Describing the scope of practice of patient navigators for older adult patients with complex care needs
Notice bibliographique
Résumé
Introduction: Transitions in care (e.g., discharge to home) are stressful for patients, especially for older adults living with complex conditions. To bridge the care gap and improve the continuity of care across care transitions, a new model of care was introduced at a large metropolitan hospital in partnership with a community agency that uses patient navigators which are embedded in the hospital’s care teams, including emergency, acute, and rehabilitative care. Patient Navigators are regulated healthcare professionals with experience in care coordination to assist with care planning during a patient's hospital stay, and to support patients/families in their care journey to their home for up to 90 days. There is qualitative evidence demonstrating that patient navigators have increased patient/caregiver satisfaction at the hospital but further evidence is required to characterize the scope of practice of navigators to better understand how they are integrating with the larger clinical team. Aim∕Objectives∕Theories∕Methods: The purpose of this study was to characterize the scope of practice of patient navigators. The objectives were to identify the characteristics of patients who have been supported by a navigator and to determine the reasons for referral and intervention, service length, and post-discharge location. To accomplish this aim, a cohort observational study design approach was used, which included using clinical notes collected by the patient navigator as well as hospital decision-support chart data between November 2019 to November 2021. Frequencies and descriptive statistics were used to analyze the data. Highlights∕Results∕Key Findings: Of the 176 cases referred to the patient navigator, 28 were deemed not appropriate for the service, 53 received consults (3 days or less of support), and 90 were assigned to the navigator caseload. Of the 90 on the navigator caseload, the average age was 78.9 (SD=9.9) and 44 had a caregiver present to support them. The top reasons were: 1) service connection to community service; 2) housing related; 3) discharge planning, and 4) provider connection. The average service days was 74 days (Median=49 days). The post-discharge location were: home (66%), rehab/reactivation (15%), palliative/deceased (13%), long-term care (3%), acute care (1%), or unknown (2%). Conclusion: To our knowledge, this is the first Canadian study to describe a patient navigator program for older adults with complex care needs in a hospital setting. The finding suggest that patient navigators are playing an active role in helping older adult patients by linking them to appropriate services while also supporting patients and caregivers. The data illustrates that it is a high resource service, with clients receiving supports over a 74 day period, but led to a large proportion of clients being returned back home. Since this is a novel care model, the findings will help provide the evidence needed to make a case for implementing patient navigators. Implications: The Patient Navigator played an important role in advocacy liaising with health and social care providers in the community, and a critical role in discharge planning in the hospital providing follow-up support to patients/families while they transitioned back home for up to 90 days.
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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 | 0,008 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».