Modelling and monitoring social network change based on exponential random graph models.

This paper aims to detect anomalous changes in social network structure in real time and to offer early warnings by phase II monitoring social networks. First, the exponential random graph model is used to model social networks. Then, a test and online monitoring technique of the exponential random graph model is developed based on the split likelihood-ratio test after determining the model and its parameters for a specific data set. This proposed approach uses pseudo-maximum likelihood estimation and likelihood ratio to construct the test statistics, avoiding the several steps of discovering Monte Carlo Markov Chain maximum likelihood estimation through an iterative method. A bisection algorithm for the control limit is given. Simulations on three data sets Flobusiness, Kapferer and Faux.mesa.high are presented to study the performance of the procedure. Different change points and shift sizes are compared to see how they affect the average run length. A real application example on the MIT reality mining social proximity network is used to illustrate the proposed modelling and online monitoring methods.

Phase I quality control framework for monitoring organ-at-risk dose.

Objective.The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling'sT2chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC).Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans.Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans.Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

Parameter free AEWMA control chart for dispersion in semiconductor manufacturing.

The study presents a new parameter free adaptive exponentially weighted moving average (AEWMA) control chart tailored for monitoring process dispersion, utilizing an adaptive approach for determining the smoothing constant. This chart is crafted to adeptly detect shifts within anticipated ranges in process dispersion by dynamically computing the smoothing constant. To assess its effectiveness, the chart's performance is measured through concise run-length profiles generated from Monte Carlo simulations. A notable aspect is the incorporation of an unbiased estimator in computing the smoothing constant through the suggested function, thereby improving the chart's capability to identify different levels of increasing and decreasing shifts in process dispersion. The comparison with an established adaptive EWMA-S2 dispersion chart highlights the considerable efficiency of the proposed chart in addressing diverse magnitudes of process dispersion shifts. Additionally, the study includes an application to a real-life dataset, showcasing the practicality and user-friendly nature of the proposed chart in real-world situations.

New adaptive EWMA CV control chart with application to the sintering process.

This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.

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.

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data.

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient's health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications.

BACKGROUND: Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge. PURPOSE: The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application. METHODS: A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process. RESULTS: The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex. CONCLUSIONS: Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.

Ensuring safe and equitable discharge: a quality improvement initiative for individuals with hypertensive disorders of pregnancy.

OBJECTIVE: To improve timely and equitable access to postpartum blood pressure (BP) monitoring in individuals with hypertensive disorders of pregnancy (HDP). METHODS: A quality improvement initiative was implemented at a large academic medical centre in the USA for postpartum individuals with HDP. The primary aim was to increase completed BP checks within 7 days of hospital discharge from 40% to 70% in people with HDP in 6 months. Secondary aims included improving rates of scheduled visits, completed visits within 3 days for severe HDP and unattended visits. The balancing measure was readmission rate. Statistical process control charts were used, and data were stratified by race and ethnicity. Direct feedback from birthing individuals was obtained through phone interviews with a focus on black birthing people after a racial disparity was noted in unattended visits. RESULTS: Statistically significant improvements were noted across all measures. Completed and scheduled visits within 7 days of discharge improved from 40% to 76% and 61% to 90%, respectively. Completed visits within 3 days for individuals with severe HDP improved from 9% to 49%. The unattended visit rate was 26% at baseline with non-Hispanic black individuals 2.3 times more likely to experience an unattended visit than non-Hispanic white counterparts. The unattended visit rate decreased to 15% overall with an elimination of disparity. A need for BP devices at discharge and enhanced education for black individuals was identified through patient feedback. CONCLUSION: Timely follow-up of postpartum individuals with HDP is challenging and requires modification to our care delivery. A hospital-level quality improvement initiative using birthing individual and frontline feedback is illustrated to improve equitable, person-centred care.

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.

Time is a terrible thing to waste: optimising use of intraoperative monitoring practitioner time towards maximising in-house IOM service provision and reducing spend on external provision.

