Application of statistical process control in healthcare improvement: systematic review

Quality improvement collaborative to increase access to caesarean sections: lessons from Bihar, India

BACKGROUND

Countries with resource-poor health systems have struggled to improve access to and the quality of caesarean section (C-section; CS) for women seeking care in public health facilities. Access to C-section in Bihar State remains very low, while access has increased in many other contexts.

METHODS

We used quality improvement (QI) combined with targeted resource management to test and implement changes that were designed to increase C-section delivery. We compared C-section delivery percentages after the interventions across eight intervened (QI) hospitals and between QI hospitals and the remaining 22 non-intervened (non-QI) hospitals with baseline CS <10%. We linked patterns of improvement and sustainability to theoretical drivers of improvement and timing of interventions.

RESULTS

In QI hospitals, C-section percentage increased from 2.9% at baseline to 5.9% in the intervention phase and 4.6% in the post intervention phase. In non-QI hospitals, we observed a small change (2.6-3.3%) during the same time period of the interventions in the QI hospitals. Addition of skilled personnel resulted in increased C-section percentage in QI hospitals (3.6-5.9%) but not non-QI hospitals (3.4-3.2%).

CONCLUSIONS

C-section availability increased for a population of women giving birth following initiation of QI BTS collaborative in a low-income country public sector setting that has historically struggled to provide this service. Addition of obstetric and operating room resources alone, without interventions to support system changes, may not result in additional increase in C-section delivery. The adaptive implementation model may contribute to efforts to provide more access to C-sections in other very resource-limited settings.

The CHEWMA Chart: A New Statistical Control Approach for Microclimate Monitoring in Preventive Conservation of Cultural Heritage

A new statistical control chart denoted as CHEWMA (Cultural Heritage EWMA) is proposed for microclimate monitoring in preventive conservation. This tool is a real-time detection method inspired by the EN 15757:2010 standard, serving as an alternative to its common adaptations. The proposed control chart is intended to detect short-term fluctuations (STFs) in temperature (T) and relative humidity (RH), which would enable timely interventions to mitigate the risk of mechanical damage to collections. The CHEWMA chart integrates the Exponentially Weighted Moving Average (EWMA) control chart with a weighting mechanism that prioritizes fluctuations occurring near extreme values. The methodology was validated using RH time series recorded by seven dataloggers installed at the Alava Fine Arts Museum, and, from these, seventy simulated time series were generated to enhance the robustness of the analyses. Sensitivity analyses demonstrated that, for the studied dataset, the CHEWMA chart exhibits stronger similarity to the application of EN 15757:2010 than other commonly used real-time STF detection methods in the literature. Furthermore, it provides a flexible option for real-time applications, enabling adaptation to specific conservation needs while remaining aligned with the general framework established by the standard. To the best of our knowledge, this is the first statistical process control chart designed for the field of preventive conservation of cultural heritage. Beyond assessing CHEWMA's performance, this study reveals that, when adapting the procedures of the European norm by developing a new real-time approach based on a simple moving average (herein termed SMA-FT), a window of approximately 14 days is more appropriate for STF detection than the commonly assumed 30-day period in the literature.

[A quality improvement study on improving the follow-up rate of preterm infants after discharge]

OBJECTIVES

To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology.

METHODS

The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved.

RESULTS

The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023.

CONCLUSIONS

The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.

Diagnostic Accuracy Prior to Congenital Heart Defect Surgery: A Multicenter Collaboration

BACKGROUND

Echocardiography is the mainstay for diagnosing congenital heart disease (CHD). Diagnostic errors can lead to suboptimal surgical outcomes.

OBJECTIVES

This multicenter pediatric echocardiography collaborative learning initiative explores reasons for diagnostic errors, investigates associations between patient- and center-specific factors and errors, and relays the benefits of a multicenter approach to decrease these errors as a first step to improve CHD surgical outcomes.

METHODS

Participating centers submitted diagnostic evaluations on patients prior to 2-ventricle repair into a central database. We held virtual meetings to revise variables and discuss cases to learn from each other.

RESULTS

Fourteen pediatric echocardiography laboratories entered data on 1,476 consecutive patients with specific cardiac diagnoses who underwent a two-ventricle repair over 11 months. The mean error rate across centers was 7.1% (103 errors, 17/126-6/125). Seventy-six (74%) errors were preventable or possibly preventable. Cognitive (43%) and imaging factors (47%) commonly contributed to these errors. Moderate to severe impact on postoperative outcomes occurred in 19 (25%) preventable or possibly preventable errors. There were no statistically significant associations between patient- or center-specific factors and errors.

CONCLUSIONS

This work represents the feasibility and advantages of a multicenter approach to preoperative diagnostic errors. Variability existed in sedated protocols, number of echos needed, use of other modalities, and in other processes. Common anatomic areas were found. Rather than undertaking isolated, single-center projects, this collaborative is poised to learn about novel changes that would improve diagnostic accuracy across centers as a first step to advancing surgical outcomes for patients with CHD.

How to Design and Write Your Quality Improvement Study for Publication: Pearls and Pitfalls

Objective

To describe a pragmatic process for translating quality improvement (QI) projects into published manuscripts.

Scope

Types of QI work that are generalizable and have broad relevance (to journals and readers), design principles that are important for publishable QI work, how QI manuscript organization might differ from biomedical manuscripts, how to use and not to use Standards for Quality Improvement Reporting Excellence and other guidelines, pitfalls, and how to avoid/repair them.

Comparison of risk-adjusted cumulative quality control charts compared with standardized mortality ratios in critical care

PURPOSE

The optimal method for monitoring intensive care unit (ICU) performance is unknown. We sought to compare process control charts using standardized mortality ratio (SMR), p-charts, and cumulative sum (CUSUM) charts for detecting increases in risk-adjusted mortality within ICUs.

METHODS

Using data from 17 medical-surgical ICUs that included 29,592 patients in Ontario, Canada, we created risk-adjusted p-charts and SMRs on monthly intervals and CUSUM charts. We defined positive signals as any data point that was above the 3-sigma limit (approximating a 99% confidence interval [CI]) on a p-chart, any data point whose 95% CI did not include 1 for the SMR charts, and when a data point exceeded control limits for an odds ratio of 1.5 for CUSUM charts. We simulated increases in mortality of 10%, 30%, and 50% for each ICU to determine the sensitivity of each method. We calculated sensitivity as the number of positive signals divided by the number of ICUs (equal to number of simulated events).

