Living labs represent a user-centric approach to solving real-world challenges by encouraging active participation of external stakeholders in co-designing the research and innovation process. Highlighted by contextual research and user co-creation, living labs are ideal for addressing the challenges of providing optimal healthcare to patients living in rural and remote regions. Our objective was to synthesise the existing research on the living lab approach in co-designing, developing or implementing a rural healthcare service, clinical intervention or health-related technology.

Scoping review.

A search was conducted on 10 May 2025, to identify articles from three electronic databases (MEDLINE, EMBASE and CINAHL).

We included published literature that presented a living lab approach to improve the provision of healthcare services in a rural environment. We excluded articles examining social determinants of health (eg, physical activity and general health promotion) without a direct link to clinical service innovation or healthcare delivery.

We collected data on study methodologies, settings, stakeholders and innovation types. Data extraction was performed by two independent reviewers using a standardised form. We used frequencies and a narrative synthesis to map characteristics, methods and contexts of living lab applications in rural healthcare.

The search identified a total of 1080 articles and ultimately included 11 articles. Studies were published between 2016 and 2025 and conducted in Canada (n=3), the USA (n=3), Australia (n=2), Guatemala (n=1), Uganda (n=1) and France/Portugal (n=1). Study settings included rural hospitals, regional health networks, Indigenous communities, farming and fishing communities and underserved rural regions. Health issues targeted included cardiovascular disease, diabetes, musculoskeletal conditions, perinatal care, palliative care and infectious disease management. Study methodologies included formalised, theory-driven frameworks (n=4), community-based participatory research (n=4), user- or human-centred design (n=3) and co-design workshops and interviews (n=3). Only one study explicitly used the term ‘living lab’ to describe their innovation.

Relatively few living lab approaches have been meaningfully applied in rural health. There is a need for greater global diversification, expanded domains of focus and more robust evaluation to fully understand the potential and impact of living labs in rural healthcare.

Understanding the prognostic factors associated with the failure of total elbow replacement (TER) is crucial for informing patients about risks and enabling shared decision-making regarding TER as a definitive management option. This protocol outlines the planned analysis of National Joint Registry (NJR) data to investigate prognostic factors for TER failure.

The primary analysis will use the NJR elbow dataset, including all eligible patients who underwent TER surgery between April 2012 and December 2023. To incorporate ethnicity and comorbidities as potential prognostic factors, the NJR will be linked to the National Health Service (NHS) England Hospital Episode Statistics-Admitted Patient Care (HES-APC) data for a secondary analysis. The analysis will adhere to the REporting recommendations for tumour MARKer prognostic studies guidelines. The primary outcome under investigation is TER failure, defined as requiring revision surgery. Initially, the overall prognosis of TER will be examined using unadjusted net implant failure via the Kaplan-Meier method. The list of potential prognostic factors to be investigated in this study has been informed by a systematic review on this topic, input from patient and public involvement and engagement (PPIE) groups and a survey shared with healthcare professionals providing TER services. The relationship between each potential prognostic factor and failure will be assessed using univariable regression methods. Based on the findings from our systematic review, the univariable association will also be adjusted for age, sex and indication for TER surgery using multivariable regression methods. The extent of missing data will be reported, and the reasons for missing data will be explored. A very high degree of data completeness is expected, and a complete case analysis will be performed as the primary analysis. Multiple imputations will be considered as a sensitivity analysis.

The NJR research committee approved this analysis, and the NHS Health Research Authority tool guidance dictates that the secondary use of such data for research does not require approval from a research ethics committee. The results from this analysis will be published in a peer-reviewed journal and presented at scientific conferences.

NCT06760585.