Intraoperative monitoring (IOM) during orthopaedic and neurosurgical operations informs surgeons about the integrity of patients' central and peripheral nervous systems. It is provided by IOM practitioners (IOMPs), who are usually neurophysiology healthcare scientists. Increasing awareness of the benefits for patient safety and surgical outcomes, along with post-COVID-19 service recovery, has resulted in a material increase in demand for IOM provision nationally, and particularly at Salford Royal Hospital (SRH), which is a regional specialist neurosciences centre.There is a shortage of IOMPs in the UK National Health Service (NHS). At SRH, this is exacerbated by staff capacity shortage, requiring £202 800 of supplementary private provision in 2022.At SRH, IOMPs work in pairs. Our productive time is wasted by delays to surgical starts beyond our control and by paired working for much of a surgery session. This quality improvement (QI) project set out to release productive time by: calling the second IOMP to theatre only shortly before start time, the other IOMP returning to the office during significant delays, releasing an IOMP from theatre when appropriate and providing a laptop in theatre for other work.We tested and refined these change ideas over two plan-do-study-act improvement cycles. Compared with complete paired working, we increased the time available for additional productive work and breaks from an average of 102 to 314 min per operating day, not quite achieving our project target of 360 min.The new ways of working we developed are a step towards ability (when staff capacity increases) to test supporting two (simultaneous) operations with three IOMPs (rather than two pairs of IOMPs). Having significantly improved the use of staff time, we then also used our QI project data to make a successful business case for investment in two further IOMP posts with a predicted net saving of £20 000 per year along with other associated benefits.

Use of rational subgrouping to understand variation and opportunity for improvement in time to ultrasound.

OBJECTIVE: To understand factors that contribute to variation in time to abdominal and/or pelvic ultrasound in pediatric patients in an emergency department (ED) by utilizing rational subgrouping to assess opportunity for improvement. METHODS: All abdominal and pelvic ultrasounds conducted in the Alberta Children's Hospital ED from May 2019 to April 2021 were included. Time of study order and time of study completion were obtained from the electronic health record. Statistical process control (SPC) I-charts were used to analyze the quarterly median number of minutes from ultrasound order to completion. Rational subgrouping was used to stratify the data based on sex, age, and ED shift type, and identify special cause variation between groups. Findings were used to inform local decision-making. RESULTS: Special cause variation was detected among subgroups for sex, age group, and shift type. The median time from order of an abdominal and/or pelvic ultrasound to completion of study was 155 min. Females had a median order to completion time of 178 min, while males had a completion time of 131 min. From age 0 to 3, the median time was 110 min, compared to 149 min for ages 4 to 11 and 171 min for ages 12 and older. Day shifts had a median order to completion time of 145 min, compared to 129 min for evening shifts and 269 min for night shifts. CONCLUSIONS: Longer time to study completion was observed in female patients, older patients, and during night shifts. Use of rational subgrouping supported understanding of variation among subgroups of patients evaluated with abdominal and/or pelvic ultrasound. This allowed informed decision-making regarding opportunities for improvement. Rational subgrouping is a useful methodology in planning QI initiatives as it identifies sources of variation within a nonhomogeneous population and allows for judicious decision-making in a context of limited resources.

RéSUMé: OBJECTIF: Comprendre les facteurs qui contribuent à la variation dans le temps de l'échographie abdominale et/ou pelvienne chez les patients pédiatriques dans un service d'urgence (ED) en utilisant un sous-groupe rationnel pour évaluer la possibilité d'amélioration. MéTHODES: Toutes les échographies abdominales et pelviennes effectuées à l'urgence de l'Hôpital pour enfants de l'Alberta de mai 2019 à avril 2021 ont été incluses. L'ordre et l'heure de fin de l'étude ont été obtenus à partir du dossier médical électronique. Des diagrammes en I de contrôle statistique des processus (SPC) ont été utilisés pour analyser le nombre médian trimestriel de minutes entre la commande d'échographie et l'achèvement. Un sous-groupe rationnel a été utilisé pour stratifier les données en fonction du sexe, de l'âge et du type de quart de travail aux urgences, et pour déterminer la variation des causes spéciales entre les groupes. Les conclusions ont été utilisées pour éclairer la prise de décisions à l'échelle locale. RéSULTATS: Une variation de cause spéciale a été détectée parmi les sous-groupes pour le sexe, le groupe d'âge et le type de poste. Le temps médian entre la commande d'une échographie abdominale et/ou pelvienne et la fin de l'étude était de 155 minutes. Les femelles avaient un délai médian de 178 minutes, tandis que les mâles avaient un délai de 131 minutes. De 0 à 3 ans, le temps médian était de 110 minutes, comparativement à 149 minutes pour les 4 à 11 ans et 171 minutes pour les 12 ans et plus. L'ordre médian des quarts de jour était de 145 minutes, comparativement à 129 minutes pour les quarts de soir et 269 minutes pour les quarts de nuit. CONCLUSIONS: On a observé un délai plus long avant l'achèvement de l'étude chez les patientes, les patientes âgées et pendant les quarts de nuit. L'utilisation de sous-groupes rationnels a permis de comprendre la variation entre les sous-groupes de patients évalués par échographie abdominale et/ou pelvienne. Cela a permis de prendre des décisions éclairées sur les possibilités d'amélioration. Le sous-regroupement rationnel est une méthode utile pour planifier les initiatives d'AQ, car il permet de déterminer les sources de variation au sein d'une population non homogène et de prendre des décisions judicieuses dans un contexte de ressources limitées.

