Wearables and the Internet of Things - Considerations for the life and health insurance industry
Bibliographic record
Abstract
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/>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".