Health Care Information Technology in Rural America: Electronic Medical Record Adoption Status in Meeting the National Agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Continuing is a national political drive for investments in health care information technology (HIT) that will allow the transformation of health care for quality improvement and cost reduction. Despite several initiatives by the federal government to spur this development, HIT implementation has been limited, particularly in the rural market. The status of technology use in the transformation effort is reviewed by examining electronic medical records (EMRs), analyzing the existing rural environment, identifying barriers and factors affecting their development and implementation, and recommending needed steps to make this transformation occur, particularly in rural communities. A review of the literature for HIT in rural settings indicates that very little progress has been made in the adoption and use of HIT in rural America. Financial barriers and a large number of HIT vendors offering different solutions present significant risks to rural health care providers wanting to invest in HIT. Although evidence in the literature has demonstrated benefits of adopting HIT such as EMRs, important technical, policy, organizational, and financial barriers still exist that prevent the implementation of these systems in rural settings. To expedite the spread of HIT in rural America, federal and state governments along with private payers, who are important beneficiaries of HIT, must make difficult decisions as to who pays for the investment in this technology, along with driving standards, simplifying approaches for reductions in risk, and creating a workable operational plan.
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.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it