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.

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure.

Aims: Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results: In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion: The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.

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.

A monitoring framework for health care processes using Generalized Additive Models and Auto-Encoders.

In recent years, there has been a considerable focus on developing effective methods for monitoring health care processes. Utilizing Statistical Process Monitoring (SPM) approaches, particularly risk-adjusted control charts, has emerged as a highly promising approach for achieving robust frameworks for this aim. Considering risk-adjusted control charts, longitudinal health care process data is typically monitored by establishing a regression relationship between various risk factors (explanatory variables) and patient outcomes (response variables). While the majority of prior research has primarily employed logistic models in risk-adjusted control charts, there are more intricate health care processes that necessitate the incorporation of both parametric and nonparametric risk factors. In such scenarios, the Generalized Additive Model (GAM) proves to be a suitable choice, albeit it often introduces higher computational complexity and associated challenges. Surprisingly, there are limited instances where researchers have proposed advancements in this direction. The primary objective of this paper is to introduce an SPM framework for monitoring health care processes using a GAM over time, coupled with a novel risk-adjusted control chart driven by machine learning techniques. This control chart is implemented on a data set encompassing two stroke types: ischemic and hemorrhagic. The key focus of this study is to monitor the stability of the relationship between stroke types and predefined explanatory variables over time within this data set. Extensive simulation results, based on real data from patients with acute stroke, demonstrate the remarkable flexibility of the proposed method in terms of its detection capabilities compared to conventional approaches.

Performance assessment of surgical tracking systems based on statistical process control and longitudinal QA.

A system for performance assessment and quality assurance (QA) of surgical trackers is reported based on principles of geometric accuracy and statistical process control (SPC) for routine longitudinal testing. A simple QA test phantom was designed, where the number and distribution of registration fiducials was determined drawing from analytical models for target registration error (TRE). A tracker testbed was configured with open-source software for measurement of a TRE-based accuracy metric ε and Jitter (J). Six trackers were tested: 2 electromagnetic (EM - Aurora); and 4 infrared (IR - 1 Spectra, 1 Vega, and 2 Vicra) - all NDI (Waterloo, ON). Phase I SPC analysis of Shewhart mean (x¯) and standard deviation (s) determined system control limits. Phase II involved weekly QA of each system for up to 32 weeks and identified Pass, Note, Alert, and Failure action rules. The process permitted QA in <1 min. Phase I control limits were established for all trackers: EM trackers exhibited higher upper control limits than IR trackers in ε (EM: x¯Îµ âˆ¼2.8-3.3 mm, IR: x¯Îµ âˆ¼1.6-2.0 mm) and Jitter (EM: x¯jitter âˆ¼0.30-0.33 mm, IR: x¯jitter âˆ¼0.08-0.10 mm), and older trackers showed evidence of degradation - e.g. higher Jitter for the older Vicra (p-value < .05). Phase II longitudinal tests yielded 676 outcomes in which a total of 4 Failures were noted - 3 resolved by intervention (metal interference for EM trackers) - and 1 owing to restrictive control limits for a new system (Vega). Weekly tests also yielded 40 Notes and 16 Alerts - each spontaneously resolved in subsequent monitoring.

Utilising routine clinical laboratory data to support quality improvement in health care: Application of a national acute kidney injury alert system as a proof of concept.

