Recent advances in treatment and care have improved survival rates for children and young adults with severe blood disorders such as sickle cell disease (SCD), transfusion-dependent beta-thalassaemia (TDT) and acute leukaemia. However, their quality of life and reproductive and psychosocial outcomes are not yet well studied. For SCD and TDT, robust survival data are mainly limited to North America. Thus, there is a need to fill these knowledge gaps to guide improvements in care, address unmet clinical needs and rigorously assess the efficacy of emerging novel therapies.

This is an observational population-based mixed-methods study of individuals diagnosed with SCD, TDT or acute leukaemia when under the age of 18 in England, involving a data linkage component and a patient-reported outcomes measures survey. Data linkage-eligible participants will be identified from national and regional databases, including the Hospital Episode Statistics, Yorkshire Specialist Register of Cancer in Children & Young People and the National Congenital Anomaly and Rare Diseases Registration Service. Data linkage will be processed within the NHS England and the University of Leeds’ secure, trusted research environments. Data will be accessed without consent under section 251 and approval by the confidentiality advisory group. It will assess survival rates for SCD and TDT as well as clinical, educational and mental health outcomes for SCD, TDT and acute leukaemia diagnosed in childhood.

Survey-eligible participants for SCD, TDT and acute leukaemia cohorts will be checked for their suitability to participate by the North of England clinical care teams. An NHS-approved survey provider will facilitate data checks with the NHS National Data Opt-Out Service. Consent is required for participation in the survey and for subsequent data linkage to existing databases. Surveys are conducted in various formats (online, paper and phone), with reminders sent after 21 days. The survey will assess quality of life and psychosocial and reproductive outcomes. Participants can withdraw at any time, and support is available via telephone helplines.

The study has received ethical and information governance approval from the Health Research Authority (Reference 24/YH/0186) and the Confidentiality Advisory Group (CAG 24/CAG/0138) to process identifiable data without consent. Study results will be available to patients, physicians, researchers, stakeholders and others through open-access publishing, results sharing via media platforms and presentations at conferences and meetings.

Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are currently being developed to aid prescribing in primary care. There is a lack of research on how these systems will be perceived and used by healthcare professionals and subsequently on how to optimise the implementation process of AI-based CDSSs (AICDSSs).

To explore healthcare professionals’ perspectives on the use of an AICDSS for prescribing in co-existing multiple long-term conditions (MLTC), and the relevance to shared decision making (SDM).

Qualitative study using template analysis of semistructured interviews, based on a case vignette and a mock-up of an AICDSS.

Healthcare professionals prescribing for patients working in the English National Health Service (NHS) primary care in the West Midlands region.

A purposive sample of general practitioners/resident doctors (10), nurse prescribers (3) and prescribing pharmacists (2) working in the English NHS primary care.

The proposed tool generated interest among the participants. Findings included the perception of the tool as user friendly and as a valuable complement to existing clinical guidelines, particularly in a patient population with multiple long-term conditions and polypharmacy, where existing guidelines may be inadequate. Concerns were raised about integration into existing clinical documentation systems, medicolegal aspects, how to interpret findings that were inconsistent with clinical guidelines, and the impact on patient-prescriber relationships. Views differed on whether the tool would aid SDM.

AICDSSs such as the OPTIMAL tool hold potential for optimising pharmaceutical treatment in patients with MLTC. However, specific issues related to the tool need to be addressed and careful implementation into the existing clinical practice is necessary to realise the potential benefits.

Cluster randomised trials (CRTs) can be at risk of bias driven by differential identification and recruitment of participants across treatments, posing a threat to the validity of findings. We explored the awareness and importance, among CRT researchers, of the recommended bias mitigation measures.

Qualitative interview study using semistructured interviews.

Participants were researchers involved in conducting CRTs, including investigators, statisticians and trial coordinators. 24 participants, including statisticians (n=13, 54.2%), clinical investigators (n=9, 37.5%) and trial coordinators (n=2, 8.3%), were interviewed; with representation from the UK (n=10, 41.7%), Australia (n=5, 20.8%) and the USA (n=4, 16.7%).

Participants exhibited differing levels of knowledge related to biases. Some participants demonstrated high levels of knowledge, but we also identified prevalent misconceptions, with some evidence of superficial knowledge. While some participants worked in collaborative teams, other teams’ responsibilities were delineated, and this impacted on how knowledge of biases was shared and acted on. Logistical and practical issues could prevent known solutions to mitigate biases being implemented. Biases also manifested because of a perception from participant recruiters that the purpose of research is for participant benefit rather than producing generalisable knowledge; and a normalisation or expectation that CRTs produce a lower level of evidence.

There is an urgent need to ensure that CRTs are free from risks of bias. Mitigation measures are either not known, not practical or unconsciously subverted. More education and collaborative working might help. Preventing subconscious bias during participant recruitment and dispelling the myth that CRTs produce lower levels of evidence would require a change in culture.

Ineffective surgery scheduling fails to align demand with need, resulting in financial waste, resource inefficiencies and delays in care, which ultimately lead to poorer patient outcomes. Digital systems present a promising approach to optimising scheduling. However, research examining their impact remains limited. This planned systematic review aims to evaluate the effects of digital surgery scheduling systems on the quality of preoperative care.

A systematic review will be undertaken using Ovid MEDLINE, Ovid EMBASE, HMIC and PsycINFO (from inception to the present). The outcomes under investigation include the domains of quality of care (eg, patient-centredness, safety, effectiveness, efficiency, timeliness of care and equity). Two independent reviewers will screen and extract data, resolving any disagreements through discussion. Once eligible studies are identified, the extracted data will be summarised in a table. The risk of bias in the articles will be evaluated using the appropriate National Heart, Lung and Blood Institute quality qssessment tool, depending on the study design. A subgroup analysis will be carried out using demographic variables supported by the data. A narrative synthesis and a meta-analysis will be performed, to quantify the impact of digital surgery scheduling tools on reported outcomes.

This proposed review aims to collate and summarise peer-reviewed, published evidence, and therefore, does not require ethical approval. This protocol and the subsequent review will be disseminated in peer-reviewed journals, at conferences and through patient-led lay summaries. PROSPERO registration number: CRD42024625469.