Bibliographic record
Abstract
Healthcare organizations around the world are facing unprecedented challenges in providing healthcare services at lower cost, while improving the level of service to their patients. To reduce costs, while meeting the demand for better quality outcomes, emphasis is being placed on preventative efforts, better access, and a personalized patient-centric experience. Canada is moving toward a healthcare model in which the patient is at the centre of care. The lifeblood of meeting these challenges will be regulatory changes, improved data, and technology. Patient empowerment over their healthcare continuum and privacy of their data is now at the forefront of major healthcare decisions. Due to the vast number of elements involved, including government, industry, technology, data sources, facilities, healthcare scenarios and an increasing range of patients' expectations, any solution must be well orchestrated and in concert with the other variables. All advances in healthcare technology, including Artificial Intelligence (AI), real-time monitoring with wearables and injectables, consolidated and enhanced data, point to a very different world of medicine in the near future. A rich and current aggregation of patient data forms the backbone of advanced healthcare. We define data usability as the ability of a clinician to quickly locate and assimilate the right patient data to determine optimal treatment. The key measurements associated with the research are in the area of clinicians saving time and being provided advanced diagnostic capability. This manuscript posits that improving data usability will be key to successfully achieving two objectives, one for the clinician and one for the patient. These objectives are: Provide a method that allows patients to pre-record and dynamically change their privacy decisions to restrict what data can be viewed by a clinician and allows patients to view who has accessed their data. Develop a data usability framework that will improve the usability of patient healthcare data empowering clinicians to more quickly identify and apply treatment to the benefit of the patient’s health. In this thesis, there are three contributions to the body of knowledge aligned with improving data usability for clinicians and patients. The first is the development of a new method to allow patients to manage the privacy of their data. The second is the creation of a framework comprised of an orchestration of features that collectively improve data usability for clinicians. The third contribution is the development of a new algorithm for defining and measuring data usability.
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 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 | 0.114 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".