Impact of Heath Information Technology on the Quality of Patient Care.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationships among Electronic Health Record (EHR) adoption and adverse outcomes and satisfaction in hospitalized patients. MATERIALS AND METHODS: This secondary analysis of cross sectional data was compiled from four sources: (1) State Inpatient Database from the Healthcare Cost Utilization Project; (2) Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute; (3) Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) and (4) New Jersey nurse survey data. The final analytic sample consisted of data on 854,258 adult patients discharged from 70 New Jersey hospitals in 2006 and 7,679 nurses working in those same hospitals. The analytic approach used ordinary least squares and multiple regression models to estimate the effects of EHR adoption stage on the delivery of nursing care and patient outcomes, controlling for characteristics of patients, nurses, and hospitals. RESULTS: Advanced EHR adoption was independently associated with fewer patients with prolonged length of stay and seven-day readmissions. Advanced EHR adoption was not associated with patient satisfaction even when controlling for the strong relationships between better nursing practice environments, particularly staffing and resource adequacy, and missed nursing care and more patients reporting "Top-Box," satisfaction ratings. CONCLUSIONS: This innovative study demonstrated that advanced stages of EHR adoption show some promise in improving important patient outcomes of prolonged length of stay and hospital readmissions. Strongly evident by the relationships among better nursing work environments, better quality nursing care, and patient satisfaction is the importance of supporting the fundamentals of quality nursing care as technology is integrated into practice.
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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 | 0.002 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".