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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,118 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».