The epistemology of quality improvement: it's all Greek

In Plato's Theaetetus, knowledge is defined as the intersection of truth and belief, where knowledge cannot be claimed if something is true but not believed or believed but not true. Using an example from neonatal intensive care, this paper adapts Plato's definition of the concept ‘knowledge’ and applies it to the field of quality improvement in order to explore and understand where current tensions may lie for both practitioners and decision makers. To increase the uptake of effective interventions, not only does there need to be scientific evidence, there also needs to be an understanding of how people's beliefs are changed in order to increase adoption more rapidly. Understanding how best to maximise the overlap between actual and best practice is where quality improvement needs to employ educational and social sciences' methodologies and techniques

Can our Conception of the Nature of Science be Tentative Without Qualification?

The tentative and revisionary character of scientific knowledge is believed to play a central role in nature of science (NOS) studies by teachers, researchers, and curriculum developers. However, some educational researchers and scholars have recently expressed serious concerns about the view of tentativeness and change espoused in the science education literature claiming that it is simplistic, one-dimensional, inconsistent, irresponsibly vague, and self-contradictory. Despite these concerns, there are few detailed examples of how these types of problems manifest themselves in the science education literature and the difficulties they might pose for learners and other researchers. Accordingly, this article isolates and critically examines a single foundational proposition about the tentativeness of science made by leading NOS researchers. It is demonstrated that this generalization has some important inconsistencies and limitations that are problematic at the philosophical and instructional level. Throughout the article, it is argued that a logico-linguistic analysis of epistemic propositions made by researchers is desirable in NOS studies and that seemingly benign propositions can give rise to different viable, yet diametrically opposed, interpretive frameworks

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

Theory Creation, Modification, and Testing: An Information Processing Model and Theory of the Anticipated and Unanticipated Consequences of Research and Development

Background: Extending Merton’s (1936) work on the consequences of purposive social action, the model, theory and taxonomy outlined here incorporates and formalizes both anticipated and unanticipated research findings in a unified theoretical framework. The model of anticipated research findings was developed initially by Carifio (1975, 1977) and was followed by the addition of the unanticipated findings component by Perla (2006). This is the first formal model, theory and synthesis of anticipated and unanticipated research findings developed to date. The wide-ranging consequences and implications of the model are discussed. Purpose:  To the extent that educational researchers, philosophers and scholars reduce unanticipated findings solely to chance, whimsy, or inspiration they declare these occurrences impossible to effectively predict, model or understand.  This article provides a way to conceptualize and formally model anticipated and unanticipated research findings.  Many concrete examples from the history of science and research and evaluation methodology are provided to illustrate the model as well as various details of an application of the model in developing instructional materials. Setting: Nature of science instructional materials development effort. Intervention: Not applicable. Research Design: Theory and model development using quantitative and qualitative methods including literature review and original model evaluations. Data Collection and Analysis: Content analysis and modified Q-sorts of research related documents, journals, logs, literature and emails as well as various theory construction and modification techniques. Findings: This article demonstrates that a formal model and theory of anticipated and unanticipated research findings can be developed and that such models should inform a broad range of research and evaluation efforts that are conducted daily worldwide.  Nine key research and evaluation principles were derived to supplement the formal model and its operations that should be helpful to novice as well as experienced researchers regardless of the research methodology or strategies they are employing.Keywords: theory construction; discovery; research methodology; information processing; program evaluation; serendipity; nature of science; instructional materials &nbsp

Theory Creation, Modification, and Testing: An Information Processing Model and Theory of the Anticipated and Unanticipated Consequences of Research and Development

Background: Extending Merton’s (1936) work on the consequences of purposive social action, the model, theory and taxonomy outlined here incorporates and formalizes both anticipated and unanticipated research findings in a unified theoretical framework. The model of anticipated research findings was developed initially by Carifio (1975, 1977) and was followed by the addition of the unanticipated findings component by Perla (2006). This is the first formal model, theory and synthesis of anticipated and unanticipated research findings developed to date. The wide-ranging consequences and implications of the model are discussed. Purpose:  To the extent that educational researchers, philosophers and scholars reduce unanticipated findings solely to chance, whimsy, or inspiration they declare these occurrences impossible to effectively predict, model or understand.  This article provides a way to conceptualize and formally model anticipated and unanticipated research findings.  Many concrete examples from the history of science and research and evaluation methodology are provided to illustrate the model as well as various details of an application of the model in developing instructional materials. Setting: Nature of science instructional materials development effort. Intervention: Not applicable. Research Design: Theory and model development using quantitative and qualitative methods including literature review and original model evaluations. Data Collection and Analysis: Content analysis and modified Q-sorts of research related documents, journals, logs, literature and emails as well as various theory construction and modification techniques. Findings: This article demonstrates that a formal model and theory of anticipated and unanticipated research findings can be developed and that such models should inform a broad range of research and evaluation efforts that are conducted daily worldwide.  Nine key research and evaluation principles were derived to supplement the formal model and its operations that should be helpful to novice as well as experienced researchers regardless of the research methodology or strategies they are employing.Keywords: theory construction; discovery; research methodology; information processing; program evaluation; serendipity; nature of science; instructional materials &nbsp

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

Large-scale improvement initiatives in healthcare: a scan of the literature

CONTEXT: The goal of this article is to provide a succinct scan of the literature as it relates to the current thinking and practice in large-scale improvement initiatives in healthcare. METHOD: We employed a scan of the literature using a modified Delphi technique. A standard review form was used. The scan was limited to large-scale spread efforts in hospitals and healthcare systems. Each of the main factors that emerged during the scan was linked to secondary factors and organized using a driver diagram. FINDINGS: Four primary drivers (factors) emerged during our scan that inform large-scale change initiatives in healthcare: Planning and Infrastructure; Individual, Group, Organizational, and System Factors; The Process of Change; and Performance Measures and Evaluation. CONCLUSION: Our scan identified a tremendous amount of work being done around the world to improve healthcare. In general, our findings suggest these initiatives tend to be fragmented from an implementation standpoint. We identified primary and secondary drivers (factors) that can be used by those responsible for implementing large-scale improvement initiatives both at a strategy level and in their daily work. These drivers could serve as a checklist of ideas to consider in different testing and implementation situations