RESULTS

Cumulative sum charts generated 31 signals in 12 different ICUs, while p-charts and SMR agreed in 10 and 6 of these signals, respectively, followed by 21 signals from p-charts across 14 ICUs (agreement in 10 of these signals for both CUSUM and SMR) and 15 signals from SMR charts across eight ICUs (agreement from p-charts and CUSUM in 10 and six signals, respectively). The p-chart had a sensitivity of 88% (95% CI, 73 to 104) for a 50% simulated increase in ICU mortality followed by CUSUM at 71% (95% CI, 49 to 102) and SMR at 59% (95% CI, 35 to 82). Performance with lower simulated increases was poor for all three methods.

CONCLUSIONS

P-charts created with risk-adjusted mortality at monthly intervals are potentially useful tools for monitoring ICU performance. Future studies should consider usability testing with ICU leaders and application of these methods to additional clinical domains.

Use of the Model for Improvement to Reduce Hyperglycemia in Adult Patients Admitted to a Public Tertiary Care Hospital

BACKGROUND

The objective of this study was to reduce by 50% the occurrence of average daily blood glucose (ADBG) > 180 mg/dL among noncritical patients admitted to a surgical ward at a public tertiary care hospital.

METHODS

This project ran from April 2022 to June 2023 and used the Model for Improvement (MFI) method. Health care Failure Modes and Effects Analysis was used to identify and analyze failure modes in hyperglycemia management, and a driver diagram (DD) was used to prioritize and structure changes. The Plan-Do-Study-Act (PDSA) tool facilitated the change process. Data were collected using standardized forms and monitored with run charts, considering process, outcome, and balance indicators. The DD included 12 changes focusing on protocol implementation, adequate medical prescription, correct insulin administration, proper blood glucose monitoring, appropriate diet prescription, safe care transitions between units, routine of publication and discussion of indicators, leadership engagement with frontline workers on hyperglycemia management, educational actions, and defining roles and responsibilities.

RESULTS

A 69.0% reduction in ADBG > 180 mg/dL and a 100% reduction in ADBG > 300 mg/dL were achieved, though hypoglycemic events increased from 8 to 11 per 100 patient-days using insulin or oral antidiabetic medications. Reductions in nonconformities in medical prescription and insulin administration (50.0% and 71.4%, respectively) were also achieved.

CONCLUSION

In this pilot project, use of the MFI led to improved prescription practices, insulin administration, and blood glucose control, reducing the rate of hyperglycemia in hospitalized patients.

Risk-adjusted observed minus expected cumulative sum (RA O-E CUSUM) chart for visualisation and monitoring of surgical outcomes

To improve patient safety, surgeons can continually monitor the surgical outcomes of their patients. To this end, they can use statistical process control tools, which primarily originated in the manufacturing industry and are now widely used in healthcare. These tools belong to a broad family, making it challenging to identify the most suitable methodology to monitor surgical outcomes. The selected tools must balance statistical rigour with surgeon usability, enabling both statistical interpretation of trends over time and comprehensibility for the surgeons, their primary users. On one hand, the observed minus expected (O-E) chart is a simple and intuitive tool that allows surgeons without statistical expertise to view and interpret their activity; however, it may not possess the sophisticated algorithms required to accurately identify important changes in surgical performance. On the other hand, a statistically robust tool like the cumulative sum (CUSUM) method can be helpful but may be too complex for surgeons to interpret and apply in practice without proper statistical training. To address this issue, we developed a new risk-adjusted (RA) O-E CUSUM chart that aims to provide a balanced solution, integrating the visualisation strengths of a user-friendly O-E chart with the statistical interpretation capabilities of a CUSUM chart. With the RA O-E CUSUM chart, surgeons can effectively monitor patients' outcomes and identify sequences of statistically abnormal changes, indicating either deterioration or improvement in surgical outcomes. They can also quantify potentially preventable or avoidable adverse events during these sequences. Subsequently, surgical teams can try implementing changes to potentially improve their performance and enhance patient safety over time. This paper outlines the methodology for building the tool and provides a concrete example using real surgical data to demonstrate its application.

New horizons in systems engineering and thinking to improve health and social care for older people

Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.

Transforming healthcare performance monitoring - A cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.

Cluster randomised evaluation of a training intervention to increase the use of statistical process control charts for hospitals in England: making data count

BACKGROUND

The way that data are presented can influence quality and safety initiatives. Time-series charts highlight changes but do not clarify whether data lie outside expected variation. Statistical process control (SPC) charts make this distinction and have been demonstrated to be effective in supporting hospital initiatives. To improve the uptake of the SPC methodology by hospitals in England, a training intervention was created. The current study evaluates the effectiveness of that training against the background of a wider national initiative to encourage the adoption of SPC charts.

METHODS

A parallel cluster randomised trial was conducted with 16 English NHS hospitals. Half were randomised to the training intervention and half to the control. The primary analysis compares the difference in use of SPC charts within hospital board papers in a postrandomisation period (adjusting for baseline use). Trainees completed feedback forms with Likert scale and open-ended items.

RESULTS

Fifteen hospitals participated across the study arms. SPC chart use increased in both intervention and control hospitals between the baseline and postrandomisation period (29 and 30 percentage points, respectively). There was no statistically significant difference between the intervention and control hospitals in use of SPC charts in the postrandomisation period (average absolute difference 9% (95% CI -34% to 52%). In the feedback forms, 93.9% (n=31/33) of trainees affirmed learning and 97.0% (n=32/33) had formed an intention to change their behaviour.

CONCLUSIONS

Control chart use increased in both intervention and control hospitals. This is consistent with a rising tide and/or contamination effect, such that the culture of control chart use is spreading across hospitals in England. Further research is needed to support hospitals implementing SPC training initiatives and to link SPC implementation to quality and safety outcomes. Such research could support future quality and safety initiatives nationally and internationally.

