Prescriptions for Investment in Health Information: Managing Risk for Maximum Benefit
Bibliographic record
Abstract
As we plan to increase our spending on health information technology in Canada, this article cautions that we must manage risk carefully and get the most out of our investments. The author outlines 13 principles for investments in information infrastructure that were derived from observations of successes and failures in health and other industries.These principles are: be certain funding is adequate; communicate project objectives in clinical or business terms; actively manage stakeholder expectations; where possible, fund results, not technology; learn from the successes and failures of others; plan for failure; put users in the driver’s seat; invest in success; build teams with experience; maintain strong communication links with stakeholders; include process design in every project; keep projects short; and avoid creating political footballs. Canada is on the verge of escalating its investments in health information systems. Spending by hospitals, health regions, governments and the private sector on health information is expected to double in the next five years. As we contemplate this increase in resources, it is essential that we look at the successes and failures of the past to inform our decisions about how best to allocate the
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 | 0.040 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".