Creating a Supportive Environment for Self-Management in Healthcare via Patient Electronic Tools
Bibliographic record
Abstract
Consumer electronic healthcare applications and tools, both Web-based and mobile apps, are increasingly available and used by citizens around the world. “eTools” denote the full range of electronic applications that consumers may use to assess, track, or treat their disease(s), including communicating with their healthcare provider. Consumer eTool use is prone to plateauing of use because it is one-sided (i.e., consumers use them without the assistance or advice of a healthcare provider). Patient eTools that allow patients to communicate with their healthcare providers, exchange data, and receive support and guidance between visits is a promising approach that could lead to more effective, sustained, and sustainable use of eTools. The key elements of a supportive environment for eTool use include 2-way data integration from patient home monitoring equipment to providers and from provider electronic medical records systems to patient eTools, mechanisms to support provider-patient communication between visits, the ability for providers to easily monitor incoming data from multiple patients, and for provider systems to leverage the team environment and delegate tasks to appropriate providers for education and follow-up. This is explored in this chapter.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".