TRIAL REGISTRATION NUMBER

NCT04977414.

Impact of a Daily Huddle on Safety in Perioperative Services

BACKGROUND

Communication failures contribute to quality gaps and may lead to serious safety events (SSEs) in the operating room (OR). Our perioperative services team experienced an increased rate of SSEs in 2020. Event analysis revealed clustered causes: communication failures and lack of timely information to prepare for cases. Consequently, the team implemented a daily morning OR safety huddle conducted before bringing patients into the OR to reduce quality gaps and improve communication.

METHODS

The attending surgeon and anesthesiologist, circulating nurse, and scrub staff are required to be present. Cases are discussed using a standard format designed by the OR team with built-in time for questions and clarifications. The surgeon initiates the huddle; the circulating nurse leads and records the discussion. OR leadership initially performed daily audits but gradually reduced them when huddles became standard operating procedure (SOP). SSEs were recorded from December 2015 to September 2020 preintervention and October 2020 to July 2023 postintervention.

RESULTS

Following the implementation of huddles, there were no SSEs for more than 900 days (2.0 SSEs/year preintervention vs. 0.0 SSEs/year postintervention). The first SSE during the postintervention period occurred in March 2023. Huddle compliance was consistently > 95%. No delays were observed in first-case on-time starts postintervention. The huddle is now SOP for all general OR teams and interventional radiology.

CONCLUSION

Implementing the morning safety huddle contributed to a reduction in the rate of SSEs without introducing delays to first-case start-times.

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case Study

BACKGROUND

Patient-driven innovation in health care is an emerging phenomenon with benefits for patients with chronic conditions, such as cystic fibrosis (CF). However, previous research has not examined what may facilitate or hinder the implementation of such innovations from the provider perspective.

OBJECTIVE

The aim of this study was to explain variations in the adoption of a patient-driven innovation among CF clinics.

METHODS

A comparative multiple-case study was conducted on the adoption of a patient-controlled app to support self-management and collaboration with health care professionals (HCPs). Data collection and analysis were guided by the nonadoption, abandonment, spread, scale-up, and sustainability and complexity assessment tool (NASSS-CAT) framework. Data included user activity levels of patients and qualitative interviews with staff at 9 clinics (n=8, 88.9%, in Sweden; n=1, 11.1%, in the United States). We calculated the maximum and mean percentage of active users at each clinic and performed statistical process control (SPC) analysis to explore how the user activity level changed over time. Qualitative data were subjected to content analysis and complexity analysis and used to generate process maps. All data were then triangulated in a cross-case analysis.

RESULTS

We found no evidence of nonadoption or clear abandonment of the app. Distinct patterns of innovation adoption were discernable based on the maximum end-user activity for each clinic, which we labeled as low (16%-23%), middle (25%-47%), or high (58%-95%) adoption. SPC charts illustrated that the introduction of new app features and research-related activity had a positive influence on user activity levels. Variation in adoption was associated with providers' perceptions of care process complexity. A higher perceived complexity of the value proposition, adopter system, and organization was associated with lower adoption. In clinics that adopted the innovation early or those that relied on champions, user activity tended to plateau or decline, suggesting a negative impact on sustainability.

CONCLUSIONS

For patient-driven innovations to be adopted and sustained in health care, understanding patient-provider interdependency and providers' perspectives on what generates value is essential.

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.

Post-Pandemic Growth in 9-1-1 Paramedic Calls and Emergency Department Transports Surpasses Pre-Pandemic Rates in the COVID-19 Era: Implications for Paramedic Resource Planning

OBJECTIVES

The COVID-19 pandemic led to a decline in emergency department (ED) visits and a subsequent return to baseline pre-pandemic levels. It is unclear if this trend extended to paramedic services and if patient cohorts accessing paramedics changed. We examined trends and associations between paramedic utilization (9-1-1 calls and ED transports) and the COVID-19 timeframe.

METHODS

We conducted a retrospective cross-sectional study using paramedic call data from the Hamilton Paramedic Services from January 2016 to December 2023. We included all 9-1-1 calls where paramedics responded to an incident, excluding paramedic interfacility transfers. We calculated lines of best fit for the pre-pandemic period (January 2016 to January 2020) and compared their predictions to the actual volumes in the post-pandemic period (May 2021 to December 2023). We used an interrupted time series regression model to determine the association between pandemic timeframes (pre-, during-, post-COVID-19) and paramedic utilization (9-1-1 calls and ED transports), while testing for annual seasonality.

RESULTS

During the study timeframe, 577,278 calls for paramedics were received and 413,491 (71.6%) were transported to the ED. Post-pandemic, 9-1-1 calls exceeded predicted pre-pandemic levels by 1,298 per month, while ED transports exceeded by 543 per month. The pandemic significantly reduced monthly 9-1-1 calls (-588.2, 95% CI -928.8 to -247.5) and ED transports (-677.3, 95% CI -927.0 to -427.5). Post-pandemic, there was a significant and sustained resurgence in monthly 9-1-1 calls (1,208.0, 95% CI 822.1 to 1,593.9) and ED transports (868.8, 95% CI 585.8 to 1,151.7). Both models exhibited seasonal variations.

CONCLUSIONS

Post-pandemic, 9-1-1-initiated paramedic calls experienced a substantial increase, surpassing pre-pandemic growth rates. ED transports returned to pre-pandemic levels but with a steeper and continuous pattern of growth. The resurgence in paramedic 9-1-1 calls and ED transports post-COVID-19 emphasizes an urgent necessity to expedite development of new care models that address how paramedics respond to 9-1-1 calls and transport to overcrowded EDs.

Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19

Control charts help epidemiologists and healthcare professionals monitor disease incidence and prevalence in real time, preventing outbreaks and health emergencies. However, there remains a notable gap in the comprehensive exploration and application of these techniques, particularly in the context of monitoring and managing disease outbreaks. This study analyses and categorizes worldwide control chart applications from 2000 to 2023 in outbreak monitoring in over 20 countries, focusing on corona-virus (COVID-19), and chooses optimal control charts for monitoring US COVID-19 death waves from February 2020 to December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, study type, and chart design. A selected optimal chart is applied to monitor COVID-19 death patterns and waves in the USA. Control chart adoption in epidemiology monitoring increased during the COVID-19 pandemic, with annual patterns showing a rise in 2021 to 2023 (18%, 36%, 41%). Important variables from 2000 to 2019 include influenza counts, Salmonella cases, and infection rates, while COVID-19 studies focus more on cases, infection rates, symptoms, and deaths. Among 22 countries, the USA (29%) is the top applier of control charts. The monitoring of USA COVID-19 deaths reveals 8 waves with varying severity  >  >  >  >  >  >  > . The associated with the JN.1 variant, highlights ongoing challenges. This study emphasizes the significance of control charts in outbreak monitoring for early disease diagnosis and intervention. Control charts help healthcare workers manage epidemics using data-driven methods, improving public health. COVID-19 mortality analysis emphasizes their importance, encouraging worldwide use.

Quality indicators in respiratory therapy

Quality indicators in healthcare refer to measurable and quantifiable parameters used to assess and monitor the performance, effectiveness, and safety of healthcare services. These indicators provide a systematic way to evaluate the quality of care offered, and thereby to identify areas for improvement and to ensure that patient care meets established standards and best practices. Respiratory therapists play a vital role in areas of clinical administration such as infection control practices and quality improvement initiatives. Quality indicators serve as essential metrics for respiratory therapy departments to assess and enhance the overall quality of care. By systematically tracking and analyzing indicators related to infection control, treatment effectiveness, and adherence to protocols, respiratory care practitioners can identify areas to improve and implement evidence-based changes. This article reviewed how to identify, implement, and monitor quality indicators specific to the respiratory therapy departments to set benchmarks and enhance patient outcomes.

A time-adjusted control chart for monitoring surgical outcome variations

BACKGROUND

Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts.

METHODS

A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen's Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference.

RESULTS

Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89.

CONCLUSION

Seasonal and secular trends can be controlled as potential confounders to improve signal detection in surgical outcomes monitoring over time.

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 Data-Driven Approach to Optimizing Medical-Legal Partnership Performance and Joint Advocacy

Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.

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.

Use of statistical process control in quality improvement projects in abdominal surgery: a PRISMA systematic review

BACKGROUND

The use of quality improvement methodology has increased in recent years due to a perceived benefit in effectively reducing morbidity, mortality and length of stay. Statistical process control (SPC) is an important tool to evaluate these actions, but its use has been limited in abdominal surgery. Previous systematic reviews have examined the use of SPC in healthcare, but relatively few surgery-related articles were found at that time.

OBJECTIVE

To perform a systematic review (SR) to evaluate the application of SPC on abdominal surgery specialties between 2004 and 2019.

METHODS

An SR following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram was completed using Embase and Ovid Medline with terms related to abdominal surgery and SPC.

RESULTS

A total of 20 articles were selected after applying the exclusion criteria. Most of the articles came from North America, Europe and Australia, and half have been published in the last 5 years. The most common outcome studied was surgical complications. Urology, colorectal and paediatric surgery made up most of the articles. Articles show the application of SPC in various outcomes and the use of different types of graphs, demonstrating flexibility in using SPC. However, some studies did not use SPC in a robust way and these studies were of variable quality.

CONCLUSION

This study shows that SPCs are being applied increasingly for most surgical specialties; however, it is still less used than in other fields, such as anaesthesia. We identified conceptual errors in several studies, such as issues with the design or incorrect data analysis. SPCs can be used to increase the quality of surgical care; the use should increase, but critically, the analysis needs to improve to prevent erroneous conclusions being drawn.

Increasing the use of anxiolysis and analgesia for paediatric procedures in a community emergency department network: a quality improvement initiative

Prior reports describe the care children receive in community EDs (CEDs) compared with paediatric EDs (PEDs) as uneven. The Emergency Medical Services for Children (EMSC) initiative works to close these gaps using quality improvement (QI) methodology. Project champion from a community hospital network identified the use of safe pharmacological and non-pharmacological anxiolysis and analgesia (A&A) as one such gap and partnered with EMSC to address it. Our primary Specific, Measurable, Achievable, Relevant and Time-Bound (SMART) aim was to increase intranasal midazolam (INM) use for common, anxiety-provoking procedures on children <8 years of age from 2% to 25% in a year.EMSC facilitated a QI team with representation from the CED and regional children's hospitals. Following the model for improvement, we initiated a process analysis of this CED A&A practice. Review of all paediatric procedural data identified common anxiety-provoking simple procedures as laceration repairs, abscess drainage and foreign body removal. Our SMART aims were benchmarked to two regional PEDs and tracked through statistical process control. A balancing metric was ED length of stay (ED LOS) for patients <8 years of age requiring a laceration repair. Additionally, we surveyed CED frontline staff and report perceptions of changes in A&A knowledge, attitudes and practice patterns. These data prioritised and informed our key driver diagram which guided the Plan-Do-Study-Act (PDSA) cycles, including guideline development, staff training and cognitive aids.Anxiety-provoking simple procedures occurred on average 10 times per month in children <8 years of age. Through PDSA cycles, the monthly average INM use increased from 2% to 42%. ED LOS was unchanged, and the perceptions of provider's A&A knowledge, attitudes and practice patterns improved.A CED-initiated QI project increased paediatric A&A use in a CED network. An A&A toolkit outlines our approach and may simplify spread from academic children's hospitals to the community.

Detecting atypical alert behavior through statistical process control: Clinical decision support alert frequency visualizations

Clinical decision support (CDS) alerts are designed to work according to a set of clearly defined criteria and have the potential to improve clinical care. To efficiently and proactively review abnormally functioning CDS alerts, we postulate that the introduction of a dashboard with statistical process control (SPC) charting will lead to effective detection of erratic alert behavior. We identified custom CDS alerts from an academic medical center that were recorded and monitored in a longitudinal fashion and the data warehouses where this information was stored. We created a dashboard of alert frequency using SPC charts, applied SPC rules for classification of variation, and validated dashboard data. From June-August 2022, the dashboard effectively pulled in data to visually depict alert behavior. SPC-defined parameters for standard deviation from the mean were applied to visualizations and allowed for rapid review of alerts with greatest variation. These alerts were subsequently investigated, and it was determined that they were functioning correctly. The most profound abnormalities detected during implementation reflected changes in practice and not system errors, though further investigation into thresholds for statistical significance will benefit this field. We conclude that SPC visualizations are a time-efficient and effective method of identifying CDS malfunctions.

Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions

BACKGROUND

 Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring.

OBJECTIVES

 In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes.

METHODS

 We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system.

RESULTS

 Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention.

CONCLUSION

 We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.

Closing the equity gap as we move to the elimination of seclusion: Early results from a national quality improvement project

OBJECTIVE

Use of seclusion within mental health inpatient facilities is harmful for consumers and staff, but it is still used in many Aotearoa New Zealand and Australian facilities, at higher, inequitable rates for the indigenous populations of both countries. We report early results from a national programme to eliminate seclusion in mental health services in Aotearoa New Zealand, using a bicultural approach to reduce inequity for Māori.

METHOD

The 'Zero Seclusion: Safety and dignity for all' programme, with programme teams nationwide, developed a co-designed bicultural change package combining Māori cultural and Western clinical interventions, incorporating quality improvement methodologies. Outcome measures included seclusion rates, duration, and average number of episodes per person admitted, by ethnicity, with a focus on equity.

RESULTS

Nationally, rates of seclusion for Māori reduced from the 12-month (to August 2019) baseline mean monthly rate of 7.5% to 6.8%, sustained from late 2020 to September 2022. The duration of seclusion for Māori reduced by 33% (4.5 h at baseline to 3.0).

CONCLUSION

A focus on inequity for Māori in use of seclusion, and a bicultural approach with cultural and clinical interventions, has been associated with a national reduction in rates and duration of seclusion for Māori.

Mass mortality event in South American sea lions (Otaria flavescens) correlated to highly pathogenic avian influenza (HPAI) H5N1 outbreak in Chile

In Chile, since January 2023, a sudden and pronounced increase in strandings and mortality has been observed among South American (SA) sea lions (Otaria flavescens), prompting significant concern. Simultaneously, an outbreak of highly pathogenic avian influenza H5N1 (HPAIV H5N1) in avian species has emerged since December 2022. To investigate the cause of this unexpected mortality, we conducted a comprehensive epidemiological and pathologic study. One hundred sixty-nine SA sea lions were sampled to ascertain their HPAIV H5N1 status, and long-term stranding trends from 2009 to 2023 were analyzed. In addition, two animals were necropsied. Remarkably, a significant surge in SA sea lion strandings was observed initiating in January 2023 and peaking in June 2023, with a count of 4,545 stranded and deceased animals. Notably, this surge in mortality correlates geographically with HPAIV outbreaks affecting wild birds. Among 168 sampled SA sea lions, 34 (20%) tested positive for Influenza A virus, and 21 confirmed for HPAIV H5N1 2.3.4.4b clade in tracheal/rectal swab pools. Clinical and pathological evaluations of the two necropsied stranded sea lions revealed prevalent neurological and respiratory signs, including disorientation, tremors, ataxia, and paralysis, as well as acute dyspnea, tachypnea, profuse nasal secretion, and abdominal breathing. The lesions identified in necropsied animals aligned with observed clinical signs. Detection of the virus via immunohistochemistry (IHC) and real-time PCR in the brain and lungs affirmed the findings. The findings provide evidence between the mass mortality occurrences in SA sea lions and HPAIV, strongly indicating a causal relationship. Further studies are needed to better understand the pathogenesis and transmission.

Addressing knowledge gaps in Surgical Safety Checklist use: statistical process control analysis of a surgical quality improvement programme in Ethiopia

BACKGROUND

The WHO Surgical Safety Checklist reduces morbidity and mortality after surgery, but uptake remains challenging. In particular, low-income countries have been found to have lower rates of checklist use compared with high-income countries. The aim of this study was to determine the impact of educational workshops on Surgical Safety Checklist use implemented as part of a quality improvement initiative in five hospitals in Ethiopia that had variable experience with the Surgical Safety Checklist.

METHODS

From April 2019 to September 2020, each hospital implemented a 6-month surgical quality improvement programme, which included a Surgical Safety Checklist workshop. Statistical process control methodology was used to understand the variation in Surgical Safety Checklist compliance before and after workshops and a time-series analysis was performed using population-averaged generalized estimating equation Poisson regression. Checklist compliance was defined as correctly completing a sign in, timeout, and sign out. Incidence rate ratios of correct checklist use pre- and post-intervention were calculated and the change in mean weekly compliance was predicted.

RESULTS

Checklist compliance data were obtained from 2767 operations (1940 (70 per cent) pre-intervention and 827 (30 per cent) post-intervention). Mean weekly checklist compliance improved from 27.3 to 41.2 per cent (mean difference 13.9 per cent, P = 0.001; incidence rate ratio 1.51, P = 0.001). Hospitals with higher checklist compliance at baseline had the greatest overall improvements in compliance, more than 50 per cent over pre-intervention, while low-performing hospitals showed no improvement.

CONCLUSION

Surgical Safety Checklist workshops improved checklist compliance in hospitals with some experience with its use. Workshops had little effect in hospitals unfamiliar with the Surgical Safety Checklist, emphasizing the importance of multifactorial interventions and culture-change approaches. In receptive facilities, short workshops can accelerate behaviour change.

Monitoring the performance of a dedicated weaning unit using risk-adjusted control charts for the weaning rate in prolonged mechanical ventilation

BACKGROUND

Weaning rate is an important quality indicator of care for patients with prolonged mechanical ventilation (PMV). However, diverse clinical characteristics often affect the measured rate. A risk-adjusted control chart may be beneficial for assessing the quality of care.

METHODS

We analyzed patients with PMV who were discharged between 2018 and 2020 from a dedicated weaning unit at a medical center. We generated a formula to estimate monthly weaning rates using multivariate logistic regression for the clinical, laboratory, and physiologic characteristics upon weaning unit admission in the first two years (Phase I). We then applied both multiplicative and additive models for adjusted p-charts, displayed in both non-segmented and segmented formats, to assess whether special cause variation existed.

