission in the economy. Traditionally, smart energy management depends on aggregated measures from billing information, as well as physics-based models, empirical results derived from extensive experiments, which tend to be limited in the ability for real-time monitoring of energy efficiency. There is an urgent need to develop energy monitoring solutions for more transparency about energy use. This paper presents a new sensor-based approach for recurrence analysis of continuous power signals and multi-state modeling of energy efficiency in the machining process. First, we leverage the recurrence plot to characterize the nonlinear variations in power signals and further help delineate different states in the machining process, thereby providing statistics of energy consumption in each state. Second, we compute the composite index of energy efficiency for each workpiece and then develop multivariate statistical control charts for process monitoring of continuous production of workpieces. Third, after an anomaly is detected, we propose the orthogonal decomposition approach to diagnose the root cause of abnormal states in the energy use. The proposed methodology is evaluated and validated on real-world manufacturing of shaft-like parts in a machine shop. Experimental results show that the prediction error of energy efficiency from sensor-based models is within 5% from the ground truth, which show great potentials to implement sensor-based monitoring and analysis of real-time energy efficiency in the manufacturing process.

Diseño y desarrollo de un prototipo Checking – Fixture para componentes de la industria automotriz con un enfoque hacia la industria 4.0

The present work shows the design and the integration of several complements in a device at prototype level that carries out the tracking of the physical and dimensional characteristics for components of the automotive industry based on the new technological trends, as it is the industry 4.0. For this purpose, CAD design software such as Solidworks 2017 is used, the use of CAM systems with EdgeCam 2019 software as a manufacturing element, the vision system is used using the Matlab software and the Minitab 2017 statistical tool as an interface a device that helps to know the behavior of the parameters that are defined as critical in the production of automotive components and be able to carry out the necessary adjustments in the production parameters to reduce the variability in the process. Looking to have information in real time and make timely decisions. Generating the corresponding statistical control charts, with the aim of avoiding possible reworking or in the case of scrap generation.

Project monitoring by dynamic statistical control charts

Project monitoring activities are fundamental to assure a timely identification of unacceptable deviations from the baseline, so that corrective actions may be taken to bring the project back in line with its objectives. In regard to this, the most used approach is the Earned Value Management (EVM) technique whose traditional metrics do not allow to discriminate between acceptable and unacceptable performance variations. With this recognition, a statistical approach based on the use of dynamic Shewhart’s and CUmulative SUM (CUSUM) control charts is proposed in the present paper to deal with the project monitoring problem. Its efficaciousness is demonstrated by its application to a set of real projects.

Access and effectiveness inpatient care indicators and economic crisis: analysis based on the Brazilian Unified Health System data, Brazil and Southern Brazil states, 2009-2018.

In the context of crisis and resource constraints, it is reasonable to assume the deteriorated weaknesses of the Unified Health System (SUS), such as regional inequalities, underfinancing, and care quality issues. This study explored the application of easily comprehensible and calculated access and effectiveness indicators that could reflect the hospital network crisis. Five indicators extracted from the Hospital Information System, related to Brazil and states of the Southeastern region, were analyzed in the 2009-2018 period: hospitalizations resulting in death; surgical hospitalizations resulting in death; elective surgeries in the total of surgical hospitalizations; hip prostheses in the senior population; and angioplasties in the population aged 20 years and over. Statistical control charts were used to compare indicators between states, before and from 2014. In Brazil, overall hospital deaths had a slight increase while surgical deaths declined; elective surgeries and hipprosthesis also decreased. In Southeastern Brazil, Rio de Janeiro was the worst performer, especially the decrease of the elective surgeries. The results illustrate the potential of indicators to monitor crisis effects on hospital care.

Large-scale empirical optimisation of statistical control charts to detect clinically relevant increases in surgical site infection rates

ObjectiveSurgical site infections (SSIs) are common costly hospital-acquired conditions. While statistical process control (SPC) use in healthcare has increased, limited rigorous empirical research compares and optimises these methods for SSI...

Carta de controle Cusum na análise de um processo de fabricação de café

Esta pesquisa trata de uma aplicação empírica com o uso da carta de controle estatístico de processo de somas acumulados, para avaliar a estabilidade de um processo produtivo de fabricação de café solúvel. Essa carta de controle sob a forma da V máscara é apropriada para processos produtivos autocorrelacionados. A amostra coletada no processo produtivo investigado corresponde a um total de 4.650 observações e a característica pesquisada corresponde ao peso das latas de 50g e 100g de café em pó instantâneo. A coleta dos dados foi realizada de maneira automatizada durante o período de 6 meses consecutivos. Os resíduos foram obtidos a partir das estimativas dos modelos autoregressivos de terceira ordem AR (3) e foram analisados com o uso da carta de controle de somas acumuladas onde evidenciou um processo de produtivo considerado instável estatisticamente, corroborando assim com a fragilidade desse processo no que tange à garantia de produtos não conformes, principalmente no que se refere ao peso dos vasilhames de 50g. Os resultados auferidos levam a sugerir uma maior atenção por parte dos funcionários da indústria de café em pó, dispensando assim uma maior atenção, com disciplina e controle nas atividades realizadas cotidianamente, além de dizimar relevantes erros cometidos pelos funcionários na rotina de trabalho e que podem ser solucionados com uma boa orientação, além da mudança de comportamento.&#x0D; Palavras Chave: Carta de Controle. Somas Acumuladas. Produção de Café. Controle Estatístico de Processos.

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

The Impact of an Interventional Pulmonary Program on Nontherapeutic Lung Resections.

Pulmonary resection can concurrently diagnose and treat known or suspected lung cancer, but is not without risk. Benign resection rates range widely (9% to 40%). We evaluated the impact of an Interventional Pulmonology (IP) program and dedicated Pulmonary Nodule Clinic on surgical benign resection rates at a single institution. An IP program was initiated in August 2010 that offered advanced diagnostic techniques and a dedicated Pulmonary Nodule Clinic was opened in August 2013. We retrospectively reviewed all patients who underwent resection for known or suspected lung cancer between 2005 and 2015 at our tertiary referral hospital. Demographics, preoperative tissue diagnoses, surgical procedure, final pathology, and staging were collected. Quarterly benign resection rates were calculated and plotted on a statistical quality control chart (P-Chart) to determine the impact of the IP program and Pulmonary Nodule Clinic on benign resection rates over time. Of 1112 resections, 209 (19%) were benign. Variation in quarterly benign resection rates decreased after introduction of the IP program in 2010, and a significant (P<0.05) sustained decrease in the quarterly benign resection rate occurred after introduction of the pulmonary nodule clinic in 2013 to a new baseline of 12% compared with 24% before 2010. After introduction of the IP program, mean quarterly preoperative tissue diagnostic rates increased from 45% to 58% (P<0.01). Integration of an IP program employing advanced diagnostic bronchoscopic techniques has improved preoperative diagnostic rates of suspicious pulmonary nodules and in combination with a pulmonary nodule clinic has resulted in fewer benign resections.