Barriers Over Time to Full Implementation of Health Information Exchange in the United States
Bibliographic record
Abstract
BACKGROUND: Although health information exchanges (HIE) have existed since their introduction by President Bush in his 2004 State of the Union Address, and despite monetary incentives earmarked in 2009 by the health information technology for economic and clinical health (HITECH) Act, adoption of HIE has been sparse in the United States. Research has been conducted to explore the concept of HIE and its benefit to patients, but viable business plans for their existence are rare, and so far, no research has been conducted on the dynamic nature of barriers over time. OBJECTIVE: The aim of this study is to map the barriers mentioned in the literature to illustrate the effect, if any, of barriers discussed with respect to the HITECH Act from 2009 to the early months of 2014. METHODS: We conducted a systematic literature review from CINAHL, PubMed, and Google Scholar. The search criteria primarily focused on studies. Each article was read by at least two of the authors, and a final set was established for evaluation (n=28). RESULTS: The 28 articles identified 16 barriers. Cost and efficiency/workflow were identified 15% and 13% of all instances of barriers mentioned in literature, respectively. The years 2010 and 2011 were the most plentiful years when barriers were discussed, with 75% and 69% of all barriers listed, respectively. CONCLUSIONS: The frequency of barriers mentioned in literature demonstrates the mindfulness of users, developers, and both local and national government. The broad conclusion is that public policy masks the effects of some barriers, while revealing others. However, a deleterious effect can be inferred when the public funds are exhausted. Public policy will need to lever incentives to overcome many of the barriers such as cost and impediments to competition. Process improvement managers need to optimize the efficiency of current practices at the point of care. Developers will need to work with users to ensure tools that use HIE resources work into existing workflows.
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 | 0.009 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".