Assessing the potential of national strategies for electronic health records for population health monitoring and research.
Bibliographic record
Abstract
OBJECTIVES: This report assesses the potential of national strategies for electronic health records for population health monitoring and research. METHODS: This study: (1) Reviewed national strategies for electronic health records in Australia, Canada, England, and New Zealand, through written materials available before January 2006. (2) Identified the potential of national strategies for electronic health records for population health monitoring and research through interviews with 96 experts in the U.S., Australia, Canada, England, and New Zealand. (3) Delineated fundamental issues that must be confronted to maximize the contribution of national strategies for electronic health records to population health monitoring and research. RESULTS: National strategies for electronic health records reflect the political, healthcare, and market systems of individual countries. National strategies also reflect technical decisions and political judgments. National strategies are evolving, and passing through stages of conceptualization, design, pilot testing, and implementation. Only England has moved to implementation. Population health monitoring and research are secondary to the primary uses of clinical care and management in all national strategies for electronic health records. Only England has conceptualized, designed, and is implementing the use of electronic health records for population health monitoring and research. Canada's strategy includes communicable disease surveillance, but not broader population health monitoring for developing health statistics. This study identifies definitional, numerator, denominator, and overarching issues that must be evaluated in assessing the potential of national strategies for electronic health records for population health monitoring and research. It delineates success factors that increase the potential for those national strategies to contribute to population health monitoring and research. Finally, this study assesses barriers that must be overcome if national strategies for electronic health records can contribute to population health monitoring and research, and especially to health statistics.
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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 | 0.387 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".