Copy Tools in the Electronic Health Record: Perceptions, Implications, and Future Directions
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) can aid in provider efficiency, but may also lead to unintended consequences, such as documentation burden and increased length of notes. To combat issues related to documentation, copying and pasting (CP) and copying or carrying forward (CF) are tools that have been used to aid in documentation burden. Multiple studies have identified the benefits and challenges of using these tools; however, few studies have identified the unintended consequences of CP and CF, and how the adoption of these tools may affect users. OBJECTIVE: The objective was to describe providers' perceptions and use of copying tools available in the EHR and describe their suggestions for improvement on these copying tools. METHODS: Research team members conducted semistructured interviews with faculty members, advanced practice providers, residents or fellow trainees, and medical students at a single academic health sciences center. The Diffusion of Innovations Theory of Unintended Consequences guided the analysis and interpretation of interview results. RESULTS: A total of 22 semistructured interviews were conducted in 2023 and analyzed during 2024. The findings showed that respondents use and value these tools for efficiency and communication purposes. The negative unintended consequences include inaccuracies and errors in documentation and increased patient safety risks. Some respondents experience inner angst or moral injury related to using CP/CF, but they feel that they must use them to satisfy organizational requirements surrounding documentation. The respondents suggested that artificial intelligence will likely help improve documentation tools, as would further training around these types of documentation tools. CONCLUSIONS: Some respondents noted feeling both internal and external pressures that influenced when and how they use CP/CF. Respondents noted that they value EHR copying tools for efficiency purposes, but they also understand the risks involved. This tension may lead to moral angst or moral injury. They offered numerous suggestions for lowering the risk, especially by improving the documentation capabilities of the EHR through artificial intelligence. Future research should investigate both technical and educational solutions to relieve the documentation burden and moral angst they are experiencing.
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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".