Understanding Qualitative Inquiry (QI) unfolds in five sections. To understand qualitative inquiry today, you need to know its origins. Section 1 includes four chapters, opening with the editor’s own account of the evolution of qualitative inquiry over the past five decades. The other chapters explore teaching qualitative research and the philosophical foundations that support it, one of the most powerful forces shaping the field today is technology. The chapters of Section 2, “The Digital Revolution”, include a comprehensive overview of AI, a deep reflection about listening and slowing down, and a review of data and the digital age. One important shift in qualitative inquiry over the decades has been shifting roles of the self. Section 3 deals with the reflective self; as researchers, we can spend days and months designing and doing a study, but what goes out to the larger community is what others see or hear. Section 4, “Telling the Story”, provides examples of different ways to convey information. Finally, Section 5 includes speculations, thoughts, and ideas about the future.
Types of projects suitable for clinical inquiry run the gamut from improvement science (quality improvement [QI], continuous QI, or process improvement) to research, with its many forms and designs, to implementation science (the study of how and how well we adopt existing evidence in practice settings).1 Any of these projects can be used for evidence-based practice (EBP), which involves not only consideration of existing evidence (research), but also consideration of patient-specific characteristics, situations, and preferences.2This issue’s Clinical Inquiry column provides investigators with tools to make clinical inquiry in the practice setting more feasible and attainable. These tools include clarifying terminology and providing insights into how to distinguish which type of project is best suited to the clinical question at hand. Because all clinical inquiry evolves or derives from patient care encounters, we strive to provide tools that simplify and clarify the pursuit of better, more evidence-based, higher quality care for the patients whom we serve.Two broad types of clinical inquiry projects—improvement science and implementation science—have become semantically entangled. Although distinct, these types of projects have characteristics that can blur their differences and unique attributes. One must distinguish between the 2 sciences to make clear the purpose of a study. The goal for us as nurses and as scientists is to avoid lingering between improvement and implementation. We strive to avoid succeeding in QI initiatives while failing to implement these initiatives or, even worse, failing to study the effects of the implementation once an improvement has been achieved. The purpose of this article is to distinguish between improvement science and implementation science and to define the opportunities for using these 2 approaches in clinical inquiry.Improvement science, by definition, is what many of us traditionally have referred to as QI.3,4 However, the concept of improvement science has expanded and emerged as a broader scientific framework, providing a more formalized structure that is focused on evaluation of processes of care inherent to health care improvement.5,6 The primary goal of clinical questions in this scientific field is to determine which improvement strategies work best to improve processes associated with the safety, timeliness, efficiency, effectiveness, equity, and patient-centered health care delivery.7,8The conceptual framework that guides improvement science as a unique discipline was born in the automobile industry9 and has gained momentum in health care as a result of the Institute of Medicine call to improve health care quality and safety.7,10 Organizations such as the Institute for Health-care Improvement, the Improvement Science Research Network, and, in the United Kingdom, the Health Foundation Improvement Science Initiative11 have emerged to establish theoretical underpinnings for QI approaches across a wide range of patient care settings and health care disciplines. The core conceptual components of improvement science focus on answering 3 key questions that undergird high-quality process evaluations12: (1) What are we trying to accomplish? (2) How will we know a change is an improvement? (3) What changes can we make that will result in an improvement?Common models for designing and conducting improvement science have been presented previously in this column, including the Lean Six Sigma model13 and the DMAIC (define, measure, analyze, improve, control) model.14 Another approach, the PDSA (plan, do, study, act) model,15 uses repeated cycles of planning through problem identification, doing an active intervention to change processes associated with the problem, studying data associated with the process change, and acting on the data to address what was learned in the process change. Applying these core concepts in cycles of evaluation, the PDSA model addresses the effects systematically and iteratively of changing a process of care. Ultimately, these cycles lead the improvement science team to identify low-performing areas in the care delivery chain and adopt strategies to improve health care system processes and the associated quality of care delivery outcomes.Terminology used in improvement science to describe methods and results draws on terminology used in different disciplines.16 Examples include the names used for evaluation approaches, data types, data displays, and results-reporting nomenclature. To help standardize the QI language and eliminate confusion, guidelines for standardized approaches and terminology used in improvement science have been developed.17 These guidelines are part of an ongoing effort to standardize the terminology, content, and style used in conducting and reporting results for improvement science.Implementation science is the study of methods to promote the adoption and integration of EBPs, interventions, and policies into routine health care and health delivery settings.1 Different from improvement science, the primary goal of clinical questions in implementation science is to create generalizable knowledge through study designs that are powered to show both a clinical and statistical difference in the approach or intervention being tested.18,19 The aim in implementation science is to answer key clinical