Developing an Evidence-Based Epilepsy Risk Assessment eHealth Solution: From Concept to Market
Bibliographic record
Abstract
INTRODUCTION: Sudden unexpected death in epilepsy (SUDEP) is possibly the most common cause of death as a result of complications from epilepsy. The need to educate and regularly review risk for all patients with epilepsy is paramount, but rarely delivered in actual clinical practice. Evidence suggests that education around SUDEP and modifiable risk variables translate into better self-management of epilepsy. OBJECTIVE: We aimed to develop and implement an eHealth solution to support education and self-management of risks, in epilepsy. METHODS: We undertook an innovation pathways approach, including problem identification, feasibility assessment, design, implementation, and marketing. People with epilepsy were provided a smartphone-based app (Epilepsy Self-Monitor, EpSMon), which translates the clinical risk assessment tool into an educational and self-monitoring platform, for the self-management of epilepsy. RESULTS: Results include the success of the marketing campaign, and in what areas, with an estimated reach of approximately 38 million people. EpSMon has proved a success in academic and clinical circles, attracting awards and nominations for awards. The number of users of EpSMon, after 3 months, turned out to be lower than expected (N=221). A 4-month trial of the app in use in the United Kingdom, and the success of the marketing strategy, point to necessary changes to the model of delivery and marketing, summarized in this paper. These include the marketing message, user cost model, and need for the availability of an Android version. CONCLUSIONS: EpSMon has proven a success in respect to its reception by academics, clinicians, stakeholder groups, and the patients who use it. There is work needed to promote the model and increase its acceptability/attractiveness, including broadening the marketing message, increasing its availability, and reducing its cost. Future development and promotion of the tool will hopefully inform iterative design of its core features for a receptive audience and lead to increased uptake as it is launched worldwide in 2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".