The co-development of a mobile navigation app in an integrated care network in Ontario, Canada
Notice bibliographique
Résumé
Introduction: The complexity of navigating health and social care services is a common challenge to both providers and patients and their families. In the nineties, navigation programs arouse to assist cancer patients accessing services. Since then, those programs expanded to additional patient populations. Navigation programs can take various forms including virtual navigation. Virtual navigation in healthcare is a proactive process by which patients obtain information and support via internet resources to manage their illness demands. Approach: With a commitment to facilitate 24/7 access to its attributed population, Burlington Ontario Health Team (BOHT)-an integrated care network in Ontario- developed a mobile navigation app. Developing this app utilized experienced-based co-design approach. This approach took place via organizing a working group that included all stakeholders within the BOHT network of organizations and patient/caregiver partners. Over several months, the working group built potential personas of users, mapped their system navigation needs, created an inventory of navigation services within the network and designed the navigation app. Creation of the app was an iterative process including several testing and feedback loops to improve content and usability. Results: The first iteration of Burlington Navigation app was launched in Fall of 2021 accompanied by a comprehensive communication plan. The plan included digital signals at the hospital and primary care offices, social media posts and presentations to local providers including primary care physicians, community navigators. The first iteration focused on linking to health, social and community services offered by BOHT’s members and collaborators, and also services that were widely used in the community. Based on the feedback we received afterwards, the second iteration included links to provincial services. The third iteration includes capability to create a trusted account verified in the background by the same technology Interac uses. This will allow secure and seamless access to integrated services without a need for a separate username and password as well as access to the provincial patient portal Implications/highlights: Burlington Navigation app was the first community-based navigator app co-designed with patient partners, providers and community volunteers with lived experiences within the health and social care systems in Ontario. It resonated well with the users (providers and patients/caregivers) as local information became available at their fingertips. The success of the app allowed it to guide other OHTs on a similar digital navigator design journey. It also became the blueprint for a provincial solution for a multi-tenant navigator, which would allow multiple OHTs to showcase the local navigation information (based on geolocation) as well as provincial offerings. Conclusion: Sharing our journey co-developing the Burlington navigation app demonstrates that when local health integrated care networks utilize experienced-based co-design approaches to planning healthcare interventions, this has the potential to a) develop solutions that are well perceived by patients and providers and b) scale and spread these local solutions to larger-scale solutions that can improve the experiences of a wider spectrum of providers and patients.
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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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».