questions that pertain to the effectiveness of adopting existing evidence, such as the following: (1) What strategies for improving care delivery processes are associated with reliable, reproducible, and sustainable change in patient outcomes? (2) What are the barriers to and facilitators of evidence adoption, and what are generalizable solutions to address those barriers? (3) What are the innovative theories, methods, and measures that must be developed to more efficiently and effectively test how evidence is adopted in practice?Because implementation science uses statistically powered research designs intended to create generalizable knowledge about barriers to and facilitators of adoption, the design and data collection processes are similar to traditional research and will likely require informed consent and full or expedited institutional review board review, rather than exemption.4 Participants must be aware of and agree to their role in the implementation research study, because everyone will not receive the intervention, or at least not in the same way. However, because hospital patient care units or individual clinical sites are typically the target of the intervention, and the EBP change typically is implemented at the hospital level, the whole hospital becomes the “unit of randomization,” rather than individual patients. As a result, if all patients in a clinical setting will be participating in the same way, the consent may be approved for the hospital level rather than the individual patient level.3 A detailed example of this design has been described previously in a series of articles about blending QI and research methods for implementation science.20,21Like research designs in general, the scope of implementation science designs is broad. Additional designs used for implementation science studies include qualitative and quantitative data types and research methods and can include a range of approaches to research that are more easily adopted in clinical practice settings. Examples include pragmatic trials, participatory action research, effectiveness-implementation hybrid studies, and randomized designs that evaluate approaches to QI as the intervention of interest in the implementation study.Data collection in implementation science is designed to accommodate the research protocol and is different from the data collected in improvement science or QI. The data collected in implementation science that are associated with the primary endpoint are not reviewed cyclically at iterative points throughout the study at the site. Rather, the study is designed to result in the ability to infer causal relationships and solutions to the questions posed, and therefore the effectiveness of the approaches being tested are not analyzed until the end. Another key point to remember is the focus on organizational behavior, including a focus on ourselves as providers and health professionals. Therefore, data collection must be designed in a way to capture how we practice, how we make decisions regarding treatment options, and how we include and engage patients in those decisions. Health care providers and health systems are the study subjects. However, unlike improvement science, which occurs in local, often single-unit settings, implementation science is designed using multiple units, hospitals, or health systems to generate enough strength in study design to allow for demonstration of causality.The definitions of research approaches such as translational research, EBP science, knowledge translation, research utilization, improvement science, and implementation research are best contrasted rather than compared (Table 1). However, a common thread in these approaches is the desire to apply research findings in routine care and adopt the scientific evidence in clinically relevant and practical ways to improve patient care outcomes. Although some components of study design may be similar across these fields, the conceptual frame of reference for improvement science is the scope of scientific study that addresses which processes work best to achieve the goals of safety, timeliness, efficiency, effectiveness, equity, and patient-centeredness in the complex systems of health care delivery. By contrast, the conceptual frame of reference for implementation science is the study of how and how well systems adopt existing evidence into practice.While the 2 foci cross paths and share a common goal to identify factors associated with adoption of a practice or guideline, improvement science emphasizes local, short cycles of observed, iterative evaluation, whereas implementation science uses multiple sites and research designs statistically powered to establish a causal relationship. Used together, these approaches can be integrated to more effectively adapt new knowledge to specific practice environments.22 For example, as described in this column by Pokorney and colleagues21 in 2015, an implementation science study may “blend” research and improvement science (ie, QI) methods to determine what factors are significantly associated with adoption of guideline-based anticoagulation therapy in patients with atrial fibrillation. In this example, multiple sites were randomized to a standard protocol (a QI intervention to improve the use of guideline-based therapies at local hospitals at discharge) to determine site-based factors that predict more effective adoption of proven stroke prevention protocols.Priorities for future study for both improvement science and implementation science have been identified, showing support for integration of the 2 initiatives. As reported by the Improvement Science Research Network5 and the National Institutes of Health, National Heart, Lung, and Blood Institute (NHLBI),19 future study priorities target important gaps in health care delivery and offer opportunities for sequencing, blending, and triangulating methods as a means of achieving common goals (Table 2).As clinical inquiry moves into the next decade, study designs will be increasingly pragmatic, including more observational data and qualitative methods to “round out” and further inform understanding of the causal relationships identified in traditional randomized study designs. Our challenge in the clinical setting is to remain engaged in the increasingly complex work of “research at the bedside” to improve both processes and outcomes of care for the patients whom we serve.
