EMRs and Clinical IS Implementation in Hospitals: A Statewide Survey
Bibliographic record
Abstract
PURPOSE: Present an overview of clinical information systems (IS) in hospitals and analyze the level of electronic medical records (EMR) implementation in relation to clinical IS capabilities and organizational characteristics. METHODS: We developed a survey instrument measuring clinical IS implementation and classified clinical IS across 5 EMR levels. The survey was administered to hospitals in a state with a large number of rural hospitals (84% response rate). FINDINGS: Clinical IS were classified across 5 EMR levels, a useful approach for understanding the gaps in clinical IS in hospitals. Almost half (43%) of hospitals in Iowa were at EMR Level 0, with at least 1 of the ancillary systems still absent; 12% were at Level 1 with all 3 ancillary systems in place; 16% were at Level 2 corresponding to an early EMR system; 18% were at Level 3 corresponding to an intermediate EMR system; and 10% were at Level 4 corresponding to an advanced EMR system. In contrast, 22% had no plans for EMR implementation at all. EMR level was lower in critical access hospitals and positively associated with more slack resources and staffed beds. Over a 3-year period, there were increases in ancillary systems and clinical documentation implementation. CONCLUSIONS: The survey performed well. There was agreement with published estimates of EMR penetration, sensitivity to change over time, and association with known organizational factors. It is well designed and can be used to map onto a comprehensive classification scheme capturing the EMR level and evaluating progress toward meaningful use.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".