Integrating Information Technology to Healthcare and Healthcare Management: Improving Quality, Access, Efficiency, Equity, and Healthy Lives
Bibliographic record
Abstract
A study published in 2022 identified the top 10 Countries with the best healthcare as (1) Denmark, (2) Switzerland, (3) Australia, (4) France, (5) Singapore, (6) The United Kingdom, (7) Germany, (8) Canada, (9) Austria, and (10) Japan. The United States spends more money per capita on healthcare than any other country. Still, the United States healthcare did not make the top twenty. Another study published in 2021 ranked the US last in access to healthcare, equity, and outcomes among the 11 high income countries, despite spending a far greater share of its GDP on healthcare (Luhby, 2021). The World Health Organization’s ranking of the world’s health systems put France at #1and the United States at #37 (World Health Organization, 2019). This paper examines a broad IT related healthcare literature and seven key IT tools and technologies that should be integrated into a comprehensive U.S. healthcare system. Properly integrating these IT tools and technologies should narrow the gap and improve the five critical success factors: Quality, Access, Efficiency, Equity, and Healthy lives.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".