London National Health Service Research Articles

Identifying barriers to outpatient appointment attendance in patient groups at risk of inequity: a mixed methods study in a London NHS trust.

There is significant health inequity in the United Kingdom (U.K.), with different populations facing challenges accessing health services, which can impact health outcomes. At one London National Health Service (NHS) Trust, data showed that patients from deprived areas and minority ethnic groups had a higher likelihood of missing their first outpatient appointment. This study's objectives were to understand barriers to specific patient populations attending first outpatient appointments, explore systemic factors and assess appointment awareness. Five high-volume specialties identified as having inequitable access based on ethnicity and deprivation were selected as the study setting. Mixed methods were employed to understand barriers to outpatient attendance, including qualitative semi-structured interviews with patients and staff, observations of staff workflows and interrogation of quantitative data on appointment communication. To identify barriers, semi-structured interviews were conducted with patients who missed their appointment and were from a minority ethnic group or deprived area. Staff interviews and observations were carried out to further understand attendance barriers. Patient interview data were analysed using inductive thematic analysis to create a thematic framework and triangulated with staff data. Subthemes were mapped onto a behavioural science framework highlighting behaviours that could be targeted. Quantitative data from patient interviews were analysed to assess appointment awareness and communication. Twenty-six patients and 11 staff were interviewed, with four staff observed. Seven themes were identified as barriers - communication factors, communication methods, healthcare system, system errors, transport, appointment, and personal factors. Knowledge about appointments was an important identified behaviour, supported by eight out of 26 patients answering that they were unaware of their missed appointment. Environmental context and resources were other strongly represented behavioural factors, highlighting systemic barriers that prevent attendance. This study showed the barriers preventing patients from minority ethnic groups or living in deprived areas from attending their outpatient appointment. These barriers included communication factors, communication methods, healthcare the system, system errors, transport, appointment, and personal factors. Healthcare services should acknowledge this and work with public members from these communities to co-design solutions supporting attendance. Our work provides a basis for future intervention design, informed by behavioural science and community involvement.

What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting

IntroductionThe English National Health Service (NHS) Diabetic Eye Screening Programme (DESP) performs around 2.3 million eye screening appointments annually, generating approximately 13 million retinal images that are graded by humans...

Ethnic inequalities during clinical placement: A qualitative study of student nurses' experiences within the London National Health Service.

To understand how student nurse experiences on clinical placement, within National Health Service (NHS) hospitals, differ for ethnic minority and White British groups. A qualitative thematic analysis with an inductive approach. Data from semi-structured interviews with 21 London (United Kingdom) hospital-based student nurses were examined using thematic analysis. Participants were interviewed as part of the Tackling Inequalities and Discrimination Experiences in Health Services (TIDES) study and asked about their experiences during clinical placement. Five main themes were identified: (1) Role of mentors, (2) Discrimination and unfair treatment, (3) Speaking up/out, (4) Career progression, and (5) Consequences of adverse experiences. All themes were linked, with the social dynamics and workplace environment (referred to as "ward culture") providing a context that normalizes mistreatment experienced by nursing students. Students from ethnic minority backgrounds reported racism as well as cultural and/or religious microaggressions. While being valued for their race and ethnicity, White British students also experienced discrimination and inequity due to their age, sex, gender, and sexual orientation. Students from both White British and ethnic minority groups acknowledged that being treated badly was a barrier to career progression. Ethnic minority students also noted the lack of diverse representation within senior nursing positions discouraged career progression within the UK NHS. These initial experiences of inequality and discrimination are liable to shape a student's perspective of their profession and ability to progress within nursing. The NHS is responsible for ensuring that student nurses' developmental opportunities are equal, irrespective of ethnicity. Ward culture is perpetuated by others who normalize mistreatment and concurrently disadvantage ethnic minority students, making them feel unvalued. This in turn impacts both staff retention and career progression within the NHS. Training assessors should be aware of the existing culture of discrimination within clinical placements and work to eradicate it.

HbA1c recording in patients following a first diagnosis of serious mental illness: the South London and Maudsley Biomedical Research Centre case register

ObjectivesTo investigate factors associated with the recording of glycated haemoglobin (HbA1c) in people with first diagnoses of serious mental illness (SMI) in a large mental healthcare provider, and factors associated...

Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach.