RESULTS

A total of 737 patients were analyzed, including 503 in Phase I and 234 in Phase II, with average weaning rates of 59.4% and 60.3%, respectively. The p-chart of crude weaning rates did not show special cause variation. Ten variables from the regression analysis were selected for the formula to predict individual weaning probability and generate estimated weaning rates in Phases I and II. For risk-adjusted p-charts, both multiplicative and additive models showed similar findings and no special cause variation.

CONCLUSION

Risk-adjusted control charts generated using a combination of multivariate logistic regression and control chart-adjustment models may provide a feasible method to assess the quality of care in the setting of PMV with standard care protocols.

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

How do healthcare providers use national audit data for improvement?

BACKGROUND

Substantial resources are invested by Health Departments worldwide in introducing National Clinical Audits (NCAs). Yet, there is variable evidence on the NCAs' effectiveness and little is known on factors underlying the successful use of NCAs to improve local practice. This study will focus on a single NCA (the National Audit of Inpatient Falls -NAIF 2017) to explore: (i) participants' perspectives on the NCA reports, local feedback characteristics and actions undertaken following the feedback underpinning the effective use of the NCA feedback to improve local practice; (ii) reported changes in local practice following the NCA feedback in England and Wales.

METHODS

Front-line staff perspectives were gathered through interviews. An inductive qualitative approach was used. Eighteen participants were purposefully sampled from 7 of the 85 participating hospitals in England and Wales. Analysis was guided by constant comparative techniques.

RESULTS

Regarding the NAIF annual report, interviewees valued performance benchmarking with other hospitals, the use of visual representations and the inclusion of case studies and recommendations. Participants stated that feedback should target front-line healthcare professionals, be straightforward and focused, and be delivered through an encouraging and honest discussion. Interviewees highlighted the value of using other relevant data sources alongside NAIF feedback and the importance of continuous data monitoring. Participants reported that engagement of front-line staff in the NAIF and following improvement activities was critical. Leadership, ownership, management support and communication at different organisational levels were perceived as enablers, while staffing level and turnover, and poor quality improvement (QI) skills, were perceived as barriers to improvement. Reported changes in practice included increased awareness and attention to patient safety issues and greater involvement of patients and staff in falls prevention activities.

CONCLUSIONS

There is scope to improve the use of NCAs by front-line staff. NCAs should not be seen as isolated interventions but should be fully embedded and integrated into the QI strategic and operational plans of NHS trusts. The use of NCAs could be optimised, but knowledge of them is poor and distributed unevenly across different disciplines. More research is needed to provide guidance on key elements to consider throughout the whole improvement process at different organisational levels.

Control Charts Usage for Monitoring Performance in Surgery: A Systematic Review

OBJECTIVES

The control chart is a graphical tool for data interpretation that detects aberrant variations in specific metrics, ideally leading to the identification of special causes that can be resolved. A clear assessment of control chart utilization and its potential impact in surgery is required to justify recommendations for its dissemination. This review aims to describe how performance monitoring using control charts was used over time in surgery.

METHODS

A systematic search of PubMed regarding statistical process control in surgery from its inception until December 2019 was performed using Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Information extracted from selected publications included study aim and population setting, monitored indicators, control charts methodological parameters, and implementation strategy.

RESULTS

One hundred thirteen studies met the selection criteria with a median of 1916 monitored patients. Overall, 57.5% of studies focused on control chart methodology, 24.8% aimed at evaluating performance changes using control charts retrospectively, and 17.7% implemented control charts for continuous quality improvement prospectively. Although there was a great diversity of used indicators and charting tools, the evaluation of patient safety (72.6%) or efficiency (15.9%) metrics based on Shewhart control chart (33.6%) or cumulative sum chart (54.9%) were common. To foster control charts implementation, 14 studies promoted their periodic review, but only three assessed their impact on patient outcomes.

CONCLUSIONS

The scientific literature supports the feasibility and utility of control chart to improve patient safety in multiple surgical settings. Additional studies are necessary to reveal the optimal manner in which to implement this affordable tool in surgical practice.

Making more nurses, one minute at a time: an efficiency and quality improvement project in emergency triage

Emergency triage is a short-duration, high-volume process so small reductions in the time taken to triage one patient can have large repercussions on the total amount of triage time. At the emergency department of a large inner-city hospital, an efficiency and quality improvement project was undertaken to reduce the time taken to safely triage patients and optimise the use of triage nurses' time. The project involved removing processes that did not contribute to the primary aim of triage, supporting individual triage nurses to improve their performance where needed, and optimising the triage process. A 44% reduction in mean triage episode time was seen, equating to 18,000 minutes of triage nurses' time saved every month. This near doubling of triage capacity was associated with an improvement in triage accuracy. The article describes the project, which used lean management principles and statistical process control methods, and discusses its implications for emergency triage.

Monitoring Length of Stay of Acute Myocardial Infarction Patients: A Times Series Analysis Using Statistical Process Control

GOAL

Given that length of stay (LOS) of acute myocardial infarction (AMI) patients has a significant impact on the utilization of hospital resources and the health status of communities, this study focused on how best to monitor LOS of AMI patients admitted to U.S. hospitals by employing statistical process control (SPC).

METHODS

Data were abstracted from the Healthcare Cost and Utilization Project Nationwide Readmissions Database between 2010 and 2016. A total of 1,491 patients were examined in the study. Patients who were admitted to nonfederal government (public) hospitals in metropolitan areas of at least 1 million residents with the primary diagnosis of AMI were abstracted. They were excluded if they developed AMI secondary to an interventional procedure or surgery, died during their index hospitalization, and were admitted and discharged on the same day. Patients were also excluded if they were discharged to short-term hospitals, nursing facilities, intermediate care facilities, home healthcare, or against medical advice. Individual moving range (I-MR) charts were used to monitor LOS of individual AMI patients in each subgroup from 2010 to 2016.

PRINCIPAL FINDINGS

The results showed I-MR charts could be used to indicate statistically out-of-control signals on LOS. Specifically, I-MR charts showed that LOS decreased between 2010 and 2016. LOS appeared to be longer at teaching hospitals compared to nonteaching hospitals and varied by gender. Female patients appeared to stay longer than male patients in the hospitals.