BACKGROUND: Acute kidney injury (AKI) is a global health issue known to cause avoidable harm and death. Improvement in its prevention and management is therefore considered an important goal for the health-care sector. The work here aimed to develop a tool which could be used to robustly and reliably measure, monitor, and compare the effectiveness of health-care interventions related to AKI across the Welsh NHS, a mechanism which did not exist previously. METHODS: Using serum creatinine (SCr) as a biomarker for AKI and a validated national data-set collected from the all Wales Laboratory Information Management System, work involved applying Donabedian's framework to develop indicators with which to measure outcomes related to AKI, and exploring the potential of statistical process control (SPC) techniques for analysing data on these indicators. RESULTS: Rate of AKI incidence and 30-day AKI-associated mortality are proposed as valid, feasible indicators with which to measure the effectiveness of health-care interventions related to AKI. The control chart, funnel plot, and Pareto chart are proposed as appropriate, robust SPC techniques to analyse and visualise variation in AKI-related outcomes. CONCLUSIONS: This work demonstrates that routinely collected large SCr data offer a significant opportunity to monitor and therefore inform improvement in patient outcomes related to AKI. Moreover, while this work concerns utilisation of SCr data for improvement in AKI strategies, it is a proof of concept which could be replicated for other routinely collected clinical laboratory data, to improve the prevention and/or management of the conditions to which they relate.

Reducing first appointment delays for electron radiotherapy patients by improving the treatment planning pathway: a quality improvement project.

Clatterbridge Cancer Centre (CCC) is a specialist hospital trust in England with three sites.Delay to the start of an appointment for radiotherapy, especially the first appointment (a 'New Start') is poor, both for operational efficiency and patient experience, causing stress for both patients and staff. Our aim is for the New Start to begin within 30 min of the allotted appointment time. To this end, we established another aim: for 'Final Checks' to the radiotherapy plan to be completed at least 30 min prior to the New Start appointment time.Prior to this quality improvement (QI) project, only 33% of electron-treatment New Start appointments started within the target 30 min (the average delay was 52.4 min) and only 48% of the corresponding Final Checks had been completed by their 30 min prior target.The treatment pathway for these patients was redesigned, with the aim of 90% of New Start appointments starting within 30 min of the allotted appointment time.By the end of this QI project, 69.2% of New Start appointments started within 30 min of the appointment time (with average delay reduced to 27.2 min), and 92.3% of Final Checks were completed by their 30 min prior target. We also reduced the number of safety (Datix) incidents due to plan not ready from 10 to 0. A year after the project, we have held most of the time improvements and still have had 0 plan-not-ready Datix.The largest improvement was achieved by introducing a proxy (without the patient present) 'day 0' appointment. This takes place in advance of the New Start appointment to enable earlier planning. Subsequent improvements included: automating previously manual planning calculations, making the care path consistent with other external beam radiotherapy care paths at CCC to reduce staff cognitive load and sharing key performance data with staff.

Observation of a quality improvement initiative to contextually adapt and use Robson classification in real time to collect data around CS delivery and to develop strategies to reduce CS rate.

The rising trend in caesarean section (CS) rate is a global concern and, in this hospital too, it rose from 21.5% in 2010 to 32.6% in 2018. The team followed the point of care quality improvement methodology and conducted a series of Plan-Do-Study-Act cycles to contextually modify and adapt Robson classification into the existing workflow to improve the process of documentation and data collection for CS in the first 6 months (January 2019-June 2019) and then to use these data to develop strategies to reduce CS rate below 30% in the next 18 months.To evaluate the impact of developed strategies, the team plotted the data on Statistical Process Control (XmR) chart. The baseline mean CS rate was 32.6%. The team observed a shift in the CS rate data twice, between April 2020 and December 2020 and between August 2021 and February 2021 with the mean 27.8% and 28.9%, respectively. October 2021 onwards, the team also observed a sustained reduction in the CS rate in women undergoing CS who had one previous CS. The mean CS rate reduced from 94% to 86%.The reductions in the CS rate were not sustained and followed by an increase again. The project highlighted the complexity of the factors related to CS delivery and the multidimensional barriers of sustaining the reduction in the CS rate. This is a well-sustained ongoing QI intervention and the team is further working on identifying the underlying factors to improve the efficacy of the interventions to sustain the reduction in the CS rate.This hospital represents the general population of North India seeking care in public healthcare facilities. Therefore, despite being a single-centre study, the population served and interpretations drawn from this study are generalisable to other hospitals with similar settings.