Pain is a widespread issue, with 20% of adults (1 in 5) experiencing it globally. A strong association has been demonstrated between pain and mental health conditions, and this association is known to exacerbate disability and impairment. Pain is also known to be strongly related to emotions, which can lead to damaging consequences. As pain is a common reason for people to access health care facilities, electronic health records (EHRs) are a potential source of information on this pain. Mental health EHRs could be particularly beneficial since they can show the overlap of pain with mental health. Most mental health EHRs contain the majority of their information within the free-text sections of the records. However, it is challenging to extract information from free text. Natural language processing (NLP) methods are therefore required to extract this information from the text. This research describes the development of a corpus of manually labeled mentions of pain and pain-related entities from the documents of a mental health EHR database, for use in the development and evaluation of future NLP methods. The EHR database used, Clinical Record Interactive Search, consists of anonymized patient records from The South London and Maudsley National Health Service Foundation Trust in the United Kingdom. The corpus was developed through a process of manual annotation where pain mentions were marked as relevant (ie, referring to physical pain afflicting the patient), negated (ie, indicating absence of pain), or not relevant (ie, referring to pain affecting someone other than the patient, or metaphorical and hypothetical mentions). Relevant mentions were also annotated with additional attributes such as anatomical location affected by pain, pain character, and pain management measures, if mentioned. A total of 5644 annotations were collected from 1985 documents (723 patients). Over 70% (n=4028) of the mentions found within the documents were annotated as relevant, and about half of these mentions also included the anatomical location affected by the pain. The most common pain character was chronic pain, and the most commonly mentioned anatomical location was the chest. Most annotations (n=1857, 33%) were from patients who had a primary diagnosis of mood disorders (International Classification of Diseases-10th edition, chapter F30-39). This research has helped better understand how pain is mentioned within the context of mental health EHRs and provided insight into the kind of information that is typically mentioned around pain in such a data source. In future work, the extracted information will be used to develop and evaluate a machine learning-based NLP application to automatically extract relevant pain information from EHR databases.

Clinical impact of reducing the frequency of clozapine monitoring: controlled mirror-image cohort study.

To minimise infection during COVID-19, the clozapine haematological monitoring interval was extended from 4-weekly to 12-weekly intervals in South London and Maudsley NHS Foundation Trust. To investigate the impact of this temporary policy change on clinical and safety outcomes. All patients who received clozapine treatment with extended (12-weekly) monitoring in a large London National Health Service trust were included in a 1-year mirror-image study. A comparison group was selected with standard monitoring. The proportion of participants with mild to severe neutropenia and the proportion of participants attending the emergency department for clozapine-induced severe neutropenia treatment during the follow-up period were compared. Psychiatric hospital admission rates, clozapine dose and concomitant psychotropic medication in the 1 year before and the 1 year after extended monitoring were compared. All-cause clozapine discontinuation at 1-year follow-up was examined. Of 569 participants, 459 received clozapine with extended monitoring and 110 controls continued as normal. The total person-years were 458 in the intervention group and 109 in the control group, with a median follow-up time of 1 year in both groups. During follow-up, two participants (0.4%) recorded mild to moderate neutropenia in the intervention group and one (0.9%) in the control group. There was no difference in the incidence of haematological events between the two groups (IRR = 0.48, 95% CI 0.02-28.15, P = 0.29). All neutropenia cases in the intervention group were mild, co-occurring during COVID-19 infection. The median number of admissions per patient during the pre-mirror period remained unchanged (0, IQR = 0) during the post-mirror period. There was one death in the control group, secondary to COVID-19 infection. There was no evidence that the incidence of severe neutropenia was increased in those receiving extended monitoring.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

IntroductionPeople often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers...

Military veterans and civilians’ mental health diagnoses: an analysis of secondary mental health services

PurposeHealthcare provision in the United Kingdom (UK) falls primarily to the National Health Service (NHS) which is free at the point of access. In the UK, there is currently no national marker to identify military veterans in electronic health records, nor a requirement to record it. This study aimed to compare the sociodemographic characteristics and recorded mental health diagnoses of a sample of veterans and civilians accessing secondary mental health services.MethodsThe Military Service Identification Tool, a machine learning computer tool, was employed to identify veterans and civilians from electronic health records. This study compared the sociodemographic characteristics and recorded mental health diagnoses of veterans and civilians accessing secondary mental health care from South London and Maudsley NHS Foundation Trust, UK. Data from 2,576 patients were analysed; 1288 civilians and 1288 veterans matched on age and gender.ResultsDepressive disorder was the most prevalent across both groups in the sample (26.2% veterans, 15.5% civilians). The present sample of veterans accessing support for mental health conditions were significantly more likely to have diagnoses of anxiety, depressive, psychosis, personality, and stress disorders (AORs ranging 1.41–2.84) but less likely to have a drug disorder (AOR = 0.51) than age- and gender-matched civilians.ConclusionVeterans accessing secondary mental health services in South London had higher risks for many mental health problems than civilians accessing the same services. Findings suggest that military career history is a key consideration for probable prognosis and treatment, but this needs corroborating in other geographical areas including national population-based studies in the UK.