This commentary reconsiders emerging standards for evaluation of the field of practice of qualitative inquiry and arts-based approaches. Specifically, this is a review of the dialogue around issues of standards that has continued through the first seven volumes of Qualitative Inquiry ( QI), from March 1995 through June 2001, and is inclusive of this special issue of QI devoted to arts-based inquiry. What has emerged in QI is an action-oriented worldview among qualitative researchers who value inquiry for its usefulness within the community where it originates. In this way, QI has contributed greatly to the construction of still emerging practices within a newly formed tradition of participatory, critical action research based on an ethics of human relationships. Arts-based inquiry is one aspect of this emerging tradition.
BACKGROUND: There is growing recognition in the literature of the 'Herculean' efforts required to bring about change in healthcare processes and systems. Leadership is recognised as a critical lever for implementation of quality improvement (QI) and other complex team-level interventions; however, the processes by which leaders facilitate change are not well understood. The aim of this study is to examine 'how' leadership influences implementation of QI interventions. METHODS: We drew on the leadership literature and used secondary data collected as part of a process evaluation of the Safer Care for Older Persons in residential Environments (SCOPE) QI intervention to gain insights regarding the processes by which leadership influences QI implementation. Specifically, using detailed process evaluation data from 31 unit-based nursing home teams we conducted a thematic analysis with a codebook developed a priori based on the existing literature to identify leadership processes. RESULTS: Effective leaders (ie, those who care teams felt supported by and who facilitated SCOPE implementation) successfully developed and reaffirmed teams' commitment to the SCOPE QI intervention (theme 1), facilitated learning capacity by fostering follower participation in SCOPE and empowering care aides to step into team leadership roles (theme 2) and actively supported team-oriented processes where they developed and nurtured relationships with their followers and supported them as they navigated relationships with other staff (theme 3). Together, these were the mechanisms by which care aides were brought on board with the intervention, stayed on board and, ultimately, transplanted the intervention into the facility. Building learning capacity and creating a culture of improvement are thought to be the overarching processes by which leadership facilitates implementation of complex interventions like SCOPE. CONCLUSIONS: Results highlight important, often overlooked, relational and sociocultural aspects of successful QI leadership in nursing homes that can guide the design, implementation and scaling of complex interventions and can guide future research.
Background and Aim: Qualitative research plays an important role in improving nursing knowledge. Understanding the concept of saturation is essential to conducting rigorous qualitative research that contributes to evidence-based practice. The purpose of this study is to clarify the concept of saturation in qualitative research. Method: Evolutionary concept analysis was performed. A literature search was conducted using a variety of online databases for the years 2005- 2023. In total, 33 articles and books were analyzed using thematic analysis to identify the attributes, antecedents and consequences of saturation. The validity of the data was obtained by examining the analysis process by two independent researchers. Results: Saturation in qualitative research is a context-dependent, subjective process that requires detailed systematic analysis. Saturation is used in four ways in qualitative research: theoretical saturation, data saturation, code or thematic saturation, and meaning saturation. The antecedents of saturation were classified into two categories: study related factors and researcher related factors. The consequences of saturation were identified as: ensuring credibility and quality in qualitative research and time, energy and budget saving. Conclusion: This concept analysis serves to enhance the understanding of the concept of saturation, thereby offering valuable resources for qualitative researchers. By gaining a profound comprehension of saturation and its various types, researchers can ensure the validity of their studies while also optimizing time and resource allocation by avoiding redundant data collection. Future investigation warranted to elucidate how factors associated with reaching saturation impact estimations sample size.
Qualitative Inquiry in TESOL by Keith Richards is a book that is described by many as far from weaknesses of having "narratives that are too frequently disrupted by heavily-jargoned lists and descriptions" (Tafaghodtari 2009: 272). Qualitative Inquiry in TESOL is a thoughtful insight into qualitative inquiry (QI). It offers definitions of QI and differentiates it from other types of research. It also offers detailed descriptions of the interview and observation process, as well as the analysis of spoken interaction.
