Medical Informatics and the Quality of Health: New Approaches to Support Patient Care
Bibliographic record
Abstract
OBJECTIVES: The Yearbook of Medical Informatics is published annually by the International Medical Informatics Association (IMIA) and contains a selection of excellent papers on medical informatics research which have been recently published (http://www. yearbook.uni-hd.de). The 2003 Yearbook of Medical Informatics took as its theme the role of medical informatics for the quality of health care. In this paper, we will discuss challenges for health care, and the lessons learned from editing IMIA Yearbook 2003. RESULTS AND CONCLUSIONS: Modern information processing methodology and information and communication technology have strongly influenced our societies and health care. As a consequence of this, medical informatics as a discipline has taken a leading role in the further development of health care. This involves developing information systems that enhance opportunities for global access to health services and medical knowledge. Informatics methodology and technology will facilitate high quality of care in aging societies, and will decrease the possibilities of health care errors. It will also enable the dissemination of the latest medical and health information on the web to consumers and health care providers alike. The selected papers of the IMIA Yearbook 2003 present clear examples and future challenges, and they highlight how various sub-disciplines of medical informatics can contribute to this.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".