Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis.

Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.

Joint monitoring of mean and variance using Max-EWMA control chart under lognormal process with application to engine oil data.

The control charts are frequently employed in process monitoring to assess the average and variability of a process, assuming a normal distribution. However, it is worth noting that some process distributions tend to exhibit a positively skewed distribution, such as the lognormal distribution. This article proposed a maximum exponential weighted moving average control chart for joint monitoring of mean and variance under a lognormal process. The proposed control chart is evaluated by using the run length profile such as ARL and SDRL. The Monte Carlo simulation is conducted by using the R language to find the run length profile. An application is presented to demonstrate the design of the proposed control chart.

Decreasing incidence of admission neonatal hypothermia in Gandhi Memorial Hospital, Addis Ababa, Ethiopia: quality improvement project.

BACKGROUND: WHO reported that neonatal hypothermia accounts for about 27% of newborn deaths worldwide. It is a serious concern in Ethiopia and other parts of sub-Saharan Africa; it poses a serious threat to global health, increasing morbidity and mortality. Hypothermic neonates are more likely to experience respiratory distress, infections and other issues that could result in longer hospital stays and delayed development. The objective of this quality improvement project was to minimise intensive medical treatments, maximise resource usage and enhance overall health outcomes for newborns at Gandhi Memorial Hospital by reducing neonatal hypothermia. METHODS: Over 10 months (from 1 March 2021 to 30 January 2022), neonatal hypothermia incidence was assessed using Quality Supervision Mentoring Team and Health Management Information System data. Root cause analysis and literature review led to evidence-based interventions in a change bundle. After team training and neonatal intensive care unit (NICU) relocation, Plan-Do-Study-Act cycles tested the bundle. Close temperature monitoring and data collection occurred. Run charts evaluated intervention success against baseline data, informing conclusions about effectiveness. RESULT: The quality improvement project reduced neonatal hypothermia in NICU admissions from a baseline median of 80.6% to a performance median of 30%. CONCLUSION AND RECOMMENDATION: The quality improvement project at Gandhi Memorial Hospital effectively reduced neonatal hypothermia through interventions such as the temperature management bundle and NICU relocation, leading to improved patient care, fewer hypothermic neonates and enhanced body temperature management. Continuous monitoring, adherence to best practices, sharing success and outcome assessment are crucial for enhancing the project's effectiveness and sustaining positive impacts on neonatal hypothermia reduction and patient outcomes.

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.

Quality improvement initiative: improving obstetric triaging practices in a rural maternal hospital in central India.

Triaging of obstetric patients by emergency care providers is paramount. It helps provide appropriate and timely management to prevent further injury and complications. Standardised trauma acuity scales have limited applicability in obstetric triage. Specific obstetric triage index tools improve maternal and neonatal outcomes but remain underused. The aim was to introduce a validity-tested obstetric triage tool to improve the percentage of correctly triaged patients (correctly colour-coded in accordance with triage index tool and attended to within the stipulated time interval mandated by the tool) from the baseline of 49% to more than 90% through a quality improvement (QI) process.A team of nurses, obstetricians and postgraduates did a root cause analysis to identify the possible reasons for incorrect triaging of obstetric patients using process flow mapping and fish bone analysis. Various change ideas were tested through sequential Plan-Do-Study-Act (PDSA) cycles to address issues identified.The interventions included introduction and application of an obstetric triage index tool, training of triage nurses and residents. We implemented these interventions in eight PDSA cycles and observed outcomes by using run charts. A set of process, output and outcome indicators were used to track if changes made were leading to improvement.Proportion of correctly triaged women increased from the baseline of 49% to more than 95% over a period of 8 months from February to September 2020, and the results have been sustained in the last PDSA cycle, and the triage system is still sustained with similar results. The median triage waiting time reduced from the baseline of 40 min to less than 10 min. There was reduction in complications attributable to improper triaging such as preterm delivery, prolonged intensive care unit stay and overall morbidity. It can be thus concluded that a QI approach improved obstetric triaging in a rural maternity hospital in India.

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles.

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using  the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.

Quality Evaluation of New Types of Layered Composites for Flooring Materials.

