Wearables and the Internet of Things - Considerations for the life and health insurance industry
Notice bibliographique
Résumé
The aim of this research was to look at the emergence of wearable technology and the internet of things (IoT) and their current and potential use in the health and care insurance area. <br/>There is a wide and ever-expanding range of wearables, devices, apps, data aggregators, and platforms allowing the measurement, tracking and aggregation of a multitude of health and lifestyle measures, information and behaviours. The use and application of such technology and the corresponding richness of data that it can provide brings the health and care insurance market both potential opportunities and challenges. <br/>Insurers across a range of fields are already engaging with this type of technology in their proposition designs in areas such as customer engagement, marketing, and underwriting. However, it seems like we are just at the start of the journey, on a learning curve to finding the optimal practical applications of such technology with many aspects as yet untried, tested or indeed backed up with quantifiable evidence. <br/>It is clear though that technology is only part of the solution, on its own it won’t engage or change behaviours and insurers will need to consider this in terms of implementation and goals.<br/>In the first weeks of forming this working party, it became evident that the potential scope of this technology, the information already out there and the pace of development of it, is almost overwhelming. With many yet unanswered questions the paper focusses on pulling together in one place relevant information for the consideration of the health and care actuary, and also to open the reader’s eyes to potential future innovations by drawing on use of the technology in other markets and spheres, and the “science fiction like” new technology that is just around the corner.<br/>The paper explores:<br/>• An overview of wearables and IoT and available measures,<br/>• Examples of how this technology is currently being used,<br/>• Data considerations, <br/>• Risks and challenges,<br/>• Future technology developments, and <br/>• What this may mean for the future of insurance.<br/><br/>Insurers who engage now are likely to be on an evolving business case model and product development journey, over which they can build up their understanding and interpretation of the data that this technology can provide.<br/>An exciting area full of potential - when and how will you get involved?<br/>
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».