A Digital Communication Intervention to Support Older Adults and Their Care Partners Transitioning Home After Major Surgery: Protocol for a Qualitative Research Study
Notice bibliographique
Résumé
BACKGROUND: Older adults (aged ≥65 years) account for approximately 30% of inpatient procedures in the United States. After major surgery, they are at high risk of a slow return to their previous functional status, loss of independence, and complications like delirium. With the development and refinement of Enhanced Recovery After Surgery protocols, older patients often return home much earlier than historically anticipated. This put a larger burden on care partners, close family or friends who partner with the patient and guide them through recovery. Without adequate preparation, both patients and their care partners may experience poor long-term outcomes. OBJECTIVE: This study aimed to improve and streamline recovery for patients aged ≥65 years by exploring the communication needs of patients and their care partners. Information from this study will be used to inform an intervention developed to address these needs and define processes for its implementation across surgical clinics. METHODS: This qualitative research protocol has two aims. First, we will define patient and care partner needs and perspectives related to digital health innovation. To achieve this aim, we will recruit dyads of patients (aged ≥65 years) who underwent elective major surgery 30-90 days prior and their respective care partners (aged ≥18 years). Participants will complete individual interviews and surveys to obtain demographic data, characterize their perceptions of the surgical experience, identify intervention targets, and assess for the type of intervention modality that would be most useful. Next, we will explore clinician perspectives, tools, and strategies to develop a blueprint for a digital intervention. To achieve this aim, clinicians (eg, geriatricians, surgeons, and nurses) will be recruited for focus groups to identify current obstacles affecting surgical outcomes for older patients, and we will review current assessments and tools used in their clinical practice. A hybrid deductive-inductive approach will be undertaken to identify relevant themes. Insights from both clinicians and patient-care partners will guide the development of a digital intervention strategy to support older patients and their care partners after surgery. RESULTS: This study has been approved by the Massachusetts General Hospital and Harvard Institutional Review Boards. Recruitment began in December 2023 for the patient and care partner interviews. As of August 2024, over half of the interviews have been performed, deidentified, and transcribed. Clinician recruitment is ongoing, with no focus groups conducted yet. The study is expected to be completed by fall 2024. CONCLUSIONS: This study will help create a scalable digital health option for older patients undergoing major surgery and their care partners. We aim to enhance our understanding of patient recovery needs; improve communication with surgical teams; and ultimately, reduce the overall burden on patients, their care partners, and health care providers through real-time assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59067.
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 | 0,065 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,006 |
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 ».