The objective of this study is to co-produce a care bundle for women with multiple long-term health conditions (MLTC) that could be pilot tested and implemented in UK maternity services.

Online co-production workshops each attended by 20–30 key interest holders.

United Kingdom, October 2023-February 2024.

Women with experience of pregnancy with MLTC, healthcare professionals and other interest holders involved in commissioning, planning and delivering care for pregnant women with MLTC.

This study followed a three-step process: (1) a consolidated list of key components of care for pregnant women with MLTC was created through secondary analysis of prior collected qualitative data; (2) the list of care components was explored during four co-production workshops; and (3) findings from (1) and (2) were synthesised to develop a maternity care bundle of 4–5 key care components for pregnant women with MLTC.

A maternity care bundle of five key care components for pregnant women with MLTC.

A list of 25 care components was refined to develop a proposed care bundle of five components. These were provisions of early and reliable medication advice and decision support; creation of a ‘goals of care summary’ accessible to women and the care team; provision of continuity of midwifery care throughout pregnancy and postnatal care; provision of a named care coordinator; and a formal postnatal handover of care from the multidisciplinary care team to the General Practitioner (GP) and secondary care team involving the woman.

This study coproduced an evidence-based care bundle for pregnant women with MLTC to enhance communication and ensure individualised care and support. Further collaborative work with women and professionals is required to refine, implement and evaluate its impact on outcomes.

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.

Multimorbidity or the presence of two or more long-term conditions is now common in people undergoing surgery. However, current care pathways often miss these healthcare encounters to support long-term health promotion. Therefore, there is a need for practical, scalable approaches that can be integrated into routine surgical care, for which limited solutions exist at present. We have co-designed a structured preoperative checklist to help identify and manage long-term conditions in patients listed for elective surgery. This study aims to evaluate the feasibility and acceptability of this preoperative checklist in patients undergoing elective surgery.

This is a mixed-methods feasibility study in one National Health Service trust in the UK. We will recruit up to 50 adults scheduled for elective surgery and use the checklist during initial surgical clinic appointments. Quantitative data will include recruitment and retention rates, completion of the checklist and baseline clinical characteristics, analysed using descriptive statistics. Qualitative data will be collected through semistructured interviews with up to 16 patients and clinicians. These interviews will be analysed thematically, guided by the Consolidated Framework for Implementation Research. Triangulation of quantitative and qualitative data will allow us to explore fidelity, acceptability, barriers and facilitators to implementation and refine the intervention ahead of a future pilot cluster randomised trial.

This study has received approval from the Yorkshire & The Humber - Sheffield Research Ethics Committee (approval number: 25/YH/0045). All participants will give written informed consent. Results will be published in peer-reviewed journals and shared with participants, the public and policy stakeholders.