Enhanced patient-based real-time quality control using the graph-based anomaly detection.

OBJECTIVES: Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. METHODS: The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. RESULTS: The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. CONCLUSIONS: The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.

BACKGROUND: Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. METHODS: Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. RESULTS: 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2 mm (2%/1 mm) criterion. CONCLUSION: The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.

Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches.

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e., s 2 , s , ln( s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA- S 2 , EWMA-ln( S 2 ), and EWMA- X ¯ - S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA- S 2 and EWMA-ln( S 2 ); the performance difference with EWMA- X ¯ - S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions.

Use of Statistical Process Control Methods for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacteria Outbreaks: A Single-Center Pilot Study.

BACKGROUND: Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility-associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks. METHODS: We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery-associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts. RESULTS: The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%. CONCLUSIONS: SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.

Statistical process control for the analysis of quality control in urodynamics: A potential new approach for quality review of urodynamics.

PURPOSE: To analyze quality control in urodynamic studies, using a proportion control chart (p-chart) for statistical process control. MATERIALS AND METHODS: This single-center study was conducted at the Urodynamic Center of West China Hospital, Sichuan University. We randomly selected 15 samples from each month in 2020, and 180 urodynamic traces were finally enrolled. We used the p-chart of statistical process control for analysis. We calculated the proportion of the incidence of a selected set of artefacts in the monthly urodynamic study process, including non-standard zero setting, no cough test, incomplete records of all measurements by urodynamicists, catheter displacement, and baseline drift. Through the specific calculation formula of statistical process control, we obtained the values of the center line, lower control limit, and upper control limit. RESULTS: All data points of each artefact were within zone A. However, one outlier was found in the p-chart of all artefacts in October, which might have been caused by inexperienced operators. CONCLUSIONS: Statistical process control may play an important role in the process control of urodynamic studies and guide us in identifying the cause of poor quality in process management.

Efficient algorithms for real-time syndromic surveillance.

OBJECTIVE: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. METHODS: Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. RESULTS: We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. CONCLUSION: Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability.

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.

Two Features of the GINAR(1) Process and Their Impact on the Run-Length Performance of Geometric Control Charts.

The geometric first-order integer-valued autoregressive process (GINAR(1)) can be particularly useful to model relevant discrete-valued time series, namely in statistical process control. We resort to stochastic ordering to prove that the GINAR(1) process is a discrete-time Markov chain governed by a totally positive order 2 (TP2) transition matrix.Stochastic ordering is also used to compare transition matrices referring to pairs of GINAR(1) processes with different values of the marginal mean. We assess and illustrate the implications of these two stochastic ordering results, namely on the properties of the run length of geometric charts for monitoring GINAR(1) counts.

[Study on the sensitivity of a volumetric modulated arc therapy plan verification equipment on multi-leaf collimator opening and closing errors and its gamma pass rate limit].

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

Learning Curve of Surgeons Performing Laparoscopic Ovarian Tissue Transplantation in Women with Premature Ovarian Insufficiency: A Statistical Process Control Analysis.

STUDY OBJECTIVE: To analyze patient safety in laparoscopic ovarian tissue transplantation surgery by tracking the rate of postoperative complications and the learning curves of the surgeons by statistical process control analysis. DESIGN: A retrospective study. SETTING: A university-affiliated hospital. PATIENTS: A total of 100 patients with premature ovarian insufficiency who underwent ovarian tissue cryopreservation by vitrification and then autologous transplantation of frozen-thawed ovarian tissues with in vitro activation. INTERVENTIONS: Ovarian tissue cryopreservation, in vitro activation, and transplantation. MEASUREMENTS AND MAIN RESULTS: We assessed the surgery complications, differences in total surgery time, transplantation time, and transplantation time per ovarian sheet in operations performed by 3 experienced laparoscopic surgeons. Surgeon A performed 80 operations; surgeon B, 29 operations; and surgeon C, 20 operations. Complications occurred in 1.55% of the procedures. Although all 3 surgeons' performance never fell below the unacceptable failure limit, only surgeon A became competent after 66 cases. CONCLUSION: The laparoscopic ovarian tissue transplantation surgery was generally safe given that the postoperative complications were infrequent (1.55%). Although the performance of all 3 surgeons was acceptable, only surgeon A attained the level of competency after 66 cases. The transplantation method may not be the key factor for reducing surgery time in this surgery. An efficient ovarian tissue transplantation team is more important in reducing the surgery time than the surgeon's surgical technique alone.