The need, or even the obligation, to take care of the natural environment compels a search for new technological solutions, or for known solutions to be adapted to new applications. The maxim is 'don't harm, but improve the world for future generations'. In the wood industry in particular, given that it is based on a natural raw material, we must look for ecological solutions. Trees grow, but the demand for wood exceeds the volume of tree growth. In industrial manufacturing, one of the ways to make full use of wood is through chipless processing, which occurs during rotary cutting (peeling). In addition, wood is a natural material, each fragment of which has a range of properties. In addition, wood defects in quality manipulation generate a lot of waste. The aim of this study was to analyse the quality effect of the tested layered composites for flooring materials on production application. The practical purpose was to exchange actual sawing-based production for chipless production. The composite base layers were made of pine wood (Pinus L.) veneers with differing quality classes. The samples were subjected to three-point bending tests to calculate the moduli of elasticity and stiffness, which are the most important parameters. Because both analysed parameters describe product quality, the analyses were based on the creation of Shewhart control charts for each parameter. In theory, these control charts are tools for analysing whether the production process is stable and yields predictable results. To have full control over the process, five elements have to be applied: central line (target), two types of control lines (upper and lower) and two types of specification lines (upper and lower). New types of layered composites for flooring may be applied to production once verified using Shewhart control charts. It turns out that it is possible to produce the base layer of the flooring materials using the rotary cutting (peeling) method without having to analyse the quality of the raw material. This is a way to significantly increase the efficiency of production in every element of manufacturing.

Patient and Operational Factors Do Not Substantively Affect the Annual Departmental Quality of Anesthesiologists' Clinical Supervision and Nurse Anesthetists' Work Habits.

INTRODUCTION: Although safety climate, teamwork, and other non-technical skills in operating rooms probably influence clinical outcomes, direct associations have not been shown, at least partially due to sample size considerations. We report data from a retrospective cohort of anesthesia evaluations that can simplify the design of prospective observational studies in this area. Associations between non-technical skills in anesthesia, specifically anesthesiologists' quality of clinical supervision and nurse anesthetists' work habits, and patient and operational factors were examined. METHODS: Eight fiscal years of evaluations and surgical cases from one hospital were included. Clinical supervision by anesthesiologists was evaluated daily using a nine-item scale. Work habits of nurse anesthetists were evaluated daily using a six-item scale. The dependent variables for both groups of staff were binary, whether all items were given the maximum score or not. Associations were tested with patient and operational variables for the entire day. RESULTS: There were 40,718 evaluations of faculty anesthesiologists by trainees, 53,772 evaluations of nurse anesthetists by anesthesiologists, and 296,449 cases that raters and ratees started together. Cohen's d values were small (≤0.10) for all independent variables, suggesting a lack of any clinically meaningful association between patient and operational factors and evaluations given the maximum scores. For supervision quality, the day's count of orthopedic cases was a significant predictor of scores (P = 0.0011). However, the resulting absolute marginal change in the percentage of supervision scores equal to the maximum was only 0.8% (99% confidence interval: 0.2% to 1.4%), i.e., too small to be of clinical or managerial importance. Neurosurgical cases may have been a significant predictor of work habits (P = 0.0054). However, the resulting marginal change in the percentage of work habits scores equal to the maximum, an increase of 0.8% (99% confidence interval: 0.1% to 1.6%), which was again too small to be important. CONCLUSIONS: When evaluating the effect of assigning anesthesiologists and nurse anesthetists with different clinical performance quality on clinical outcomes, supervision quality and work habits scores may be included as independent variables without concern that their effects are confounded by association with the patient or case characteristics. Clinical supervision and work habits are measures of non-technical skills. Hence, these findings suggest that non-technical performance can be judged by observing the typical small sample size of cases. Then, associations can be tested with administrative data for a far greater number of patients because there is unlikely to be a confounding association between patient and case characteristics and the clinicians' non-technical performance.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

Use of improved memory type control charts for monitoring cancer patients recovery time censored data.

Control charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.

Monitoring epidemic processes under political measures.

Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on-line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision-makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID-19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany.

Application of condition-based maintenance for electrical generators based on statistical control charts.

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality. Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators.•Application of statistical control charts for condition-based maintenance of electrical generators.•Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts.•Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process.

The optimal control chart selection for monitoring COVID-19 phases: a case study of daily deaths in the USA.