This paper is one of a series exploring the implications of complexity for systematic reviews and guideline development, commissioned by the WHO. The paper specifically explores the role of qualitative evidence synthesis. Qualitative evidence synthesis is the broad term for the group of methods used to undertake systematic reviews of qualitative research evidence. As an approach, qualitative evidence synthesis is increasingly recognised as having a key role to play in addressing questions relating to intervention or system complexity, and guideline development processes. This is due to the unique role qualitative research can play in establishing the relative importance of outcomes, the acceptability, fidelity and reach of interventions, their feasibility in different settings and potential consequences on equity across populations. This paper outlines the purpose of qualitative evidence synthesis, provides detail of how qualitative evidence syntheses can help establish understanding and explanation of the complexity that can occur in relation to both interventions and systems, and how qualitative evidence syntheses can contribute to evidence to decision frameworks. It provides guidance for the choice of qualitative evidence synthesis methods in the context of guideline development for complex interventions, giving 'real life' examples of where this has occurred. Information to support decision-making around choice qualitative evidence synthesis methods in the context of guideline development is provided. Approaches for reporting qualitative evidence syntheses are discussed alongside mechanisms for assessing confidence in the findings of a review.
We share about an introductory qualitative inquiry (QI) class when we asked students to bring their “thinking with theory” assignment paper and then invited them to think with theory-tissue paper-scissors-discarded library books-writing from literature reviews-transcripts of interviews-fieldnotes as a way to produce newness. This assignment was conceived as the result of our own theoretically informed approach(es) to pedagogy; it was not enough for us to read about post-theories or even to put them to work in our own inquiries, but we also had to find ways to live them out in our pedagogies. In this way, this assignment became one way for our students not only to read about QI, but also be/do qualitative inquiry(ers)—differently—as they came to know/be(come)/do. We focus this article on one student, Gigi, as she created and shared during that night in class and participated later in a follow-up focus group session. We end the article with several questions for other instructors of QI to consider in relation to their pedagogies and what they produce.
OBJECTIVES: Health systems must rapidly move knowledge into practice to address disparities impacting sexual and gender minority (SGM) patients. This qualitative study explores barriers and facilitators that arose during an initiative to improve care for SGM patients in federally qualified health centres (FQHCs) from the perspectives of FQHC staff. DESIGN: Cross-sectional qualitative content analysis, using a general inductive approach, of secondary data from transcripts of intervention events offered to FQHC staff and semistructured interviews with staff and FQHC leadership during the intervention. SETTING: 10 FQHCs from nine states in the USA. PARTICIPANTS: FQHC quality improvement (QI) and clinical care staff, and leaders at each FQHC. INTERVENTIONS: The transforming care for lesbian, gay, bisexual and transgender people QI initiative combined two evidence-based programmes, Learning Collaborative (LC) and Project Extension for Community Healthcare Outcomes (ECHO), to assist primary care health centres in developing capacity to identify SGM patients, monitor their health and care, and improve disparities. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was identification of barriers and facilitators to implementing initiatives to improve care for SGM patients. The secondary outcome was clarification of how intervention participants used Project ECHO sessions versus LC meetings to obtain information that influenced implementation of the initiative at their FQHC. RESULTS: Barriers and facilitators mapped to two major themes: 'Clinical' (patients' health, wellness, and available treatment) and Health Systems and Institutional Culture (FQHC operations, and customs and social institutions within the FQHCs and in the external environment). Common 'Clinical' inquiries were for assistance with behavioural health, pre-exposure prophylaxis and transgender hormone therapy. Prevalent facilitators included workflow change and staff training, while adapting electronic health records for data collection, decision support and data extraction was the most prevalent barrier. CONCLUSIONS: Project ECHO and LC provided complimentary forums to explore clinical and operational changes needed to improve care for SGM at FQHCs.
