What counts as good evidence

Making better use of evidence is essential if public services are to deliver more for less. Central to this challenge is the need for a clearer understanding about standards of evidence that can be applied to the research informing social policy. This paper reviews the extent to which it is possible to reach a workable consensus on ways of identifying and labelling evidence. It does this by exploring the efforts made to date and the debates that have ensued. Throughout, the focus is on evidence that is underpinned by research, rather than other sources of evidence such as expert opinion or stakeholder views.Publisher PD

How do frontline staff use patient experience data for service improvement? Findings from an ethnographic case study evaluation

Funding Information: The authors would like to thank the following: the ward teams and senior management teams at the six participating case study sites. Neil Churchill, Angela Coulter, Ray Fitzpatrick, Crispin Jenkinson, Trish Greenhalgh and Sian Rees who were co-investigators on the study, contributing to the original design and conduct of the study. Esther Ainley and Steve Sizmur from Picker Institute Europe, who contributed to data collection and analysis. Prof. John Gabbay and Prof. Andr? le May, University of Southampton, for facilitating the learning community meetings. The members of the lay advisory panel: Barbara Bass, Tina Lonhgurst, Georgina McMasters, Carol Munt, Gillian Richards, Tracey Richards, Gordon Sturmey, Karen Swaffield, Ann Tomlime and Paul Whitehouse. The external members of the Study Steering Committee: Joanna Foster, Tony Berendt, Caroline Shuldham, Joanna Goodrich, Leigh Kendall, Bernard Gudgin and Manoj Mistry. At the time of conducting the research LL and SP were employed by the University of Oxford. Preliminary findings from the study have been presented publicly at the following conferences: European Association for Communication in Healthcare 2016; The International Society for Quality in Healthcare 2017; Health Services Research UK 2017; Medical Sociology 2018. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Publisher Copyright: © The Author(s) 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

Understanding how front-line staff use patient experience data for service improvement: an exploratory case study evaluation

Background and aim: The NHS collects a large number of data on patient experience, but there are concerns that it does not use this information to improve care. This study explored whether or not and how front-line staff use patient experience data for service improvement. Methods: Phase 1 – secondary analysis of existing national survey data, and a new survey of NHS trust patient experience leads. Phase 2 – case studies in six medical wards using ethnographic observations and interviews. A baseline and a follow-up patient experience survey were conducted on each ward, supplemented by in-depth interviews. Following an initial learning community to discuss approaches to learning from and improving patient experience, teams developed and implemented their own interventions. Emerging findings from the ethnographic research were shared formatively. Phase 3 – dissemination, including an online guide for NHS staff. Key findings: Phase 1 – an analysis of staff and inpatient survey results for all 153 acute trusts in England was undertaken, and 57 completed surveys were obtained from patient experience leads. The most commonly cited barrier to using patient experience data was a lack of staff time to examine the data (75%), followed by cost (35%), lack of staff interest/support (21%) and too many data (21%). Trusts were grouped in a matrix of high, medium and low performance across several indices to inform case study selection. Phase 2 – in every site, staff undertook quality improvement projects using a range of data sources. The number and scale of these varied, as did the extent to which they drew directly on patient experience data, and the extent of involvement of patients. Before-and-after surveys of patient experience showed little statistically significant change. Making sense of patient experience ‘data’ Staff were engaged in a process of sense-making from a range of formal and informal sources of intelligence. Survey data remain the most commonly recognised and used form of data. ‘Soft’ intelligence, such as patient stories, informal comments and daily ward experiences of staff, patients and family, also fed into staff’s improvement plans, but they and the wider organisation may not recognise these as ‘data’. Staff may lack confidence in using them for improvement. Staff could not always point to a specific source of patient experience ‘data’ that led to a particular project, and sometimes reported acting on what they felt they already knew needed changing. Staff experience as a route to improving patient experience Some sites focused on staff motivation and experience on the assumption that this would improve patient experience through indirect cultural and attitudinal change, and by making staff feel empowered and supported. Staff participants identified several potential interlinked mechanisms: (1) motivated staff provide better care, (2) staff who feel taken seriously are more likely to be motivated, (3) involvement in quality improvement is itself motivating and (4) improving patient experience can directly improve staff experience. ‘Team-based capital’ in NHS settings We propose ‘team-based capital’ in NHS settings as a key mechanism between the contexts in our case studies and observed outcomes. ‘Capital’ is the extent to which staff command varied practical, organisational and social resources that enable them to set agendas, drive process and implement change. These include not just material or economic resources, but also status, time, space, relational networks and influence. Teams involving a range of clinical and non-clinical staff from multiple disciplines and levels of seniority could assemble a greater range of capital; progress was generally greater when the team included individuals from the patient experience office. Phase 3 – an online guide for NHS staff was produced in collaboration with The Point of Care Foundation. Limitations: This was an ethnographic study of how and why NHS front-line staff do or do not use patient experience data for quality improvement. It was not designed to demonstrate whether particular types of patient experience data or quality improvement approaches are more effective than others. Future research: Developing and testing interventions focused specifically on staff but with patient experience as the outcome, with a health economics component. Studies focusing on the effect of team composition and diversity on the impact and scope of patient-centred quality improvement. Research into using unstructured feedback and soft intelligence

