Personal Health Information in the Age of Ubiquitous Health
Bibliographic record
Abstract
We have long passed through the information age into an information perfusion in health care, and new strategies for managing it are emerging. The ubiquity of health information has transformed the clinician, the public, and the patient, forever changing the landscape of health care in the shift toward consumerism and the notion of the empowered patient. This chapter explores essential issues of ubiquitous health information (UHI), beginning with its origins in the explosion of health information and the advent of new technologies. Challenges of UHI include privacy issues, change management, and the lack of basic infrastructure. However, benefits for patients include improvements in access to information, communication with providers, prescription renewals, medication tracking, and the ability to self-manage their conditions. Benefits at the organizational level include increased patient satisfaction, continuity of care, changes in costing models and improved standardization of care as organizations streamline processes to address this change in clinical practice.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".