Does Electronic Health Record Systems Implementation Impact Hospital Efficiency, Profitability, and Quality?
Bibliographic record
Abstract
This paper empirically analyzed how electronic health records (EHR) systems impacted hospital operations. This study examined the merits of implementing EHR in operational efficiency, profitability, and service quality delivered to patients. Therefore, this research tested three hypotheses postulating overall positive associations of EHR implementation for the three areas, respectively. This paper used the 2015 American Hospital Association U. S. Hospital Survey dataset and the Hospital Consumer Assessment of Healthcare Providers and Systems dataset. To measure each hospital’s efficiency, this study developed a data envelopment analysis model with four inputs including beds, doctors, nurses, and total operating expenses, and three outputs including outpatient visits, inpatient days, and total patient revenues. This research used the operating margin to measure the hospital profitability, while patient experience ratings and readmission rates were used to measure the hospital quality. Results indicated that EHR implementing hospitals outperformed non-EHR implementing hospitals in operational efficiency, profitability, and quality.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".