PET-CT Surveillance versus Neck Dissection in Advanced Head and Neck Cancer

BACKGROUND: The role of image-guided surveillance as compared with planned neck dissection in the treatment of patients with squamous-cell carcinoma of the head and neck who have advanced nodal disease (stage N2 or N3) and who have received chemoradiotherapy for primary treatment is a matter of debate. METHODS: In this prospective, randomized, controlled trial, we assessed the noninferiority of positron-emission tomography–computed tomography (PET-CT)–guided surveillance (performed 12 weeks after the end of chemoradiotherapy, with neck dissection performed only if PET-CT showed an incomplete or equivocal response) to planned neck dissection in patients with stage N2 or N3 disease. The primary end point was overall survival. RESULTS: From 2007 through 2012, we recruited 564 patients (282 patients in the planned-surgery group and 282 patients in the surveillance group) from 37 centers in the United Kingdom. Among these patients, 17% had nodal stage N2a disease and 61% had stage N2b disease. A total of 84% of the patients had oropharyngeal cancer, and 75% had tumor specimens that stained positive for the p16 protein, an indicator that human papillomavirus had a role in the causation of the cancer. The median follow-up was 36 months. PET-CT–guided surveillance resulted in fewer neck dissections than did planned dissection surgery (54 vs. 221); rates of surgical complications were similar in the two groups (42% and 38%, respectively). The 2-year overall survival rate was 84.9% (95% confidence interval [CI], 80.7 to 89.1) in the surveillance group and 81.5% (95% CI, 76.9 to 86.3) in the planned-surgery group. The hazard ratio for death slightly favored PET-CT–guided surveillance and indicated noninferiority (upper boundary of the 95% CI for the hazard ratio, <1.50; P=0.004). There was no significant difference between the groups with respect to p16 expression. Quality of life was similar in the two groups. PET-CT–guided surveillance, as compared with neck dissection, resulted in savings of £1,492 (approximately $2,190 in U.S. dollars) per person over the duration of the trial. CONCLUSIONS: Survival was similar among patients who underwent PET-CT–guided surveillance and those who underwent planned neck dissection, but surveillance resulted in considerably fewer operations and it was more cost-effective. (Funded by the National Institute for Health Research Health Technology Assessment Programme and Cancer Research UK; PET-NECK Current Controlled Trials number, ISRCTN13735240.

A study protocol for applying the co-creating knowledge translation framework to a population health study

BACKGROUND: Population health research can generate significant outcomes for communities, while Knowledge Translation (KT) aims to expressly maximize the outcomes of knowledge producing activity. Yet the two approaches are seldom explicitly combined as part of the research process. A population health study in Port Lincoln, South Australia offered the opportunity to develop and apply the co-KT Framework to the entire research process. This is a new framework to facilitate knowledge formation collaboratively between researchers and communities throughout a research to intervention implementation process. DESIGN: This study employs a five step framework (the co-KT Framework) that is formulated from engaged scholarship and action research principles. By following the steps a knowledge base will be cumulatively co-created with the study population that is useful to the research aims. Step 1 is the initiating of contact between the researcher and the study contexts, and the framing of the research issue, achieved through a systematic data collection tool. Step 2 refines the research issue and the knowledge base by building into it context specific details and conducting knowledge exchange events. Step 3 involves interpreting and analysing the knowledge base, and integrating evidence to inform intervention development. In Step 4 the intervention will be piloted and evaluated. Step 5 is the completion of the research process where outcomes for improvement will be instituted as regular practice with the facilitation of the community. In summary, the model uses an iterative knowledge construction mechanism that is complemented by external evidence to design interventions to address health priorities within the community. DISCUSSION: This is a systematic approach that operationalises the translational cycle using a framework for KT practice. It begins with the local context as its foundation for knowledge creation and ends with the development of contextually applicable interventions. It will be of interest to those involved in KT research, participatory action research, population health research and health care systems studies. The co-KT Framework is a method for embedding the principles of KT into all stages of a community-based research process, in which research questions are framed by emergent data from each previous stage.Kathryn Powell, Alison Kitson, Elizabeth Hoon, Jonathan Newbury, Anne Wilson and Justin Beilb

C. L. Downing, Salvation from Cinema: The Medium is the Message. New York and Abingdon: Routledge, 2016. Pp. viii, 196. Pb. £33.99. ISBN 978-1-138-91394-3

Review of this book on theology and film for Modern Believing

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues.

There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection