Assessing the Cost of Electronic Health Records: A Review of Cost Indicators
Bibliographic record
Abstract
We systematically reviewed PubMed and EBSCO business, looking for cost indicators of electronic health record (EHR) implementations and their associated benefit indicators. We provide a set of the most common cost and benefit (CB) indicators used in the EHR literature, as well as an overall estimate of the CB related to EHR implementation. Overall, CB evaluation of EHR implementation showed a rapid capital-recovering process. On average, the annual benefits were 76.5% of the first-year costs and 308.6% of the annual costs. However, the initial investments were not recovered in a few studied implementations. Distinctions in reporting fixed and variable costs are suggested. A literature review of 24 articles, identified from a pool of 92 articles, on electronic health records (EHRs) was conducted. The articles were found in PubMed and EBSCO Business databases. The review focused on cost indicators and associated benefits, a set of the most common cost and benefit indicators used in the EHR literature, as well as an overall estimate of the cost–benefit related to EHR implementation. Overall, cost–benefit evaluation of EHR implementation showed a rapid capital recovering process. On average, the annual benefits cost savings were 76.5% of the first-year costs, and 308.6% of the annual costs.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".