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Nasir Hamid
Implementation, Processes and Outcomes of Advance Care Planning: A Culturally and Contextually Appropriate Programme Theory Developed in Chinese Long-Term Care Facilities
Background: Despite advance care planning (ACP) being associated with positive outcomes for residents in long-term care facilities (LTCFs), the causal pathways between ACP and these outcomes are context-specific and less understood. This lack of clarity can hinder the cultural adaptation and evaluation of ACP interventions. This study aimed to develop a programme theory that outlines the causal pathways through which the ACP is hypothesised to achieve impacts in Chinese LTCFs, with a focus on understanding its implementation, processes and outcomes. Methods: Exploratory qualitative design incorporating Theory of Change (ToC) methodology. Two ToC workshops (one face-to-face and one online) were held with 37 participants experienced in caring for residents or older people. The process was informed by a realist review and primary qualitative study. A programme theory was developed through thematic analysis, generating a ToC map depicting implementation, processes and outcomes of ACP in LTCFs. Results: The programme theory was constructed to outline the causal pathways of ACP in LTCFs, populating five ‘precondition’ domains: (1) buy-in from government and facility leadership, (2) availability of external and internal resource, (3) adequate training and awareness for public and facility, (4) identification of residents who are ready for ACP and (5) culturally sensitive communication. Nine intervention components were identified that target preconditions, such as raising ACP awareness and providing staff training and mentoring. The potential impacts of ACP were identified, for example, fostering public attitudes towards a ‘good death’ and increasing public awareness and acceptance of palliative care. Conclusions: Our mid-range programme theory can serve as a heuristic tool, adaptable for context-specific ACP interventions in other countries, enhancing the likelihood of achieving intended impacts. In particular, intervention components focused on family involvement can be transferable to East Asian regions, where relational autonomy and family-centred decision-making are emphasised. The programme theory is ready for feasibility testing for residents in Chinese LTCFs. Patient or Public Contributions: We were guided by patient and public involvement members including two residents and one family member of a resident throughout the study. They supported the overall development of programme theory, including reviewing the theory and interpreting findings.
Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation
Ensuring the generalisability of clinical machine learning (ML) models across diverse healthcare settings remains a significant challenge due to variability in patient demographics, disease prevalence, and institutional practices. Existing model evaluation approaches often rely on real-world datasets, which are limited in availability, embed confounding biases, and lack the flexibility needed for systematic experimentation. Furthermore, while generative models aim for statistical realism, they often lack transparency and explicit control over factors driving distributional shifts. In this work, we propose a novel structured synthetic data framework designed for the controlled benchmarking of model robustness, fairness, and generalisability. Unlike approaches focused solely on mimicking observed data, our framework provides explicit control over the data generating process, including site-specific prevalence variations, hierarchical subgroup effects, and structured feature interactions. This enables targeted investigation into how models respond to specific distributional shifts and potential biases. Through controlled experiments, we demonstrate the framework’s ability to isolate the impact of site variations, support fairness-aware audits, and reveal generalisation failures, particularly highlighting how model complexity interacts with site-specific effects. This work contributes a reproducible, interpretable, and configurable tool designed to advance the reliable deployment of ML in clinical settings.
Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes
Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, policies, or interventions), referred to as \textit{composite treatments}, on a set of outcome variables of interest, referred to as \textit{composite outcomes}, for a unit from observational data -- remains a fundamental problem in causal inference with applications across disciplines, such as healthcare, economics, education, social science, marketing, and computer science. Previous work in causal machine learning for ITE estimation is limited to simple settings, like single treatments and single outcomes. This hinders their use in complex real-world scenarios; for example, consider studying the effect of different ICU interventions, such as beta-blockers and statins for a patient admitted for heart surgery, on different outcomes of interest such as atrial fibrillation and in-hospital mortality. The limited research into composite treatments and outcomes is primarily due to data scarcity for all treatments and outcomes. To address the above challenges, we propose a novel and innovative hypernetwork-based approach, called \emph{H-Learner}, to solve ITE estimation under composite treatments and composite outcomes, which tackles the data scarcity issue by dynamically sharing information across treatments and outcomes. Our empirical analysis with binary and arbitrary composite treatments and outcomes demonstrates the effectiveness of the proposed approach compared to existing methods.
Application of large language models in medicine
Large language models (LLMs), such as ChatGPT, have received great attention owing to their capabilities for understanding and generating human language. Despite a trend in researching the application of LLMs in supporting different medical tasks (such as enhancing clinical diagnostics and providing medical education), a comprehensive assessment of their development, practical applications and outcomes in the medical space is still missing. Therefore, this Review aims to provide an overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face. In terms of development, we discuss the principles of existing medical LLMs, including their basic model structures, number of parameters, and sources and scales of data used for model development. In terms of deployment, we compare different LLMs across various medical tasks and with state-of-the-art lightweight models.