An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways

The National Health Service (NHS) constitution sets out minimum standards for rights of access of patients to NHS services. The ‘Faster Diagnosis Standard’ (FDS) states that 75% of patients should be told whether they have a diagnosis of cancer or not within 28 days of an urgent GP referral. Timely diagnosis and treatment lead to improved outcomes for cancer patients, however, compliance with these standards has recently been challenged, particularly in the context of operational pressures and resource constraints relating to the COVID-19 pandemic. In order to minimise diagnostic delays, the National Physical Laboratory in collaboration with the Royal Free London (RFL) NHS Foundation Trust address this problem by treating it as a formal resource optimisation, aiming to minimise the number of patients who breach the FDS. We use discrete event simulation and particle swarm optimisation to identify areas for improving the efficiency of cancer diagnosis at the RFL. We highlight capacity-demand mismatches in the current cancer diagnosis pathways at the RFL, including imaging and endoscopy investigations. This is due to the volume of patients requiring these investigations to meet the 28-day FDS target. We find that increasing resources in one area alone does not fully solve the problem. By looking at the system as a whole we identify areas for improvement which will have system-wide impact even though individually they do not necessarily seem significant. The outcomes and impact of this project have the potential to make a valuable impact on shaping future hospital activity.

Predictors of falls and fractures leading to hospitalisation in 36 101 people with affective disorders: a large representative cohort study

ObjectivesTo investigate predictors of falls and fractures leading to hospitalisation in people with affective disorders.DesignCohort study.SettingThe South London and Maudsley National Health Service (NHS) Foundation Trust (SLaM) Biomedical Research Centre...

Using natural language processing to extract self-harm and suicidality data from a clinical sample of patients with eating disorders: a retrospective cohort study

ObjectivesThe objective of this study was to determine risk factors for those diagnosed with eating disorders who report self-harm and suicidality.Design and settingThis study was a retrospective cohort study within...

Identifying factors that promote and limit the effective use of real-time patient experience feedback: a mixed-methods study in secondary care

ObjectivesThe Friends and Family Test (FFT) is commissioned by the National Health Service (NHS) in England to capture patient experience as a real-time feedback initiative for patient-centred quality improvement (QI)....

Evaluation of the effectiveness and acceptability of the long-acting oral antipsychotic penfluridol: Illustrative case series.

In this study, we sought to determine clinical outcomes at 1 year for patients prescribed penfluridol in an inner London National Health Service Trust. Using noninterventional data, we describe the use, effectiveness and safety of this treatment modality. We retrospectively followed up 17 patients prescribed penfluridol as part of routine clinical practice. All patients took penfluridol once weekly. Of these patients, 12 (70.6%) were considered treatment resistant. The average duration of illness for this cohort was 10 years (SD = 6.7). At 1 year, nine (53%) patients remained on treatment. Median survival time was not reached at 1-year follow-up; mean time on penfluridol was 251 days (95% confidence interval (CI), 184-318). The mean number of admissions to hospital in the year following penfluridol initiation was 0.6 compared with 0.8, 1 year before initiation (p = 0.465). The median number of bed days 1 year before penfluridol initiation was 24, whereas in the year following penfluridol initiation, it was 0 (p = 0.514). Although penfluridol is unlicensed in the United Kingdom, limited data suggest that this long-acting oral therapy has the potential to be used safely and effectively for the treatment of psychotic disorders. However, more data are required to establish the place of penfluridol and other potential long-acting oral antipsychotic formulations in the treatment of psychotic disorders.

