Role of multihospital system membership in electronic medical record adoption
Bibliographic record
Abstract
BACKGROUND: Health information technology (HIT) is designed to help reduce medical errors and improve quality of care and efficiency by providing the right information for the right patients in the right place at the right time. Nevertheless, substantial variation currently exists in the adoption of electronic medical records (EMRs) resulting in differences in hospital HIT capacity. PURPOSE: The purpose of this article is to examine the impact of different types of multihospital system affiliation on EMR adoption in hospitals in the United States. METHODOLOGY: A cross-sectional design was used with a sample of 4,017 hospitals in the United States. Secondary data were retrieved from the Health Information and Management Systems Society Analytics Database. Multiple regression analysis was used to examine the impact of multihospital system affiliation on EMR level of adoption. FINDINGS: The mean EMR adoption level varies significantly between independent hospitals and hospitals owned by a system for small hospitals. After adjusting for the number of operating rooms, the number of emergency room visits, and the number of hospital total full-time equivalent staff, small hospitals owned by multihospital systems were associated with 0.25 higher mean EMR adoption level (p < .05) than independent hospitals and no significant relationship was observed for hospitals that were leased/managed by a system. There was no significant effect of multihospital system membership on EMR level of adoption for medium and large hospitals. PRACTICE IMPLICATIONS: Small hospitals owned by multihospital systems had a significantly higher EMR level compared with independent hospitals. These findings have significant implications for small hospitals that are struggling to improve their HIT capacity. Smaller hospitals in ownership arrangements with larger health care systems have an advantage over small independent hospitals in HIT capacity possibly because of the greater availability of capital, access to shared HIT capacity, and other resources including technical expertise.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".