PRACTICAL APPLICATIONS

The application of SPC and control charts can facilitate improved decision-making in healthcare organizations. This study shows the value of integrating control charts in administrative and medical decision-making processes. It may also help healthcare providers and managers achieve higher quality and lower cost of care.

Effects of COVID-19 pandemic restrictions on an Australian neonatal and paediatric retrieval service

AIM

The COVID-19 pandemic and associated travel and social distancing restrictions have reduced paediatric intensive care unit admissions for respiratory illnesses. The effects on retrieval (transport) services remain unquantified. Our study examined the utility of statistical process control in assessing the impact of the COVID-19 pandemic on the number of neonatal and paediatric transfers in an Australian retrieval service.

METHODS

Data collected prospectively from the SA Ambulance Service MedSTAR Emergency Retrieval database in South Australia were analysed from January 2015 to June 2021. Statistical process control methodology, a combination of a time series analysis and assessment for common and special cause variation, was used to assess the impact of the COVID-19 pandemic on retrieval workload (primary outcome of interest).

RESULTS

A total of 5659 neonatal and paediatric transfers occurred during the study period and were included. A significant decrease in paediatric transfers occurred after the initial lockdown measures in March 2020 were announced in South Australia (special cause variation). However, a similar reduction was not observed for neonatal transfers (common cause variation).

CONCLUSION

Our study demonstrates that statistical process control may be effectively used to understand the effects of external events and processes on usual activity patterns in the retrieval setting. We found a reduction in retrieval numbers for paediatric transfers but no effect on neonatal transfer numbers. The decline in paediatric transfers was primarily attributed to reduced respiratory cases.

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

Predicting Outcome for Early Attention Training After Acquired Brain Injury

Background

The training of impaired attention after acquired brain injury is central for successful reintegration in daily living, social, and working life. Using statistical process control, we found different improvement trajectories following attention training in a group of relatively homogeneous patients early after acquired brain injury (ABI).

Objective

To examine the contribution of pre-injury factors and clinical characteristics to differences in outcome after early attention training.

Materials and Methods

Data collected in a clinical trial comparing systematic attention training (APT) with activity-based attention training (ABAT) early after brain injury were reanalyzed.

Results

Stroke patients (p = 0.004) with unifocal (p = 0.002) and right hemisphere lesions (p = 0.045), and those with higher mental flexibility (TMT 4) (p = 0.048) benefitted most from APT training. Cognitive reserve (p = 0.030) was associated with CHANGE and APT as the sole pre-injury factor. For TBI patients, there was no statistical difference between the two treatments.

Conclusion

Our study identifies indiscernible factors predicting improvement after early attention training. APT is beneficial for patients with right-hemispheric stroke in an early recovery phase. Knowledge of prognostic factors, including the level of attention deficit, diagnosis, and injury characteristics, is vital to maximizing the efficiency of resource allocation and the effectiveness of rehabilitative interventions to enhance outcomes following stroke and TBI.

How to Build and Assess the Quality of Healthcare-Related Research Questions

The objective of this article is to describe a simplified process for building and assessing the quality of healthcare-related research questions. This process consisted of three stages. The first stage aimed to select and explore a field of science. This field would be the area for which to identify outputs, such as units of analysis, variables, and objectives. The second stage aimed to write structured research questions, taking into account the outputs of the first stage. In general, the structure of research questions starts with interrogative adverbs (e.g., what and when), auxiliary verbs (e.g., is there and are there), or other auxiliaries (e.g., do, does, and did); followed by nouns nominalized from verbs of research objectives, such as association, correlation, influence, causation, prediction, application; research variables (e.g., risk factors, efficiency, effectiveness, and safety); and units of analysis (e.g., patients with hypertension and general hospitals). The third stage aimed to assess the quality and feasibility of the research questions against a set of criteria such as relevance, originality, generalizability, measurability, communicability, availability of resources, and ethical issues. By following the proposed simplified process, novice researchers may learn how to write structured research questions of sound scientific value.

Key observations in terms of management of electronic health records from a mHealth perspective

The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.

Managing Shiga Toxin-Producing E. coli Using Statistical Process Control Charts for Routine Health and Production Monitoring in Pig Farming

Oedema disease (ED) caused by Shiga-toxin-producing E. coli in pigs is a serious life-threatening disease, particularly among weaned piglets. When a preventive protocol is adopted in a specific farm, interpretation of effectiveness is often complicated in field conditions due to natural or “common cause” variation. For this reason, in this study a Statistical process control (SPC) approach was used to retrospectively evaluate the application of an ED preventive protocol (lower protein diet, ad-libitum fiber, vaccination at 5 days of age) in an infected commercial piglets' weaning site. The analysis was established over a 9-years period (n = 75 consecutive batches; 1,800 weaners per batch) using mortality for each batch as the key parameter of health and production; the statistics and the control limits (mean ± 3-fold sd; UCL, upper control limit; LCL, lower control limit) were based on data from the first 28 batches (Period 1) before the onset of the first ED clinical signs. The charts allowed the detection of defined out of control batches (i.e., with mortality out of the intervention limits) from batch 29 ongoing, exploring a Period 2 (unstable production and ED clinical signs; 36 batches) and a Period 3 (application of the ED preventive protocol; 11 batches). Mortality evaluation using SPC revealed a production system defined under-control (mean moving range bar = 1,34%; UCL = 4,37%; LCL = 0%) during Period 1. During Period 2, charts lost the state of statistical control, as showed by several signals of special cause variation due to the ED outbreak. Period 3 was characterized again by a state of statistical control, where no signals of special cause variation was showed. In conclusion, the retrospective application of SPC charts in the present study was able to confirm the efficacy of an ED preventive protocol in reducing mortality in a piglets' weaning site. SPC charting is suggested as an useful tool to provide insights into relationships between health, managerial, and welfare decision and some selected iceberg parameters in livestock.

Saudi Radiologists’ and Radiographers’ Perceptions of Accreditation Programmes in Clinical Radiology Departments: A Cross-Sectional Study

Purpose

The hospital accreditation programme is an assessment tool that involves a comprehensive evaluation by an external independent accreditation body to ensure consistency in clinical practice by adhering to the established standards and guidelines. The study aims to investigate Radiology professionals’ perceptions of the impact of accreditation and implementation of change towards the quality-of-service delivery in Radiology Departments.