This article adopts a constructivist grounded theory approach based on the principle of intersubjective relations and the co-construction of interpretations. Reflecting on the author's experiences as a tutor, supervisor, examiner, and reviewer to demystify qualitative data analysis (QDA), this article emphasizes member checking with participants to confirm the researchers' interpretations and collaboratively constructed meanings, addressing reflexivity, peer debriefing, and triangulation. QDA is framed as an iterative, dynamic process of extracting meaning from diverse data forms (field text, narrative, voice, reflective note, text, audio, and video). The procedures of data analysis and/or writing as a process of inquiry, such as data immersion, initial impressions, codes, categories, and theme developments, are explored across methods or methodologies, including autoethnography, participatory action research (PAR), narrative inquiry, grounded theory, phenomenology, ethnography, case study, and other alternative research methods or methodologies, with data saturation as the final stepping point when no new data and/or insights from field text, narrative, voice, reflective note, text, audio, or video are extracted. Challenges like generating an intersubjective construction of meaning with research participants and achieving data saturation are addressed through methods such as reflexivity, peer debriefing, member checking and triangulation. This article provides a practical guide for scholars on data analysis, incorporating reflections, procedures, and some points for consideration to ensure rigor and meaningful analysis.
BACKGROUND: A greater understanding of the factors that influence long-term sustainment of quality improvement (QI) initiatives is needed to promote organizational ability to sustain QI practices over time, help improve future interventions, and increase the value of QI investments. METHODS: We approached 83 of 201 executive sponsors or change leaders at addiction treatment organizations that participated in the 2007-2009 NIATx200 QI intervention. We completed semi-structured interviews with 33 individuals between November 2015 and April 2016. NIATx200 goals were to decrease wait time, increase admissions and improve retention in treatment. Interviews sought to understand factors that either facilitated or impeded long-term sustainment of organizational QI practices made during the intervention. We used thematic analysis to organize the data and group patterns of responses. We assessed available quantitative outcome data and intervention engagement data to corroborate qualitative results. RESULTS: We used narrative analysis to group four important themes related to long-term sustainment of QI practices: (1) finding alignment between business- and client-centered practices; (2) staff engagement early in QI process added legitimacy which facilitated sustainment; (3) commitment to integrating data into monitoring practices and the identification of a data champion; and (4) adequate organizational human resources devoted to sustainment. We found four corollary factors among agencies which did not sustain practices: (1) lack of evidence of impact on business practices led to discontinuation; (2) disengaged staff and lack of organizational capacity during implementation period led to lack of sustainment; (3) no data integration into overall business practices and no identified data champion; and (4) high staff turnover. In addition, we found that many agencies' current use of NIATx methods and tools suggested a legacy effect that might improve quality elsewhere, even absent overall sustainment of original study outcome goals. Available quantitative data on wait-time reduction demonstrated general concordance between agency perceptions of, and evidence for, sustainment 2 years following the end of the intervention. Additional quantitative data suggested that greater engagement during the intervention period showed some association with sustainment. CONCLUSIONS: Factors identified in QI frameworks as important for short-term sustainment-organizational capacity (e.g. staffing and leadership) and intervention characteristics (e.g. flexibility and fit)-are also important to long-term sustainment.
My doctoral study was focussed on women’s activities and practices in community organizations. In this paper, I revisit “the hug,” a fieldwork event (understood in the Deleuzian sense as something—a situation or a problem—that provokes thought) recreating it as a pedagogical event in relation to emerging Deleuzo–Guattarian and new materialist qualitative (QI) pedagogies. The effect of the hug event demonstrates the potential productivity of emerging QI pedagogies, and implications for what intra-active QI pedagogies might produce for researchers, and the research community at large.