Clinical and cost-effectiveness of a personalised guided consultation versus usual physiotherapy care in people presenting with shoulder pain: a protocol for the PANDA-S cluster randomised controlled trial and process evaluation.
INTRODUCTION: Musculoskeletal shoulder pain is a common reason for people to be treated in physiotherapy services, but diagnosis can be difficult and often does not guide treatment or predict outcome. People with shoulder pain cite a need for clear information, and timely, tailored consultations for their pain. This trial will evaluate the introduction of a personalised guided consultation to help physiotherapists manage care for individuals with shoulder pain. METHODS AND ANALYSIS: This is a cluster randomised controlled trial to evaluate the clinical and cost-effectiveness of introducing a personalised guided consultation compared with usual UK NHS physiotherapy care. Physiotherapy services (n=16) will be randomised in a 1:1 ratio to either intervention (physiotherapy training package and personalised guided consultation incorporating a new prognostic tool) or control (usual care); 832 participants (416 in each arm) identified from participating physiotherapy service waiting lists aged 18 years or over with shoulder pain will be enrolled. Follow-up will occur at 3 time points: 6 weeks, 6 months and 12 months. The primary outcome will be the Shoulder Pain and Disability Index (SPADI) score over 12 months. Secondary outcomes include global perceived change of the shoulder condition, sleep, work absence and the impact of shoulder pain on work performance, healthcare utilisation and health-related quality of life (using EuroQol 5 Dimension 5 Level (EQ-5D-5L)). A multimethod process evaluation will investigate views and experiences of participants and physiotherapists, assess uptake, facilitators and barriers to delivery, and changes in factors assumed to explain intervention outcomes. Primary analysis of effectiveness will be by intention-to-treat, and a health economic evaluation will assess cost-utility of introducing the personalised consultation. ETHICS AND DISSEMINATION: The trial received ethics approval from the Yorkshire & The Humber (South Yorkshire) Research Ethics Committee (REC reference: 23/YH/0070). Findings will be shared through journal publications, media outlets and conference presentations. Supported by patient contributors and clinical advisors, we will communicate findings through a designated website, networks, newsletters, leaflets and in the participating physiotherapy services. TRIAL REGISTRATION NUMBER: ISRCTN45377604.
Improving the understanding of cancer and cancer care by applying data science and machine learning methods to electronic patient records
Electronic health records (EHR) hold great potential for improving the understanding of cancer care by containing high-resolution real-world data for large numbers of patients. This dissertation explores the application of data science and machine learning (ML) methods to EHRs for the purposes of translational colorectal cancer (CRC) research. I first explore the challenges in using EHRs throughout the data life cycle. I present a lightweight information extraction pipeline that retrieves TNM staging scores---common descriptors of cancer severity---from free text clinical reports with high sensitivity and precision, and also retrieves information about the presence and recurrence of CRC. These data items are essential to CRC research, for identifying cases, studying treatment variation, and comparing treatment outcomes. The pipeline was developed using data from Oxford University Hospitals (OUH) and Royal Marsden (RMH) NHS Foundation Trusts (FT), and supported the establishment of the National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) CRC database. I then focus on a specific application: combining the faecal immunochemical test (FIT) results with routinely collected data to predict CRC in symptomatic patients. The current practice is to refer patients with FIT above 10 μg/g for invasive endoscopic investigations, but only one in six investigated have CRC, motivating prediction model development. I demonstrate that an externally-derived model does not outperform FIT in the Oxford University Hospitals FIT dataset (OUH-FIT), and highlight the importance of clinically-relevant performance measures. I then show that employing more predictors, a spectrum of ML models, and novel training methods, was not sufficient to outperform FIT on OUH-FIT data. Finally, I build on and incorporate an existing sequence analysis method into an interactive app that allows to explore and cluster thousands of medical event sequences, such as visualising treatment patterns of CRC patients. The principal contributions are: a holistic discussion of EHR data quality; a staging extraction algorithm that facilitates further research/audits; a comprehensive pipeline for developing/evaluating FIT-based CRC prediction models; and a fast medical sequence exploration app that can help check data quality and identify treatment variations. There is considerable potential to use these tools on larger datasets to understand if FIT-based models are bound to fail (or if they may work on subgroups with more severe disease); and to contrast different treatment patterns employed for subgroups of CRC patients with complex disease, such as those with liver metastases.
