Copy and paste in the electronic medical record: A scoping review
Bibliographic record
Abstract
Copy and paste (CPF) can be defined as the act of duplicating medical documentation from one section of the electronic medical record (EMR) and placing it verbatim in another section. The objective of this scoping review is to: 1) describe the prevalence of copy and paste usage in EMR documentation, 2) detail the known measurable safety hazards associated with its use, and 3) identify potential solutions and/or strategies that can be used to mitigate the negative consequences of the CPF while preserving its essential role in documentation efficiency. The Joanna Briggs Institute guidelines were used to identify, screen, and assess the text of articles for final inclusion in CPF article review. The primary search strategy for copy-paste articles was developed in PubMed® and then translated to CINAHL®, ScienceDirect®, and IEEExplore® to extract additional articles. Identified copy-paste articles were imported into Covidence®. Two reviewers determined the final articles that were included in the review. The search retrieved 63 publications of which 17 were identified for final inclusion. The scoping review revealed CPF of medical text is a common occurrence that cuts across all clinician types (e.g., physicians and nurses). The scoping review revealed that automated methods for finding duplication in electronic documentation had emerged. A limited number of studies with quantifiable harms associated with CPF were found. Clinicians stated that CPF: 1) had a negative impact on critical thinking, 2) led to medical complications being more likely to be overlooked, and 3) led to safety issues being missed with copy-paste content. A few different approaches were tested by researchers as alternatives to CPF. They included dictation systems, practice guidelines, note templates, highlighting of copied information, note splitting, and text insertion. CPF is long overdue for innovative approaches to minimizing patient risk and maximizing provider efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.288 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.059 | 0.054 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".