<p id=C6>With the methodological changes in psychology, more and more researchers tend to accept qualitative research as an effective way to solve psychological problems and serve the public. In a qualitative study, sufficient sample is the guarantee of research validity, and saturation is an indicator used to assess the adequacy of research data. Saturation means that on the basis of the currently collected and analyzed data, further data collection will not help researchers develop a deeper understanding of the story or theory, so there is no need to continue to collect data. The concept of theoretical saturation was first proposed in grounded theory. Then with the development of qualitative research methods, researchers have further created more saturation concepts, including data saturation, code or thematic saturation, meaning saturation, etc. Due to the diversity of saturation and its judgment standards, the relationship between different kinds of saturation are complicated and ambiguous. In addition, previous studies lack operational description and practical guidance for the evaluation of saturation, which leads to the vagueness of the concept of saturation and many difficulties in evaluation. In order to solve these problems, this study clarified the concepts and evaluation methods of four levels of saturation, and provided suggestions for researchers operations based on comparison and analysis. The four types of saturation occur at different stages of the research process, and each has its own specific connotations. Data saturation, code or thematic saturation focuses on the breadth of collected data, while meaning saturation and theoretical saturation focus on the depth of research data. In terms of evaluation methods and criteria, researchers usually judge data saturation based on the repeatability of initial data; code or thematic saturation is determined based on empirical research results, the emergence of new codes or themes, or saturation coefficients; the results of retrospective empirical analysis or tables of meaning unit are normally used to evaluate meaning saturation; while the assessment of theoretical saturation relies on a process called continuous comparison in grounded theory, which focuses on the continuous improvement of the theory. Some problems are discussed in this study. 1) The sample size standard for reaching saturation should be embedded in the specific research process instead of being uniformly set in advance. Because each study has its own uniqueness in terms of questions, purposes, methods, etc., which saturation is extremely sensitive to, the evaluation of saturation should be based on the characteristics of the current research to select an appropriate level of saturation. 2) Due to the logical uncertainty of saturation, a little oversampling would be helpful. The logical uncertainty here means that researchers can only predict the necessity of continuing data collection based on the information that has been collected, which relies on the subjective judgment of researchers, and its accuracy can never be further proved. Oversampling may be an effective way to solve this problem, which means that even if saturation has been achieved, the researcher is recommended to add 2 to 3 personal interviews or 1 to 2 focus group interviews to further confirm. 3) As an important index to evaluate the quality of qualitative research, saturation is not suitable for all qualitative research, such as psychobiography, narrative analysis, etc., which focus on single or a few cases and pay more attention to the integrity of individual stories. In the future, researchers should further focus on the evaluation and testing of saturation in different kinds of qualitative research.
BACKGROUND: Health care has experimented with many different quality improvement (QI) approaches with greater variation in name than content. This has been dubbed pseudoinnovation. However, it could also be that the subtleties and differences are not clearly understood. To explore this further, the purpose of this study was to explore how hospital managers perceive lean in the context of QI. METHODS: We used a qualitative study design with semi-structured interviews to explore twelve top managers' perceptions of the relationship between lean and quality improvement (QI) at a university-affiliated hospital. RESULTS: Managers described that QI and lean shared the same overall purpose: focus on patient needs and improve efficiency and effectiveness. Employee involvement was emphasized in both strategies, as well as the support offered by managers of staff initiatives. QI was perceived as a strategy that could support structural changes at the organizational level whereas lean was seen as applicable at the operational level. Moreover, lean carried a negative connotation, lacked the credibility of QI, and was perceived as a management fad. CONCLUSIONS: Aspects of QI and lean were misunderstood. In a context where lean remains an abstract term, and staff associate lean with automotive applications and cost reduction, it may be fruitful for managers to invest time and resources to develop a strategy for continual improvement and utilize vocabulary that resonates with health care staff. This could reduce the risk that improvement efforts are rejected out of hand.
INTRODUCTION: Inadequate and varied quality of care in care homes has led to a proliferation of quality improvement (QI) projects. This study examined the sustainability of interventions initiated by such projects. METHOD: This qualitative study explored the sustainability of seven interventions initiated by three QI projects between 2016 and 2018 in UK care homes and explored the perceived influences to the sustainability of interventions. QI projects were followed up in 2019. Staff leading QI projects (n=9) and care home (n=21, from 13 care homes) and healthcare (n=2) staff took part in semi-structured interviews. Interventions were classified as sustained if the intervention was continued at the point of the study. Thematic analysis of interview data was performed, drawing on the Consolidated Framework for Sustainability (CFS), a 40-construct model of sustainability of interventions. RESULTS: Three interventions were sustained and four interventions were not. Seven themes described perceptions around what influenced sustainability: monitoring outcomes and regular check-in; access to replacement intervention materials; staff willingness to dedicate time and effort towards interventions; continuity of staff and thorough handover/inductions in place for new staff; ongoing communication and awareness raising; perceived effectiveness; and addressing care home priorities. All study themes fell within 18 of the 40 CFS constructs. DISCUSSION: Our findings resonate with the CFS and are also consistent with implementation theories, suggesting sustainability is best addressed during implementation rather than treated as a separate process which follows implementation. Commissioning and funding QI projects should address these considerations early on, during implementation.
