Nurse Practitioners Documentation Approach: A Scoping Review
Bibliographic record
Abstract
Aim: To identify barriers and facilitators to nurse practitioner (NP) documentation and highlight opportunities for improvement in practice and research. Background: Documentation is central to safe and effective NP practice. It ensures continuity of care, supports communication, and meets regulatory standards. Yet persistent challenges remain, shaped by organizational and educational factors. These issues can create inefficiencies, contribute to provider burden, and ultimately affect the quality of patient care. Understanding these influences is key to strengthening NP practice and outcomes. Methods: A scoping review was conducted using Arksey and O’Malley’s framework. Literature from 2014–2024 was systematically searched for studies on NP documentation. Of 153 articles screened, six met inclusion criteria. Data were charted and synthesized thematically to identify common patterns across studies. Findings: Two themes stood out: organizational culture and educational interventions. Facilitators included structured training, standardized tools, and streamlined processes. Barriers were linked to workflow inefficiencies, inconsistent templates, and limited support for electronic health record (EHR) systems. Although only a small number of studies met inclusion criteria, findings consistently emphasized the importance of system-level supports in strengthening documentation practices. Conclusion: Research on NP documentation is limited but growing. System-level changes—such as better EHR training, consistent templates, and supportive workplace cultures—can enhance accuracy and efficiency. Emerging technologies, including artificial intelligence (AI), also show promise in easing documentation burden, improving accuracy, and freeing NPs to focus more on patient care. Further research is needed to explore NP documentation reasoning and to evaluate targeted interventions that enhance practice and accountability.
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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 | 0.042 | 0.116 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.047 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".