The problem with dichotomizing quality improvement measures.

The Anesthesia Quality Institute (AQI) promotes improvements in clinical care outcomes by managing data entered in the National Anesthesia Clinical Outcomes Registry (NACOR). Each case included in NACOR is classified as "performance met" or "performance not met" and expressed as a percentage for a length of time. The clarity associated with this binary classification is associated with limitations on data analysis and presentations that may not be optimal guides to evaluate the quality of care. High compliance benchmarks present another obstacle for evaluating quality. Traditional approaches for interpreting statistical process control (SPC) charts depend on data points above and below a center line, which may not provide adequate characterizations of a QI process with a low failure rate, or few possible data points below the center line. This article demonstrates the limitations associated with the use of binary datasets to evaluate the quality of care at an individual organization with QI measures, describes a method for characterizing binary data with continuous variables and presents a solution to analyze rare QI events using g charts.

Statistical process control to monitor use of a web-based autoplanning tool.

PURPOSE: To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). METHODS: Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. RESULTS: For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, -2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62% ± 0.34%, 0.32% ± 0.23%, and -0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC-TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC-TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS: Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.

A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties.

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters' prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.

Statistical process control charts for better analysis of sequential data in psychology: The case of illusion of control experiments.

Illusion of control (IOC) is a bias in the judgment of personal success with implications to learning theories and health policies; some important questions in the investigation of IOC may be related to traditional measures in the field, namely self-assessment using Likert scales about the sense of control. Statistical process control (SPC) and Shewhart charts are methods developed to monitor and control industrial processes, never applied in psychological studies before. The present two studies investigated the use of the technique of Shewhart charts in the analysis of IOC. The purpose was to explore the use of SPC and Shewhart charts in the analysis of data sequences from psychological experiments; the objective was to analyze the results of reaction time (RT) data sequences plotted in SPC charts, in comparison with self-assessment judgments from an IOC task. Participants were 63 undergraduate students (Study 1) and 103 mine workers (Study 2) instructed to try to control a traffic light on a computer by pressing or not the keyboard. Higher probabilities of the successful outcome generated judgments of illusion and shifts (due to cognitive activity) in the charts of RT; lower probabilities resulted in null illusion and RT presented a random and stable profile. Patterns for different groups emerged in Shewhart charts. SPC can contribute to the analysis of the behavior of sequences of data in psychological studies, so that the charts indicate changes and patterns not detected by traditional ANOVA and other linear models.

Selection of within-run quality control rules for laboratory biomarkers.

In order to release correct biomarker results of a laboratory test, it is a regulatory requirement to apply quality control standards for controlling analytical errors. Releasing an incorrect test result might lead to wrong diagnosis or treatment of a patient in medical decision-making. In laboratory medicine, one of the means to control analytical errors is statistical process control procedures proposed by James O. Westgard and his coworkers nowadays known as "Westgard rules." To judge their performance for discriminating in-control from out-of-control processes, power curves are used. In this article, we describe functions for the power curves of the within-run Westgard rules. Based on these power curves, we use a benchmark approach for selecting a quality control procedure out of the set of Westgard rules. It is shown that two graphical procedures proposed by Westgard and his coworkers can be reduced to this benchmark approach. Besides, a commonly used measure in laboratory medicine for describing out-of-control processes is critically examined revealing the threat of selecting too optimistic quality control rules.

Effective disease surveillance by using covariate information.

