Electronic Patient-Generated Health Data for Healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gathering and sharing by individuals of their health-related data to enhance their medical care or personal wellness are popular and growing rapidly. As a relatively new field, nomenclature is variable, but this is termed patient-generated health data, person-generated health data, or simply PGHD. This chapter introduces the concept of PGHD. Essential nomenclature is provided, and a model of the purpose, flow, and use of PGHD is presented and discussed. Benefits and challenges are noted, and legal, regulatory, and ethical issues are briefly outlined. Although benefits of PGHD are perceived or inherently believed, the available empirical evidence for improved and collaborative healthcare monitoring and management is slight. Also, there are many challenges. Some of these noted challenges include smart device regulation and reliability, data quality, integration into healthcare processes (adoption), and data integration into records (interoperability). Furthermore, there are legal, regulatory, and ethical issues. Widespread adoption and use of PGHD will require more definitive research into evidence of benefits, and efficient and effective resolution of the challenges.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it