HUMAN-CENTERED HEALTH INFORMATICS: VISUAL SOLUTIONS IN EHR DEVELOPMENT
Bibliographic record
Abstract
The evolution of e-health systems—such as electronic health records (EHRs) and personal health records (PHRs)—is transforming the healthcare landscape by improving efficiency, patient safety, and cost-effectiveness. The adoption of computerized health information systems has demonstrated the potential to save approximately 60,000 lives annually, prevent over 500,000 medication errors, and reduce healthcare costs by an estimated $9.7 billion (Leapfrog, 2004). According to the World Health Organization, e-health refers to the cost-effective and secure use of information and communication technologies in support of healthcare services, health surveillance, education, and research.E-health spans a wide range of applications, including telemedicine, electronic medical records, telecare, and consumer health informatics. As seen in other information-intensive industries—such as finance, retail, and aviation—the integration of digital technologies enables greater value creation and system efficiency. In healthcare, these technologies address the growing complexity of patient care and the vast data volumes generated by increasing population and disease burdens.This paper examines the transformative potential of e-health systems in modern healthcare, focusing on their impact on service delivery, clinical outcomes, and data management. By leveraging technology, healthcare providers can streamline workflows, improve communication, and support informed decision-making—ultimately leading to enhanced patient care. The study emphasizes the strategic importance of e-health adoption in meeting future healthcare demands and promoting sustainable, data-driven health systems
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".