Effective surveillance of infectious diseases, cancers, and other deadly diseases is critically important for public health and safety of our society. Incidence data of such diseases are often collected spatially from different clinics and hospitals through a regional, national or global disease reporting system. In such a system, new batches of data keep being collected over time, and a decision needs to be made immediately after new data are collected regarding whether there is a disease outbreak at the current time point. This is the disease surveillance problem that will be focused in this article. There are some existing methods for solving this problem, most of which use the disease incidence data only. In practice, however, disease incidence is often associated with some covariates, including the air temperature, humidity, and other weather or environmental conditions. In this article, we develop a new methodology for disease surveillance which can make use of helpful covariate information to improve its effectiveness. A novelty of this new method is behind the property that only those covariate information that is associated with a true disease outbreak can help trigger a signal. The new method can accommodate seasonality, spatio-temporal data correlation, and nonparametric data distribution. These features make it feasible to use in many real applications.

Monitoring operating room performance with control charts: findings from a Greek public hospital.

OBJECTIVE: To describe the development and demonstrate the use of a statistical framework based on statistical quality control (SQC) in order to monitor the performance of operating rooms (ORs). DESIGN: Data related to scheduled surgical operations have been collected from the information system of an existing Greek hospital. The data that contain the anesthesia and operation start and completion times of the operations carried out in the 14 ORs of the hospital are analyzed using control p-charts and hypotheses testing. The results obtained provide crucial information to health-care managers. SETTING: A large Greek public hospital. PARTICIPANTS: Real-world data captured on daily basis from January 2015 to November 2017. INTERVENTION: The proportion of the idle time of an OR over its total available time is proposed as an OR key performance index. We present two directions of data monitoring and analysis: one that uses control p-charts and a second based on hypotheses testing. The improved Laney's p΄-chart and the Laney's approach for cross-sectional data are employed in order to overcome overdispersion that affects OR idle time data. RESULTS: The proposed methodology allows hospital management (i) to monitor the percentage of the idle time of an operating room through time and (ii) to identify the ORs that demonstrate exceptionally high or low percentage of idle time at a given period of time. CONCLUSION: SQC charts are simple, yet powerful tools that may support the hospital management in monitoring OR performance and decision-making. The development of a dedicated management information system that automatically captures the required data and constructs the corresponding control charts would support effectively managerial decision-making.

A hybrid Shewhart chart for visualizing and learning from epidemic data.

OBJECTIVE: As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time. CONTEXT: Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken. METHODS: We designed a hybrid Shewhart chart describing four 'epochs' ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts. RESULTS: The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021. CONCLUSIONS: The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it.

Understanding variation in reported covid-19 deaths with a novel Shewhart chart application.

OBJECTIVE: Motivated by the coronavirus disease 2019 (covid-19) pandemic, we developed a novel Shewhart chart to visualize and learn from variation in reported deaths in an epidemic. CONTEXT: Without a method to understand if a day-to-day variation in outcomes may be attributed to meaningful signals of change-rather than variability we would expect-care providers, improvement leaders, policy-makers, and the public will struggle to recognize if epidemic conditions are improving. METHODS: We developed a novel hybrid C-chart and I-chart to detect within a geographic area the start and end of exponential growth in reported deaths. Reported deaths were the unit of analysis owing to erratic reporting of cases from variability in local testing strategies. We used simulation and case studies to assess chart performance and define technical parameters. This approach also applies to other critical measures related to a pandemic when high-quality data are available. CONCLUSIONS: The hybrid chart detected the start of exponential growth and identified early signals that the growth phase was ending. During a pandemic, timely reliable signals that an epidemic is waxing or waning may have mortal implications. This novel chart offers a practical tool, accessible to system leaders and frontline teams, to visualize and learn from daily reported deaths during an epidemic. Without Shewhart charts and, more broadly, a theory of variation in our epidemiological arsenal, we lack a scientific method for a real-time assessment of local conditions. Shewhart charts should become a standard method for learning from data in the context of a pandemic or epidemic.