Collecting and Sharing Person-Centered AI Clinical Summaries Across Frailty Services Provided by the National Health Service and Voluntary, Community, and Social Enterprise: Protocol for a Co-Design and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Due to its association with multimorbidity, frailty gives rise to multidimensional needs for different services. Too often, patient preferences and service encounter information are not adequately shared. OBJECTIVE: This developmental study aims to co-design, collect, and analyze encounter data from multiple community and primary-based multidisciplinary teams (MDTs) providing services for people with frailty to develop prototype large language models that can generate clinical and person-centered care summaries. METHODS: Engaging stakeholders in 2 primary care networks, we will co-design the large language model to ensure it meets local needs and preferences as well as infrastructure, information governance, and regulation requirements. General practitioners will identify 50 patients with frailty requiring MDT engagement. Three consecutive encounters between the patients and different members of MDTs will then be audio-recorded. Recordings will be transcribed into text for concept design and model pretraining. These data combine stakeholder engagement insights to develop sensitive artificial intelligence (AI) models responding to stakeholders' needs, workflows, and preferences. To generate the person-centered summaries, we will test 2 approaches to modeling the encounter data: graph-based modeling and hierarchical transformers. The AI-generated summaries will be compared to human-written summaries of the same encounter data and assessed for accuracy, quality, fluency, and person-centeredness. They will also be shared with the original MDT members for validation. We will capture inputs, processes, and outcomes across all key phases of the implementation journey to identify capability requirements, determinants of implementation (including key challenges and best practices to overcome them), and the value added by the technology. RESULTS: This protocol aims to review implementation evidence and engage stakeholders in co-design. This work package will aid the development of contextually sensitive, longitudinal, and AI-generated person-centered summarization tools. Model development will aim to achieve longitudinal person-centered summaries tested against MDT standards. If deemed suitable for deployment, optimum ways of integrating these summaries into shared care records will be explored with local key system leaders. Model evaluations will provide conclusive insights into such technologies' benefits and risks. As of August 2025, this study has not yet been funded, nor has ethical approval for the project been obtained. Consequently, dates of data collection and numbers of recruited participants are not applicable at this time. CONCLUSIONS: Our protocol provides a robust method of co-designing, evaluating, and implementing a longitudinal AI medical summary tool. Including key stakeholders at multiple stages facilitates an iterative development strategy that is designed to solve implementation challenges as they emerge. This project fits within our long-term vision to deliver a multimodal AI tool that saves clinicians time and deepens the health care professional-patient relationship. Future studies should include a larger patient sample, video-recorded health care professional-patient encounters, and a more extensive longitudinal evaluation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/68511.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".