An Early Model for Value and Sustainability in Health Information Exchanges: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The primary value relative to health information exchange has been seen in terms of cost savings relative to laboratory and radiology testing, emergency department expenditures, and admissions. However, models are needed to statistically quantify value and sustainability and better understand the dependent and mediating factors that contribute to value and sustainability. OBJECTIVE: The purpose of this study was to provide a basis for early model development for health information exchange value and sustainability. METHODS: A qualitative study was conducted with 21 interviews of eHealth Exchange participants across 10 organizations. Using a grounded theory approach and 3.0 as a relative frequency threshold, 5 main categories and 16 subcategories emerged. RESULTS: This study identifies 3 core current perceived value factors and 5 potential perceived value factors-how interviewees predict health information exchanges may evolve as there are more participants. These value factors were used as the foundation for early model development for sustainability of health information exchange. CONCLUSIONS: Using the value factors from the interviews, the study provides the basis for early model development for health information exchange value and sustainability. This basis includes factors from the research: fostering consumer engagement; establishing a provider directory; quantifying use, cost, and clinical outcomes; ensuring data integrity through patient matching; and increasing awareness, usefulness, interoperability, and sustainability of eHealth Exchange.
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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 | 0.046 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".