Health IT procurement: best practices and risk management for personal health record (PHR) implementation
Bibliographic record
Abstract
Electronic Personal Health Records (PHRs) are patient-centred health and/or medical records in electronic form. As healthcare authorities move in the direction of empowering consumers to take more responsibility for their own health through self management and education, implementing PHRs in a cost efficient and effective manner is becoming an important issue. Incorporating Health Information Technology (HIT) procurement best practices into a personal health record (PHR) project can be a valuable risk-reducing exercise. Unfortunately, a set of best practices does not yet exist. This paper investigates three important HIT procurement principles including: contract terms (software licensing and service level agreements), vendor relations (influencers, integrators, certification, request for proposals, the vendor evaluation matrix), and privacy. While neither prescriptive nor exhaustive, these three principles, when properly considered and applied may contribute to a best practices model of PHR procurement, significantly reducing the risks inherent in the procurement process.
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 | 0.039 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".