Evaluating the Economic Impact of EHR Systems: Cost Analysis, ROI, and Strategic Efficiency Metrics in Diverse Healthcare Settings
Bibliographic record
Abstract
Electronic Health Record (EHR) systems have become central to healthcare delivery, yet their economic value varies widely across settings. This analysis examines the economics of EHR implementation through a comparative assessment of workflow efficiency and patient outcomes across diverse socioeconomic environments. A mixed-methods approach was applied, combining a systematic, PRISMA-guided review of the literature with quantitative analysis of economic data related to EHR adoption. Cost components were examined across hardware, software, and installation, alongside return on investment and break-even considerations using an established economic modeling approach. System performance was evaluated through efficiency indicators such as availability, reliability, and latency, while patient outcomes reflected changes in hospital costs, admission efficiency, quality of care, and recovery time. The results illustrate how variations in implementation costs, operational performance, and outcome measures shape the overall economic viability of EHR systems. Together, these findings highlight the importance of context-sensitive economic evaluation when assessing the value and sustainability of EHR investments across healthcare settings.
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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 | 0.078 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".