BACKGROUND: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. METHODS: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. RESULTS: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained. CONCLUSIONS: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.
The issue of ‘quality’ in qualitative research is part of a much larger and contested debate about the nature of the knowledge produced by qualitative research, whether its quality can legitimately be judged according to a single set of general principles and, if so, how. In the field of qualitative research, concern to be able to assess quality has manifested itself in the proliferation of guidelines for doing and judging qualitative work, particularly in the health field. This chapter outlines two views of how qualitative methods might be judged. It then argues that qualitative research can be assessed with reference to the same broad criteria of quality as quantitative research, albeit the meaning attributed to these criteria may not be exactly the same and they may be assessed differently. The chapter concludes with a list of questions that can be used as a guide to assessing the quality of a piece of qualitative research.
The notion of zone implies dynamic processes—exchange, transaction, transformation, and intensity, socially and historically situated. The QI Congress provides such a zone in which the traditional role of the researcher as a lone, isolated figure working independently can be reimagined as dialogic and collaborative. In this special issue, the intersection of the arts and qualitative research is explored as providing a common structure for researchers from multiple disciplines to examine a new social phenomenon, the affordances and problematic of arts-based research (ABR), arts as a form of spirituality across cultures, arts as generating data collection tools toward integration among disciplines, and the use of artistic and kinesthetic sensibilities beyond text to promote a dialogue between artists and researchers. The arts, which traditionally have been marginalized in academic discourses, are embraced by the QI Congress, allowing for new interpretive zones of understanding through performative practices.
BACKGROUND & OBJECTIVES: The comparative uses of different types of patient experience (PE) feedback as data within quality improvement (QI) are poorly understood. This paper reviews what types are currently available and categorizes them by their characteristics in order to better understand their roles in QI. METHODS: A scoping review of types of feedback currently available to hospital staff in the UK was undertaken. This comprised academic database searches for "measures of PE outcomes" (2000-2016), and grey literature and websites for all types of "PE feedback" potentially available (2005-2016). Through an iterative consensus process, we developed a list of characteristics and used this to present categories of similar types. MAIN RESULTS: The scoping review returned 37 feedback types. A list of 12 characteristics was developed and applied, enabling identification of 4 categories that help understand potential use within QI-(1) Hospital-initiated (validated) quantitative surveys: for example the NHS Adult Inpatient Survey; (2) Patient-initiated qualitative feedback: for example complaints or twitter comments; (3) Hospital-initiated qualitative feedback: for example Experience Based Co-Design; (4) Other: for example Friends & Family Test. Of those routinely collected, few elicit "ready-to-use" data and those that do elicit data most suitable for measuring accountability, not for informing ward-based improvement. Guidance does exist for linking collection of feedback to QI for some feedback types in Category 3 but these types are not routinely used. CONCLUSION: If feedback is to be used more frequently within QI, more attention must be paid to obtaining and making available the most appropriate types.
BACKGROUND: Multiorganisational quality improvement (QI) collaborative networks are promoted for improving quality within healthcare. Recently, several large-scale QI initiatives have been conducted in the intensive care unit (ICU) environment with successful quantitative results. However, the mechanisms through which such networks lead to QI success remain uncertain. We aim to understand ICU staff perspectives on collaborative QI based on involvement in a multiorganisational improvement network and hypothesise about theoretical constructs that might explain the effect of collaboration in such networks. METHODS: Qualitative study using a modified grounded theory approach. Key informant interviews were conducted with staff from 12 community hospital ICUs that participated in a cluster randomized control trial (RCT) of a QI intervention using a collaborative approach between 2006 and 2008. Data analysis followed the standard procedure for grounded theory using constant comparative methodology. RESULTS: The collaborative network was perceived to promote increased intrateam cooperation over interorganisational cooperation, but friendly competition with other ICUs appeared to be a prominent driver of behaviour change. Bedsides, clinicians reported that belonging to a collaborative network provided recognition for the high-quality patient care that they already provided. However, the existing communication structure was perceived to be ineffective for staff engagement since it was based on a hierarchical approach to knowledge transfer and project awareness. CONCLUSIONS: QI collaborative networks may promote behaviour change by improving intrateam communication, fostering competition with other institutions, and increasing recognition for providing high-quality care. Other commonly held assumptions about their potential impact, for instance, increasing interorganisational legitimisation, communication and collaboration, may be less important.