Organizational Strategies for the Adoption of Electronic Medical Records: Toward an Understanding of Outcome Variation in Nursing Homes
Bibliographic record
Abstract
[Excerpt] An important element in president-elect Obama's economic stimulus proposal is his plan to invest a significant proportion of federal dollars in installing electronic medical records (EMR) in U.S. healthcare institutions. In emphasizing the need for EMR, Obama is following the advice of numerous healthcare experts who have pointed out that the healthcare sector lags behind other industries in the use of computer technology. They believe the widespread use of EMR would help reduce medical errors, control the costs of healthcare, and lead to significant improvements in the quality of care Americans receive. In this paper we present preliminary results of an ongoing study of the introduction of EMR in 20 nursing homes in the New York City area. Although most observers believe EMR holds great promise for the improvement of healthcare, in fact recent studies have found mixed evidence regarding the effect of EMR on patient outcomes. The evidence we have gathered to date suggests that whether EMR has beneficial effects on the costs and quality of healthcare depends very much on the purposes and objectives nursing home managers and administrators intend to achieve through its use. That is, management strategy and style, we believe, strongly influences healthcare outcomes associated with technological innovation.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".