To explore experience and prevalence of vulval lichen sclerosus (VLS) diagnosis in general practice using an anonymous patient survey.

Quantitative descriptive cross-sectional survey informed by previous qualitative interviews and developed with patient representatives, sent to people recorded in general practice as having a VLS diagnosis.

General practices (n=24) in the UK (West Midlands).

n=177 respondents.

One in five respondents reported that they had been misdiagnosed, and about a third reported that it was a struggle to get treatment. Only one third said they received regular check-ups, recommended in clinical guidelines. One-fifth reported they were not being treated with topical corticosteroids, the main first-line treatment for VLS. Less than one in 10 were members of a support group, and around four in 10 felt they had to hide their condition and did not speak to anyone else about it. Survey respondents prioritised improving education and awareness among healthcare professionals (HCPs).

General practitioners and other primary care HCPs have a key role in recognising, diagnosing and managing VLS. Improving education and awareness among HCPs was a key priority for this patient group. Patients should be made aware of the need for ongoing treatment and yearly check-ups to prevent or manage disease progression. VLS is a highly stigmatised condition, and appointments with HCPs may be the only opportunity for people to talk about their experience.

To integrate the quantitative and qualitative data collected as part of the PEACH (Procalcitonin: Evaluation of Antibiotic use in COVID-19 Hospitalised patients) study, which evaluated whether procalcitonin (PCT) testing should be used to guide antibiotic prescribing and safely reduce antibiotic use among patients admitted to acute UK National Health Service (NHS) hospitals.

Triangulation to integrate quantitative and qualitative data.

Four data sources in 148 NHS hospitals in England and Wales including data from 6089 patients.

A triangulation protocol was used to integrate three quantitative data sources (survey, organisation-level data and patient-level data: data sources 1, 2 and 3) and one qualitative data source (clinician interviews: data source 4) collected as part of the PEACH study. Analysis of data sources initially took place independently, and then, key findings for each data source were added to a matrix. A series of interactive discussion meetings took place with quantitative, qualitative and clinical researchers, together with patient and public involvement (PPI) representatives, to group the key findings and produce seven statements relating to the study objectives. Each statement and the key findings related to that statement were considered alongside an assessment of whether there was agreement, partial agreement, dissonance or silence across all four data sources (convergence coding). The matrix was then interpreted to produce a narrative for each statement.

To explore whether PCT testing safely reduced antibiotic use during the first wave of the COVID-19 pandemic.

Seven statements were produced relating to the PEACH study objective. There was agreement across all four data sources for our first key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. The second statement was related to this key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing safely reduced antibiotic prescribing’. Partial agreement was found between data sources 3 (quantitative patient-level data) and 4 (qualitative clinician interviews). There were no data regarding safety from data sources 1 or 2 (quantitative survey and organisational-level data) to contribute to this statement. For statements three and four, ‘PCT was not used as a central factor influencing antibiotic prescribing’, and ‘PCT testing reduced antibiotic prescribing in the emergency department (ED)/acute medical unit (AMU),’ there was agreement between data source 2 (organisational-level data) and data source 4 (interviews with clinicians). The remaining two data sources (survey and patient-level data) contributed no data on this statement. For statement five, ‘PCT testing reduced antibiotic prescribing in the intensive care unit (ICU)’, there was disagreement between data sources 2 and 3 (organisational-level data and patient-level data) and data source 4 (clinician interviews). Data source 1 (survey) did not provide data on this statement. We therefore assigned dissonance to this statement. For statement six, ‘There were many barriers to implementing PCT testing during the first wave of COVID-19’, there was partial agreement between data source 1 (survey) and data source 4 (clinician interviews) and no data provided by the two remaining data sources (organisational-level data and patient-level data). For statement seven, ‘Local PCT guidelines/protocols were perceived to be valuable’, only data source 4 (clinician interviews) provided data. The clinicians expressed that guidelines were valuable, but as there was no data from the other three data sources, we assigned silence to this statement.

There was agreement between all four data sources on our key finding ‘during the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. Data, methodological and investigator triangulation, and a transparent triangulation protocol give validity to this finding.

ISRCTN66682918.

To evaluate the role of using outputs from patient-facing online triage tools in clinical decision-making in primary care.

Systematic review.

Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science and Scopus were searched for literature published between 1 January 2002 and 31 December 2022 and updated for literature published up to end of November 2024.

Studies of any design are included where the study investigates how primary care clinicians make clinical decisions in response to patient concerns reported using online triage tools.

Data were extracted, and quality assessment was conducted using the Mixed Methods Appraisal Tool. Narrative synthesis was used to analyse the findings.

14 studies were included, which were conducted in the UK (n=9), Sweden (n=3) and Spain (n=2). There were no studies that examined clinical decision-making as an outcome. Outcomes relating to the impact on clinical decision-making were grouped into three categories: patient clinical outcomes (n=9), primary care practitioner experience (n=11) and healthcare system outcomes (n=14). Studies reported faster clinical decisions made in response to patient concerns. Other studies reported clinicians offering unnecessary urgent appointments as patients learnt to ‘game’ the system. Clinicians felt confident managing patient requests as they can access additional information (such as a photo attachment). Moreover, clinicians’ time was freed up from appointments with limited clinical value. Contrarily, online triage was perceived as an additional step in the workflow.

Clinicians should be aware that their decision-making processes are likely to differ when using online triage tools. Developers can use the findings to improve the usability of the tools to aid clinical decision-making. Future research should focus on patient-facing online triage tools in general practice and the process of clinical decision-making.

CRD42022373944.