Looking for the Evidence: Value of Health Informatics. Editorial
Bibliographic record
Abstract
OBJECTIVES: To provide an editorial introduction to the 2013 IMIA Yearbook of Medical Informatics with an overview of its contents and contributors. METHODS: A brief overview of the main theme, and an outline of the purposes, contents, format, and acknowledgment of contributions. RESULTS: Health information technology (HIT) is currently widely implemented to improve healthcare quality and patient safety while reducing costs. Although these benefits are expected and largely advertised, the evidence for these benefits is still missing. Unintended consequences are often reported and some applications have been shown to be wasteful, harmful, and even fatal. Evidence-based health informatics has been defined as "the conscientious, explicit and judicious use of current best evidence when making decisions about the introduction and operation of information technology in a given health care setting". The 2013 issue of the IMIA Yearbook highlights important contributions about the significant challenges that arise from the assessment of HIT solutions. Progress towards evidence-based health informatics is identified to elicit what works, what doesn't work, and why. In an environment where resources are limited, budgets lower than in past years, and the need to improve care is becoming ever more pressing, focusing on this topic should guide institutions and providers in the implementation of the best health information technology. CONCLUSION: This overview of progress and current challenges across the spectrum of the discipline shows many great examples of evidence that have been gathered on the effectiveness of HIT. However, evidence remains limited and a significant work should be conducted to improve the development, testing, and implementation of HIT applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".