Identifying the Emerging e-Health Technologies To Ubiquity 2.0 and Beyond
Bibliographic record
Abstract
Achieving improvements and optimum healthcare delivery has become a bipartisan top priority for several governments and institutions. The ability to meet this goal depends on the exchange of information within and across healthcare communities. The real challenge for any healthcare initiative is at the application level, where patient data may be stored on hundreds of different clinical systems such as lab, radiology, or pharmacy systems, and various clinical applications such as electronic medical record (EHRs), that use different protocols and schemas. In an attempt to overcome these challenges, many organizations have used enterprise-oriented integration platforms to transform and translate information so that disparate systems could exchange information internally and externally. However, the development and ongoing maintenance of such healthcare systems has become extremely expensive due to the growing complexity of healthcare organizations as they acquire more systems to meet clinical and business needs. As a result, healthcare communities continue to face the same challenge: how to achieve a level of interoperability for accessing all relevant information about a patient from a single point, which is universally becoming the Web, as well as to ensure accuracy, security, and privacy of all the relevant data. This chapter provides a roadmap solution based on the emerging web technologies that hold great promise for addressing these challenges. The roadmap is termed as the “ubiquity 2.0 trend.” This chapter also highlights the security challenges and the emerging web-oriented identity management technologies to provide a single, common user credential that is trusted, secure, and widely supported across the Web and within the healthcare enterprises.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".