#NavigatingHealth - Building a Global Movement Empowering Patients to Navigate Health Systems
Notice bibliographique
Résumé
Background: Whilst health reports talk about health systems shifts from reactive “one size fits all” to proactive personalised health care, the current reality is that health systems are challenging for patients and carers to navigate. Patients frequently experience gaps and barriers in healthcare. We talk about patient-centred care, but we do not have patient centred systems. The challenges are not new and patient navigation was extensively research by Professor Harold Freeman in the US over 30 years ago. Audience: This presentation is for a multidisciplinary audience of patients, health professionals, policy managers and politicians focused on enabling patient-centred at a systems level. Who did you involve and engage with? What was the approach and process undertaken? Patients, professionals and policy makers in Australia and globally have been involved in what started as a national Community of Practice in Australia “Mind the Gap: #NavigatingHealth”. The approach was tested for audience interest with a series of two webinars in September 2021 to set the scene of navigating health across a life journey of patient and provider experiences with a solutions focused approach on how to tackle the many challenges. Following the success and interest of the webinars and as part of a focused advocacy and change management approach we developed a bimonthly Community of Practice. This is open to all those in Australia. Our focus in the first year was to make visible the many solutions being developed to support patients and to develop an engaged participatory community and build connection. Further to the success in the first year, we have now expanded the approach at a global level running our first global webinar in November 2022 with speakers from Australia, the United States of America, Canada and the United Kingdom providing a connected approach across a life journey. This included lived experience of patients as well as presentations from non-governmental, medical and government organisations interweaving micro, meso and macro approaches (for example included American Cancer Society, NHS England). What is the learning for the international audience? Problems that patients face navigating health systems in countries are common and consistent. Collaborating across disease and functional areas both within and across countries allows the opportunity to take a “big-picture” view to take a systems focused approach. This allows the solutions being developed to be coalesced at a systems level, remove duplication and leverage resources to benefit both patients, providers and policy makers to focus on a person-centred value-based healthcare approach across a life journey. What are the next steps? Our next steps are to consolidate the learnings and scale. Whilst our initial focus has been high income countries we will be expanding the approach in 2023 to low and middle income countries using the same methodology.
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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 | 0,018 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,018 |
| Communication savante | 0,013 | 0,017 |
| Science ouverte | 0,002 | 0,035 |
| Intégrité de la recherche | 0,006 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,004 |
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 ».