Methods

A cross-sectional prospective study was conducted in Saudi Arabia among radiology professionals (ie, radiographers and radiologists) from July to September 2021. After obtaining institutional review board approval from the local ethics committee and using a non-probability convenient sampling technique, 335 participants completed the survey, which was distributed via social media channels, and through professional networks within hospitals across the country.

Results

A total of 335 participants agreed to participate. The study’s participants strongly agreed that the accreditation programmes have positively impacted customer satisfaction and care provided to patients. A significant difference was identified in the level of agreement on the effect of accreditation programmes when hospital types and personnel qualifications were tested (P < 0.05). Radiology personnel who worked in academic hospitals and who had diplomas and PhDs degrees showed a significantly higher level of agreement than other participants (P < 0.05).

Conclusion

Saudi radiologists and radiographers showed strong agreement or agreement towards hospital accreditation programmes domains’ criteria. The results of the study support the need to bridge the gap between higher-level management and employees in order to facilitate change and enhance the standards of quality and practice in radiology departments. Additional policies are needed to continue and strengthen quality improvement programmes.

Bridging the Silos: A Comparative Analysis of Implementation Science and Improvement Science

Background

Implementation science and improvement science have similar goals of improving health care services for better patient and population outcomes, yet historically there has been limited exchange between the two fields. Implementation science was born out of the recognition that research findings and effective practices should be more systematically disseminated and applied in various settings to achieve improved health and welfare of populations. Improvement science has grown out of the wider quality improvement movement, but a fundamental difference between quality improvement and improvement science is that the former generates knowledge for local improvement, whereas the latter is aimed at producing generalizable scientific knowledge.

Objectives

The first objective of this paper is to characterise and contrast implementation science and improvement science. The second objective, building on the first, is to highlight aspects of improvement science that potentially could inform implementation science and vice versa.

Methods

We used a critical literature review approach. Search methods included systematic literature searches in PubMed, CINAHL, and PsycINFO until October 2021; reviewing references in identified articles and books; and the authors' own cross-disciplinary knowledge of key literature.

Findings

The comparative analysis of the fields of implementation science and improvement science centred on six categories: (1) influences; (2) ontology, epistemology and methodology; (3) identified problem; (4) potential solutions; (5) analytical tools; and (6) knowledge production and use. The two fields have different origins and draw mostly on different sources of knowledge, but they have a shared goal of using scientific methods to understand and explain how health care services can be improved for their users. Both describe problems in terms of a gap or chasm between current and optimal care delivery and consider similar strategies to address the problems. Both apply a range of analytical tools to analyse problems and facilitate appropriate solutions.

Conclusions

Implementation science and improvement science have similar endpoints but different starting points and academic perspectives. To bridge the silos between the fields, increased collaboration between implementation and improvement scholars will help to clarify the differences and connections between the science and practice of improvement, to expand scientific application of quality improvement tools, to further address contextual influences on implementation and improvement efforts, and to share and use theory to support strategy development, delivery and evaluation.

Markovchart: an R package for cost-optimal patient monitoring and treatment using control charts

Control charts originate from industrial statistics, but are constantly seeing new areas of application, for example in health care (Thor et al. in BMJ Qual Saf 16(5):387–399, 2007. https://doi.org/10.1136/qshc.2006.022194; Suman and Prajapati in Int J Metrol Qual Eng, 2018. https://doi.org/10.1051/ijmqe/2018003). This paper is about the package, an implementation of generalised Markov chain-based control charts with health care applications in mind and with a focus on cost-effectiveness. The methods are based on Zempléni et al. (Appl Stoch Model Bus Ind 20(3):185–200, 2004. https://doi.org/10.1002/asmb.521), Dobi and Zempléni (Qual Reliab Eng Int 35(5):1379–1395, 2019a. https://doi.org/10.1002/qre.2518, Ann Univ Sci Budapestinensis Rolando Eötvös Nomin Sect Comput 49:129–146, 2019b). The implemented ideas in the package were motivated by problems encountered by health care professionals and biostatisticians when assessing the effects and costs of different monitoring schemes and therapeutic regimens. However, the implemented generalisations may be useful in other (e.g., engineering) applications too, as they mainly revolve around the loosening of assumptions seen in traditional control chart theory. The package is able to model processes with random shift sizes (i.e., the degradation of the patient’s health), random repair (i.e., treatment) and random time between samplings (i.e., visits) as well. The article highlights the flexibility of the methods through the modelling of different disease progression and treatment scenarios and also through an application on real-world data of diabetic patients.

Board level “Picture-Understanding-Action”: a new way of looking at quality

Purpose

The purpose of this study is to present a quality improvement approach titled “Picture-Understanding-Action” used in Ireland to enhance the role of healthcare boards in the oversight of healthcare quality and its improvement.

Design/methodology/approach

The novel and practical “Picture-Understanding-Action” approach was implemented using the Model for Improvement to iteratively introduce changes across three quality improvement projects. This approach outlines the concepts and activities used at each step to support planning and implementation of processes that allow a board to effectively achieve its role in overseeing and improving quality. This approach matured over three quality improvement projects.

Findings

The “Picture” included quantitative and qualitative aspects. The quantitative “Picture” consisted of a quality dashboard/profile of board selected outcome indicators representative of the health system using statistical process control (SPC) charts to focus discussion on real signals of change. The qualitative picture was based on the experience of people who use and work in health services which “people-ised” the numbers. Probing this “Picture” with collective grounding, curiosity and expert training/facilitation developed a shared “Understanding”. This led to “Action(s)” from board members to improve the “Picture” and “Understanding” (feedback action), to ask better questions and make better decisions and recommendations to the executive (feed-forward action). The Model for Improvement, Plan-Do-Study-Act cycles and a co-design approach in design and implementation were key to success.

Originality/value

To the authors’ knowledge, this is the first time a board has undertaken a quality improvement (QI) project to enhance its own processes. It addresses a gap in research by outlining actions that boards can take to improve their oversight of quality of care.