Adopting Healthcare Information Exchange among Organizations, Regions, and Hospital Systems toward Quality, Sustainability, and Effectiveness
Bibliographic record
Abstract
Adopting data and information integration and exchange between two or more organizations, regions, communities, health insurance companies, and hospital systems has gradually become a requirement to improve health care quality, sustainability in management of work processes, and efficiency and effectiveness in financial and administrative management through cost monitoring and improved clinical protocols. The central aim of this study was to demonstrate how the use of Health Information Exchange (HIE) models helps increase quality, sustainability, efficiency, and effectiveness through data and information integration and exchange. Data collection were performed through an assessment of the literature in the timeframe between 2002 and 2016 referring to academic or non-academic organizations. The analysis of the literature considered Brazilian, American, and European articles. The results from the research on electronic databases were not satisfactory for the Brazilian scenario, where this theme is still very recent. Results from statistical, quantitative, and qualitative analyses showing data, indices, and the use of ICTs through dedicated software showed that health data and information exchange is capable of improving: 1) the quality of care given to patients and employees of the institution; 2) sustainability in management of work processes and activities pertaining to the relationship between providers, users, and service offer; 3) effectiveness and efficiency in operational cost monitoring and service protocols, giving the health institutions a prominent position in their fields in the eyes of external investors. Regarding the appropriate, legally- and policy-compliant use of data and information exchange and integration, organizations must obey legal requirements in conformity with each country’s current legislation. Suggestions for future studies are given at the end.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".