Factors influencing procurement behaviour and decision-making: an exploratory qualitative study in a UK healthcare provider

BackgroundIn 2016 the UK Department of Health and Social Care published the results of a comprehensive review of efficiency in hospitals, identifying “unwarranted variation” in procurement (or purchasing) practices for materials, supplies and devices. Addressing this variation in materials and supplies procurement practice has been identified as particularly important for creating efficiencies in health service delivery. However, little is known about the behaviour and experiences of front-line individuals who make these procurement decisions, which has implications for the development of strategies to improve efficiency. The objective of this study is to improve understanding of the factors influencing procurement behaviour and decisions among requisitioners who use an internal electronic procurement portal for medical supplies and equipment, and identify areas where efficiency could be improved.MethodsQualitative semi-structured individual interview study, following approximately 70 h of exploratory observations on site. The study context was a large London National Health Service (NHS) healthcare provider (the Trust), where we focussed primarily on purchases managed by a large hospital. Participants were drawn from requisitioners from multiple directorates across the Trust (n = 15; of these n = 2 clinical staff members, n = 13 non-clinical).ResultsFour factors stood out in our analysis as directly affecting procurement decisions: (1) a high level of variation in electronic purchasing and inventory management procedures throughout the Trust, (ii) an inaccurate and cumbersome search facility on the internal electronic procurement platform, exacerbated by poor IT skills training and support (iii) an inefficient purchase approvals system and (iv) multiple working sites and cluttered environments. We observed that these factors led requisitioners to employ a variety of strategies or so-called ‘workarounds’ to overcome the challenges they encountered, including stockpiling, relying on internal and supplier relationships, by-passing procedures to save time, purchasing outside existing agreements to save cost, and (re) delegating purchasing responsibilities among requisitioner staff - which both addressed and created difficulties.ConclusionsWorking with the assumption that staff ‘workarounds’ indicate where main issues lie, we offer four possible explanations to why they occur: (a) to maintain services and prepare for future care requirements, (b) to save on costs for the organisation, (c) to develop skills and development in purchasing and (d) to break silos and work collaboratively. These four explanations help provide initial starting points for improving efficiencies in health supplies’ procurement processes.

Evaluation of a patient and public involvement training programme for researchers at a large biomedical research centre in the UK

ObjectivesTo design, deliver and evaluate a programme of training workshops for biomedical researchers aimed at building confidence and skills in actively involving patients and the public (PPI) in research.DesignA bespoke...

The challenges and opportunities of mental health data sharing in the UK

The challenges and opportunities of mental health data sharing in the UK

Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data

Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models. In this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis. The outcome of interest was reliable and clinically significant improvement in depression (PHQ-9) and anxiety (GAD-7) symptoms. The predictive accuracy of the model was assessed in an external test sample (Cumbria Northumberland Tyne and Wear NHS Foundation Trust dataset; n=2036) using the area under the curve (AUC), positive predictive values (PPVs), and negative predictive values (NPVs). In study 2, we retrained the Oracle algorithm using a multiservice sample (South West Yorkshire Partnership NHS Foundation Trust, North East London NHS Foundation Trust, Cheshire and Wirral Partnership NHS Foundation Trust, and Cambridgeshire and Peterborough NHS Foundation Trust; n=42 992) and compared its performance with an expected treatment response model and five machine learning models (Bayesian updating algorithm, elastic net regularisation, extreme gradient boosting, support vector machine, and neural networks based on a multilayer perceptron algorithm) in an external test sample (Whittington Health NHS Trust; Barnet Enfield and Haringey Mental Health Trust; Pennine Care NHS Foundation Trust; and Humber NHS Foundation Trust; n=30 026). The Oracle algorithm trained using iterative logistic regressions generalised well to external test samples, explaining up to 47·3% of variability in treatment outcomes. Prediction accuracy was modest at session one (AUC 0·59 [95% CI 0·55-0·62], PPV 0·63, NPV 0·61), but improved over time, reaching high prediction accuracy (AUC 0·81 [0·77-0·86], PPV 0·79, NPV 0·69) as early as session seven. The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that included additional predictors modelling sample-level and individual-level variability. Overall, the Oracle algorithm significantly outperformed the expected treatment response model (mean AUC 0·80 vs 0·70, p<0·0001]). Dynamic prediction models using sparse and readily available symptom measures are capable of predicting psychotherapy outcomes with high accuracy. University of Sheffield.

Clinical characteristics and outcomes of adult patients admitted with COVID-19 in East London: a retrospective cohort analysis

BackgroundDescriptions of clinical characteristics of patients hospitalised withCOVID-19, their clinical course and short-term inpatient and outpatient outcomes in deprived urban populations in the UK are still relatively sparse. We describe...

Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals

IntroductionPatients with diabetes mellitus admitted to hospital with COVID-19 have poorer outcomes. However, the drivers of poorer outcomes are not fully elucidated. We performed detailed characterization of patients with COVID-19...

Rapid implementation of Microsoft Teams in response to COVID-19: one acute healthcare organisation’s experience

Background COVID-19 presented significant challenges to healthcare organisations, which needed to rapidly remodel their services but were unable to allow staff to meet face to face to minimise infection risk....