The Importance of Using Open Source Technologies and Common Standards for Interoperability within eHealth: Perspectives from the Millennium Villages Project
Bibliographic record
Abstract
PURPOSE: The purpose of this chapter is to illustrate the importance of using open source technologies and common standards for interoperability when implementing eHealth systems, and to illustrate this through case studies, where possible. DESIGN/METHODOLOGY/APPROACH: The sources used to inform this chapter draw from the implementation and evaluation of the eHealth Program in the context of the Millennium Villages Project (MVP). FINDINGS: As the eHealth Team was tasked to deploy an eHealth architecture, the Millennium Villages Global-Network (MVG-Net), across all 14 of the MVP sites in sub-Saharan Africa, the team not only recognized the need for standards and uniformity but also realized that context would be an important factor. Therefore, the team decided to utilize open source solutions. PRACTICAL IMPLICATIONS: The MVP implementation of MVG-Net provides a model for those looking to implement informatics solutions across disciplines and countries. Furthermore, there are valuable lessons learned that the eHealth community can benefit from. ORIGINALITY/VALUE: By sharing lessons learned and developing an accessible, open source eHealth platform, we believe that we can more efficiently and rapidly achieve the health-related and collaborative Millennium Development Goals.
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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 | 0.113 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".