Co-design of the PREDICT-Kidney online tool by patients, members of the public and healthcare professionals (HCPs), to support the communication of the risk of recurrence following surgical treatment for kidney cancer.

Qualitative co-design study. Using an iterative process, feedback was collected (via workshops), prioritised and implemented.

Online workshops with participants from across the UK were conducted between December 2023 and November 2024.

18 adult participants, including patients surgically treated for kidney cancer, members of the public without a history of kidney cancer and HCPs involved in kidney cancer care.

To produce an online tool to support the communication of risk of kidney cancer recurrence that is easy to use, easy to understand and acceptable to stakeholders. Secondary outcomes are the properties of the feedback collected, including volume and type.

Across nine workshops, 99 discrete feedback items were collected, resulting in 71 actionable changes to the initial prototype tool. Differences in priorities were observed between participant groups, especially around the inclusion of information about competing risks of death. Participants valued the tool for improving consistency of follow-up information, supporting shared decision-making and providing multiple visual formats to communicate risk. Iterative feedback led to refinements in terminology, design, content and delivery, including adjustments to the presentation of recurrence and mortality risk.

A co-design approach was used to improve the PREDICT-Kidney online tool to align with the needs of patients and HCPs. A feasibility study is required to evaluate its use and impact in clinical practice.

Pulmonary embolism (PE) is a potentially fatal condition requiring timely diagnosis and treatment. CT pulmonary angiography (CTPA) is the gold standard for diagnosis and indicates PE severity through radiological markers of right heart strain. However, accurate interpretation and communication of these findings is often suboptimal in real-world practice. Artificial intelligence (AI) could alleviate pressure on radiology services by supporting PE identification, risk stratification and worklist prioritisation. Before widespread adoption, AI tools must be rigorously validated for diagnostic accuracy, safety and clinical impact.

This pragmatic single-centre, non-randomised quasi-experimental study will evaluate the diagnostic accuracy, feasibility, and clinical-cost impact of AI-assisted PE detection and risk stratification using AIDOC and IMBIO software. We will recruit two consecutive cohorts of adult patients undergoing CTPAs for suspected PE: a comparator cohort (12 months pre-AI implementation) and an intervention cohort (12 months post-AI implementation). AI will be applied retrospectively to the comparator cohort, while in the intervention cohort, radiologists will have contemporaneous access to the AI’s interpretation of CTPA images.

A subset of retrospective scans, both PE-positive and PE-negative, will undergo expert thoracic radiologist review to establish a reference standard. Data on patient demographics, clinical management and outcomes will be collected. Clinical management pathways and patient outcomes will be compared between cohorts to assess AI’s influence on acute PE management. Health economic modelling will assess the cost-effectiveness of integrating AI technology within the diagnostic workflow of acute PE.

This study was approved by the UK Healthcare Research authority (IRAS 311735, 10 May 2023). Ethical approval was granted by West of Scotland Research Ethics Service (23/WS/0067, 3 May 2023). Results will be shared with stakeholders, presented at national and international conferences, and published in open-access peer-reviewed journals.

NCT06093217.

It is unclear how mis- and disinformation regarding healthcare policy changes propagate throughout Latino communities via social media. This may lead to chilling effects that dissuade eligible individuals from enrolling in critical safety net programmes such as Medicaid. This study will examine pathways and mechanisms by which sentiment in response to mis- and disinformation regarding healthcare policies on social media differentially impact health disparity populations, thus supporting the design of tailored social media interventions to mitigate this.

We will search social media from X/Twitter, Facebook/Instagram and Reddit for keywords relating to health benefit programmes. Demographic, geographical location and other characteristics of users will be used to stratify social media data. Posts will be classified as fake-news-related or fact-related based on curated lists of fake-news-related websites. The number, temporal dissemination and positive or negative sentiment in reacting to posts and threads will be examined using the Python-based Valence Aware Dictionary and sEntiment Reasoner (VADER). Using a crowd-sourcing methodology, a novel Spanish-language VADER (S-VADER) will be created to rate sentiment to social media among Spanish-speaking Latinos. With the proposed approach, we will explore reactions to the dissemination of fake-news- or fact-related social media tweets and posts and their sources. Analyses of social media posts in response to healthcare-related policies will provide insights into fears faced by Latinos and Spanish speakers, as well as positive or negative perceptions relating to the policy over time among social media users.

Our study protocol was approved by the University of California, Los Angeles IRB (IRB#23–0 01 123). Results from this study will be disseminated in peer-reviewed journals and conference presentations, and S-VADER will be disseminated to public repositories such as GitHub.