Analytical studies: a framework for quality improvement design and analysis

Conducting studies for learning is fundamental to improvement. Deming emphasised that the reason for conducting a study is to provide a basis for action on the system of interest. He classified studies into two types depending on the intended target for action. An enumerative study is one in which action will be taken on the universe that was studied. An analytical study is one in which action will be taken on a cause system to improve the future performance of the system of interest. The aim of an enumerative study is estimation, while an analytical study focuses on prediction. Because of the temporal nature of improvement, the theory and methods for analytical studies are a critical component of the science of improvement

Free exopolysaccharide from Mycoplasma mycoides subsp. mycoides possesses anti-inflammatory properties

In this study we explored the immunomodulatory properties of highly purified free galactan, the soluble exopolysaccharide secreted by Mycoplasma mycoides subsp. mycoides (Mmm). Galactan was shown to bind to TLR2 but not TLR4 using HEK293 reporter cells and to induce the production of the anti-inflammatory cytokine IL-10 in bovine macrophages, whereas low IL-12p40 and no TNF-α, both pro-inflammatory cytokines, were induced in these cells. In addition, pre-treatment of macrophages with galactan substantially reduced lipopolysaccharide (LPS)-induced production of pro-inflammatory cytokines TNF- and IL-12p40 while increasing LPS-induced secretion of immunosuppressive IL-10. Also, galactan did not activate naïve lymphocytes and induced only low production of the Th1 cytokine IFN-γ in Mmm-experienced lymphocytes. Finally, galactan triggered weak recall proliferation of CD4+ T lymphocytes from contagious bovine pleuropneumonia-infected animals despite having a positive effect on the expression of co-stimulatory molecules on macrophages. All together, these results suggest that galactan possesses anti-inflammatory properties and potentially provides Mmm with a mechanism to evade host innate and adaptive cell-mediated immune responses. (Résumé d'auteur

Food and nutrient intake in relation to mental wellbeing

BACKGROUND: We studied food consumption and nutrient intake in subjects with depressed mood, anxiety and insomnia as indices of compromised mental wellbeing. METHODS: The study population consisted of 29,133 male smokers aged 50 to 69 years who entered the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study in 1985–1988. This was a placebo-controlled trial to test whether supplementation with alpha-tocopherol or beta-carotene prevents lung cancer. At baseline 27,111 men completed a diet history questionnaire from which food and alcohol consumption and nutrient intake were calculated. The questionnaire on background and medical history included three symptoms on mental wellbeing, anxiety, depression and insomnia experienced in the past four months. RESULTS: Energy intake was higher in men who reported anxiety or depressed mood, and those reporting any such symptoms consumed more alcohol. Subjects reporting anxiety or depressed mood had higher intake of omega-3 fatty acids and omega-6 fatty acids. CONCLUSIONS: Our findings conflict with the previous reports of beneficial effects of omega-3 fatty acids on mood

Judgment sampling: a health care improvement perspective

Sampling plays a major role in quality improvement work. Random sampling (assumed by most traditional statistical methods) is the exception in improvement situations. In most cases, some type of judgment sample is used to collect data from a system. Unfortunately, judgment sampling is not well understood. Judgment sampling relies upon those with process and subject matter knowledge to select useful samples for learning about process performance and the impact of changes over time. It many cases, where the goal is to learn about or improve a specific process or system, judgment samples are not merely the most convenient and economical approach, they are technically and conceptually the most appropriate approach. This is because improvement work is done in the real world in complex situations involving specific areas of concern and focus; in these situations, the assumptions of classical measurement theory neither can be met nor should an attempt be made to meet them. The purpose of this article is to describe judgment sampling and its importance in quality improvement work and studies with a focus on health care settings

learning from variation in healthcare processes The run chart: a simple analytical tool for References The run chart: a simple analytical tool for learning from variation in healthcare processes

Background: Those working in healthcare today are challenged more than ever before to quickly and efficiently learn from data to improve their services and delivery of care. There is broad agreement that healthcare professionals working on the front lines benefit greatly from the visual display of data presented in time order. Aim: To describe the run chartdan analytical tool commonly used by professionals in quality improvement but underutilised in healthcare. Methods: A standard approach to the construction, use and interpretation of run charts for healthcare applications is developed based on the statistical process control literature. Discussion: Run charts allow us to understand objectively if the changes we make to a process or system over time lead to improvements and do so with minimal mathematical complexity. This method of analyzing and reporting data is of greater value to improvement projects and teams than traditional aggregate summary statistics that ignore time order. Because of its utility and simplicity, the run chart has wide potential application in healthcare for practitioners and decision-makers. Run charts also provide the foundation for more sophisticated methods of analysis and learning such as Shewhart (control) charts and planned experimentation

A hybrid Shewhart chart for visualizing and learning from epidemic data

OBJECTIVE: As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time. CONTEXT: Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken. METHODS: We designed a hybrid Shewhart chart describing four \u27epochs\u27 ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts. RESULTS: The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021. CONCLUSIONS: The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it

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

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

Seven propositions of the science of improvement: exploring foundations

CONTEXT: The phrase Science of Improvement or Improvement Science is commonly used today by a range of people and professions to mean different things, creating confusion to those trying to learn about improvement. In this article, we briefly define the concepts of improvement and science, and review the history of the consideration of improvement as a science. METHODS: We trace key concepts and ideas in improvement to their philosophical and theoretical foundation with a focus on Deming\u27s System of Profound Knowledge. We suggest that Deming\u27s system has a firm association with many contemporary and historic philosophic and scientific debates and concepts. With reference to these debates and concepts, we identify 7 propositions that provide the scientific and philosophical foundation for the science of improvement. FINDINGS: A standard view of the science of improvement does not presently exist that is grounded in the philosophical and theoretical basis of the field. The 7 propositions outlined here demonstrate the value of examining the underpinnings of improvement. This is needed to both advance the field and minimize confusion about what the phrase science of improvement represents. We argue that advanced scientists of improvement are those who like Deming and Shewhart can integrate ideas, concepts, and models between scientific disciplines for the purpose of developing more robust improvement models, tools, and techniques with a focus on application and problem solving in real world contexts. CONCLUSIONS: The epistemological foundations and theoretical basis of the science of improvement and its reasoning methods need to be critically examined to ensure its continued development and relevance. If improvement efforts and projects in health care are to be characterized under the canon of science, then health care professionals engaged in quality improvement work would benefit from a standard set of core principles, a standard lexicon, and an understanding of the evolution of the science of improvement