Navigating change and crisis: an ethnographic case study of the digitalisation of general practice work between 2020-2024
Since 2020, changes in the organisation and delivery of UK general practice have been extensive and far-reaching. The widespread scale-up of remote and digital forms of working in UK general practice during the COVID-19 pandemic has driven the development of new routines and working styles, affecting how work is done, and the conditions in which it is completed, with repercussions for the wellbeing of the workforce. In the work reported here, I aim to build a more nuanced understanding of the impact of digitalisation on the kinds of work performed by patients and staff in UK GP practices, the impact thereof on staff wellbeing, and ascertain what further learning could be gleaned about how change and crisis are navigated in practice teams. I conducted a narrative literature synthesis and a multi-sited ethnographic case study of UK GP practice, informed by the Eisenhardt method. To do so, I employed multiple qualitative methods to collect data from two in-depth ethnographic case study sites. I also collected and reanalysed previously collected qualitative data from eight comparative case study sites. I analysed these data at three sequential levels: inductively, thematically, and abductively, to build and extend theory in conversation with my data. In this thesis, I make several novel contributions to empirical, methodological, and theoretical literature. I split my results on the impact of digitalisation during 2020-24 across four chapters. The first outlines the work that patients must now perform to achieve digital candidacy and craft a digital facsimile to access their GP practice successfully. The second looks at the impact on the work and wellbeing of support staff, highlighting the unique translational work they perform. The third describes the impacts on the whole practice team, and identifies new risks to their wellbeing: technostress, technosuffering, and relational strain. Finally, the fourth results chapter illustrates the organisational conditions that are most protective of staff wellbeing when navigating these kinds of change and crisis events and suggests a model for how these conditions can be constructed, maintained, or slip away. I have disseminated these findings to academic, public, policy, and practice audiences.
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)
Background: Improved blood-glucose control decreases the progression of diabetic microvascular disease, but the effect on macrovascular complications is unknown. There is concern that sulphonylureas may increase cardiovascular mortality in patients with type 2 diabetes and that high insulin concentrations may enhance atheroma formation. We compared the effects of intensive blood-glucose control with either sulphonylurea or insulin and conventional treatment on the risk of microvascular and macrovascular complications in patients with type 2 diabetes in a randomised controlled trial. Methods: 3867 newly diagnosed patients with type 2 diabetes, median age 54 years (IQR 48-60 years), who after 3 months' diet treatment had a mean of two fasting plasma glucose (FPG) concentrations of 6.1-15.0 mmol/L were randomly assigned intensive policy with a sulphonylurea (chlorpropamide, glibenclamide, or glipizide) or with insulin, or conventional policy with diet. The aim in the intensive group was FPG less than 6 mmol/L. In the conventional group, the aim was the best achievable FPG with diet alone; drugs were added only if there were hyperglycaemic symptoms or FPG greater than 15 mmol/L. Three aggregate endpoints were used to assess differences between conventional and intensive treatment: any diabetes-related endpoint (sudden death, death from hyperglycaemia or hypoglycaemia, fatal or non-fatal myocardial infarction, angina, heart failure, stroke, renal failure, amputation [of at least one digit], vitreous haemorrhage, retinopathy requiring photocoagulation, blindness in one eye, or cataract extraction); diabetes-related death (death from myocardial infarction, stroke, peripheral vascular disease, renal disease, hyperglycaemia or hypoglycaemia, and sudden death); all-cause mortality. Single clinical endpoints and surrogate subclinical endpoints were also assessed. All analyses were by intention to treat and frequency of hypoglycaemia was also analysed by actual therapy. Findings: Over 10 years, haemoglobin A(1c) (HbA(1c)) was 7.0% (6.2-8.2) in the intensive group compared with 7.9% (6.9-8.8) in the conventional group - an 11% reduction. There was no difference in HbA(1c) among agents in the intensive group. Compared with the conventional group, the risk in the intensive group was 12% lower (95% CI 1-21, p = 0.029) for any diabetes-related endpoint; 10% lower (-11 to 27, p = 0.34) for any diabetes-related death; and 6% lower (-10 to 20, p = 0.44) for all-cause mortality. Most of the risk reduction in the any diabetes-related aggregate endpoint was due to a 25% risk reduction (7-40, p = 0.0099) in microvascular endpoints, including the need for retinal photocoagulation. There was no difference for any of the three aggregate endpoints between the three intensive agents (chlorpropamide, glibenclamide, or insulin). Patients in the intensive group had more hypoglycaemic episodes than those in the conventional group on both types of analysis (both p < 0.0001). The rates of major hypoglycaemic episodes per year were 0.7% with conventional treatment, 1.0% with chlorpropamide, 1.4% with glibenclamide, and 1.8% with insulin. Weight gain was significantly higher in the intensive group (mean 2.9 kg) than in the conventional group (p < 0.001), and patients assigned insulin had a greater gain in weight (4.0 kg) than those assigned chlorpropamide (2.6 kg) or glibenclamide (1.7 kg). Interpretation: Intensive blood-glucose control by either sulphonylureas or insulin substantially decreases the risk of microvascular complications, but not macrovascular disease, in patients with type 2 diabetes. None of the individual drugs had an adverse effect on cardiovascular outcomes. All intensive treatment increased the risk of hypoglycaemia.