Epidemiologists frequently adopt statistical process control tools, like control charts, to detect changes in the incidence or prevalence of a specific disease in real time, thereby protecting against outbreaks and emergent health concerns. Control charts have proven essential in instantly identifying fluctuations in infection rates, spotting emerging patterns, and enabling timely reaction measures in the context of COVID-19 monitoring. This study aims to review and select an optimal control chart in epidemiology to monitor variations in COVID-19 deaths and understand pandemic mortality patterns. An essential aspect of the present study is selecting an appropriate monitoring technique for distinct deaths in the USA in seven phases, including pre-growth, growth, and post-growth phases. Stage-1 evaluated control chart applications in epidemiology departments of 12 countries between 2000 and 2022. The study assessed various control charts and identified the optimal one based on maximum shift detection using sample data. This study considered at Shewhart ($\bar X$, $R$, $C$) control charts and exponentially weighted moving average (EWMA) control chart with smoothing parameters λ = 0.25, 0.5, 0.75, and 1 were all investigated in this study. In Stage-2, we applied the EWMA control chart for monitoring because of its outstanding shift detection capabilities and compatibility with the present data. Daily deaths have been monitored from March 2020 to February 2023. Control charts in epidemiology show growing use, with the USA leading at 42% applications among top countries. During the application on COVID-19 deaths, the EWMA chart accurately depicted mortality dynamics from March 2020 to February 2022, indicating six distinct stages of death. The third and fifth waves were extremely catastrophic, resulting in a considerable loss of life. Significantly, a persistent sixth wave appeared from March 2022 to February 2023. The EWMA map effectively determined the peaks associated with each wave by thoroughly examining the time and amount of deaths, providing vital insights into the pandemic's progression. The severity of each wave was measured by the average number of deaths $W5(1899)\,\gt\,W3(1881)\,\gt\,W4(1393)\,\gt\,W1(1036)\,\gt\,W2(853)\,\gt\,(W6(473)$. The USA entered a seventh phase (6th wave) from March 2022 to February 2023, marked by fewer deaths. While reassuring, it remains crucial to maintain vaccinations and pandemic control measures. Control charts enable early detection of daily COVID-19 deaths, providing a systematic strategy for government and medical staff. Incorporating the EWMA chart for monitoring immunizations, cases, and deaths is recommended.

Economic analysis of surgical outcome monitoring using control charts: the SHEWHART cluster randomised trial.

IMPORTANCE: Surgical complications represent a considerable proportion of hospital expenses. Therefore, interventions that improve surgical outcomes could reduce healthcare costs. OBJECTIVE: Evaluate the effects of implementing surgical outcome monitoring using control charts to reduce hospital bed-days within 30 days following surgery, and hospital costs reimbursed for this care by the insurer. DESIGN: National, parallel, cluster-randomised SHEWHART trial using a difference-in-difference approach. SETTING: 40 surgical departments from distinct hospitals across France. PARTICIPANTS: 155 362 patients over the age of 18 years, who underwent hernia repair, cholecystectomy, appendectomy, bariatric, colorectal, hepatopancreatic or oesophageal and gastric surgery were included in analyses. INTERVENTION: After the baseline assessment period (2014-2015), hospitals were randomly allocated to the intervention or control groups. In 2017-2018, the 20 hospitals assigned to the intervention were provided quarterly with control charts for monitoring their surgical outcomes (inpatient death, intensive care stay, reoperation and severe complications). At each site, pairs, consisting of one surgeon and a collaborator (surgeon, anaesthesiologist or nurse), were trained to conduct control chart team meetings, display posters in operating rooms, maintain logbooks and design improvement plans. MAIN OUTCOMES: Number of hospital bed-days per patient within 30 days following surgery, including the index stay and any acute care readmissions related to the occurrence of major adverse events, and hospital costs reimbursed for this care per patient by the insurer. RESULTS: Postintervention, hospital bed-days per patient within 30 days following surgery decreased at an adjusted ratio of rate ratio (RRR) of 0.97 (95% CI 0.95 to 0.98; p<0.001), corresponding to a 3.3% reduction (95% CI 2.1% to 4.6%) for intervention hospitals versus control hospitals. Hospital costs reimbursed for this care per patient by the insurer significantly decreased at an adjusted ratio of cost ratio (RCR) of 0.99 (95% CI 0.98 to 1.00; p=0.01), corresponding to a 1.3% decrease (95% CI 0.0% to 2.6%). The consumption of a total of 8910 hospital bed-days (95% CI 5611 to 12 634 bed-days) and €2 615 524 (95% CI €32 366 to €5 405 528) was avoided in the intervention hospitals postintervention. CONCLUSIONS: Using control charts paired with indicator feedback to surgical teams was associated with significant reductions in hospital bed-days within 30 days following surgery, and hospital costs reimbursed for this care by the insurer. TRIAL REGISTRATION NUMBER: NCT02569450.

A latent space model and Hotelling's T2 control chart to monitor the networks of Covid-19 symptoms.

In the COVID-19 coronavirus pandemic, potential patients that suffer from different symptoms can be diagnosed with COVID-19. At the early stages of the pandemic, patients were mainly diagnosed with fever and respiratory symptoms. Recently, patients with new symptoms, such as gastrointestinal or loss of senses, are also diagnosed with COVID-19. Monitoring these symptoms can help the healthcare system to be aware of new symptoms that can be related to the COVID-19 coronavirus. This article focuses on monitoring the behavior of COVID-19 symptoms over time. In this regard, a Latent space model (LSM) and a Generalized linear model (GLM) are introduced to model the networks of symptoms. We apply Hotelling's T2 control chart to the estimated parameters of the LSM and GLM, to identify significant changes and detect anomalies in the networks. The performance of the proposed methods is evaluated using simulation and calculating average run length (ARL). Then, dynamic networks are generated from a COVID-19 epidemic survey dataset.

Increasing utilisation of a rebound hyperbilirubinaemia calculator in two newborn nurseries.

Neonatal hyperbilirubinaemia requiring phototherapy treatment is a common problem impacting the length of hospital stay and rates of hospital readmission. Previous guidelines included guidance for initiating phototherapy treatment but not for discontinuing phototherapy treatment during initial newborn admission.In response to dissatisfaction from trainees, staff and families regarding the variable approach to discontinuing phototherapy among attending nursery providers, we used quality improvement methodologies to increase utilisation of a rebound hyperbilirubinaemia calculator as a more consistent method for guiding the timing of phototherapy discontinuation. The aim was to increase utilisation of the rebound hyperbilirubinaemia calculator for newborns treated with phototherapy in two newborn nurseries to >90% within 2 years.Sequential interventions focused on increasing provider awareness of the rebound hyperbilirubinaemia calculator and making the calculator simple to access and use.At the university medical centre nursery, the use of the calculator increased from 8.7% to 100%, exceeding the project goal. In the community hospital nursery, there was a statistically significant increase in the rate of utilisation from 3.7% to 79.4%, but this fell slightly below the goal of >90%.Electronic Health Record integration, along with education and addition of prompts to providers, increased utilisation of a rebound hyperbilirubinaemia calculator as a consistent approach for guiding decisions about discontinuing phototherapy treatment in newborns.

Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.

Time is tissue. Want to save millions in wound care? Start early: a QI project to expedite referral of high-risk wound care patients to specialised care.

INTRODUCTION: Wound care is a multibillion-dollar industry, and most research and treatment are geared towards late-stage or end-stage care. The longer a patient has a wound, the more likely it is that complications (like sepsis or vascular compromise) will occur that will both extend treatment and multiply costs. We postulated that much of the suffering and healthcare costs of chronic wounds could be avoided by early identification of high-risk patients and subsequent earlier intervention. In an established regional wound clinic, our aim was to decrease referral times by 50% within 1 year, and to demonstrate the beneficial outcomes on wound healing and total cost of care. METHODS: A prospective interventional quality improvement study was performed between June 2017 and June 2018. We determined baseline referral times to the clinic and then performed three interventions. The effects on referral time, healing time and number of home care visits to achieve wound healing were collected and displayed on annotated control charts. The cost of care and potential for cost avoidance was determined by an analysis of the medical encounters of twenty chronic wound patients. RESULTS: We achieved a 53.6% reduction in average referral times to the clinic, a 59.6% reduction in average healing times and a 66.0% reduction in the average number of home care visits required to achieve healing. Our cost analysis suggested the potential for significant cost avoidance (87.7%) compared with delayed treatment outside the clinic. CONCLUSIONS: Early identification and treatment of patients at high risk for wound chronicity and complications, followed by early referral to and treatment at a specialised wound clinic, resulted in faster healing and reduced health system costs.

QA/QC as a tool for improvement of analytical process in case of determination of tritium specific activity by electrolytic enrichment.

Electrolytic enrichment is used as a method for measuring tritium in environmental waters. All measurement data and parameters are collected in control charts. Together with measurements and modelling of temperatures in the electrolytic cooling system, these charts were used to optimise the entire process. After modification, the scatter of the final sample masses was considerably reduced. Enrichment factor scatter was reduced by 33%, and overall enrichment can now be increased by up to 64%.