Project description:IntroductionLong COVID, a new condition whose origins and natural history are not yet fully established, currently affects 1.5 million people in the UK. Most do not have access to specialist long COVID services. We seek to optimise long COVID care both within and outside specialist clinics, including improving access, reducing inequalities, helping self-management and providing guidance and decision support for primary care. We aim to establish a 'gold standard' of care by systematically analysing current practices, iteratively improving pathways and systems of care.Methods and analysisThis mixed-methods, multisite study is informed by the principles of applied health services research, quality improvement, co-design, outcome measurement and learning health systems. It was developed in close partnership with patients (whose stated priorities are prompt clinical assessment; evidence-based advice and treatment and help with returning to work and other roles) and with front-line clinicians. Workstreams and tasks to optimise assessment, treatment and monitoring are based in three contrasting settings: workstream 1 (qualitative research, up to 100 participants), specialist management in 10 long COVID clinics across the UK, via a quality improvement collaborative, experience-based co-design and targeted efforts to reduce inequalities of access, return to work and peer support; workstream 2 (quantitative research, up to 5000 participants), patient self-management at home, technology-supported monitoring and validation of condition-specific outcome measures and workstream 3 (quantitative research, up to 5000 participants), generalist management in primary care, harnessing electronic record data to study population phenotypes and develop evidence-based decision support, referral pathways and analysis of costs. Study governance includes an active patient advisory group.Ethics and disseminationLOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Participants will provide informed consent. Dissemination plans include academic and lay publications, and partnerships with national and regional policymakers.Trial registration numberNCT05057260, ISRCTN15022307.

Project description:BackgroundThis study identified and explored how National Health Service (NHS) hearing aid clinics address cultural competence concerning Deaf British Sign Language (BSL) users. This was approached by (i) investigating how organisational processes meet the needs of Deaf signers from a hospital and hearing aid clinic perspective, (ii) analysing policies and guidelines to investigate if they equip practitioners to meet the needs of Deaf signers and (iii) exploring with practitioners who work in hearing aid clinics about their experiences of working with Deaf signers.MethodsThis study utilised a mixed-methods multiple case study design, incorporating documentary analysis and semi-structured interviews. Interview analysis was conducted using Reflexive Thematic Analysis (RTA). The research encompassed two hearing aid clinics in separate hospitals, producing 19 documents and eight interviews (four at each site) with audiologists ensuring a representative mix of professional experience levels.ResultsFour themes emerged from the integrated analysis: (1) Understanding Deaf signers; (2) Communicating with Deaf signers; (3) Barriers and Facilitators and (4) Service improvement. A noticeable gap in understanding BSL as both a language and a cultural system was apparent across various policies, strategies, training programmes and staff expertise. Over-reliance on interpreters provided a false sense of accessibility and most participants felt tentative to engage directly with Deaf signers. Positive practices observed at Sites A and B encompassed accurate identification of patients as Deaf signers, improved interpreter availability, communication methods, enhanced training and the encouragement of professional self-awareness.ConclusionThis is the first study that explores cultural competence of hearing aid clinics and its staff concerning Deaf signers in the UK. The results show both clinics require development to become an effective provider for culturally Deaf signers. Examples of how to design culturally competent practices have been provided to assist hearing aid clinics. The findings may be applicable to other underrepresented groups who are not typical users of conventional, acoustic hearing aids provided by the NHS.

Project description:BackgroundDiagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.ObjectiveThis study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.MethodsWe introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions.ResultsOn a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions.ConclusionsOur experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

Project description:ObjectiveTo examine the key themes of positive and negative feedback in patients' online feedback on NHS (National Health Service) services in England and to understand the specific issues within these themes and how they drive positive and negative evaluation.DesignComputer-assisted quantitative and qualitative studies of 228 113 comments (28 971 142 words) of online feedback posted to the NHS Choices website. Comments containing the most frequent positive and negative evaluative words are qualitatively examined to determine the key drivers of positive and negative feedback.ParticipantsContributors posting comments about the NHS between March 2013 and September 2015.ResultsOverall, NHS services were evaluated positively approximately three times more often than negatively. The four key areas of focus were: treatment, communication, interpersonal skills and system/organisation. Treatment exhibited the highest proportion of positive evaluative comments (87%), followed by communication (77%), interpersonal skills (44%) and, finally, system/organisation (41%). Qualitative analysis revealed that reference to staff interpersonal skills featured prominently, even in comments relating to treatment and system/organisational issues. Positive feedback was elicited in cases of staff being caring, compassionate and knowing patients'' names, while rudeness, apathy and not listening were frequent drivers of negative feedback.ConclusionsAlthough technical competence constitutes an undoubtedly fundamental aspect of healthcare provision, staff members were much more likely to be evaluated both positively and negatively according to their interpersonal skills. Therefore, the findings reported in this study highlight the salience of such 'soft' skills to patients and emphasise the need for these to be focused upon and developed in staff training programmes, as well as ensuring that decisions around NHS funding do not result in demotivated and rushed staff. The findings also reveal a significant overlap between the four key themes in the ways that care is evaluated by patients.

Project description:ObjectivesTo examine how observer and self-report measures of shared decision-making (SDM) evaluate the decision-making activities that patients and clinicians undertake in routine consultations.DesignMulti-method study using observational and self-reported measures of SDM and qualitative analysis.SettingBreast care and predialysis teams who had already implemented SDM.ParticipantsBreast care consultants, clinical nurse specialists and patients who were making decisions about treatment for early-stage breast cancer. Predialysis clinical nurse specialists and patients who needed to make dialysis treatment decisions.MethodsConsultations were audio recorded, transcribed and thematically analysed. SDM was measured using Observer OPTION-5 and a dyadic SureScore self-reported measure.ResultsTwenty-two breast and 21 renal consultations were analysed. SureScore indicated that clinicians and patients felt SDM was occurring, but scores showed ceiling effects for most participants, making differentiation difficult. There was mismatch between SureScore and OPTION-5 score data, the latter showing that each consultation lacked at least some elements of SDM. Highest scoring items using OPTION-5 were 'incorporating patient preferences into decisions' for the breast team (mean 18.5, range 12.5-20, SD 2.39) and 'eliciting patient preferences to options' for the renal team (mean 16.15, range 10-20, SD 3.48). Thematic analysis identified that the SDM encounter is difficult to measure because decision-making is often distributed across encounters and time, with multiple people, it is contextually adapted and can involve multiple decisions.ConclusionsSelf-reported measures can broadly indicate satisfaction with SDM, but do not tell us about the quality of the interaction and are unlikely to capture the multi-staged nature of the SDM process. Observational measures provide an indication of the extent to which elements of SDM are present in the observed consultation, but cannot explain why some elements might not be present or scored lower. Findings are important when considering measuring SDM in practice.

Dataset Information

‘Enough is enough’: a mixed methods study on the key factors driving UK NHS nurses’ decision to strike

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets