AI-Assisted Medical Records Management and EHR Workflow Optimization for Community Health Centres Serving Immigrant Populations
Bibliographic record
Abstract
This study investigates the persistent problem of documentation burden, incomplete patient records, multilingual communication challenges, and workflow inefficiencies in community health centres serving immigrant populations, where conventional EHR systems often struggle to capture and manage complex patient information effectively. The purpose of the research was to examine whether AI-assisted medical records management improves EHR workflow optimization, record completeness, multilingual documentation performance, service delivery quality, and overall operational effectiveness in these settings. The study adopted a quantitative, cross-sectional, case-based design and collected data from 200 respondents drawn from selected cloud-enabled and enterprise-style EHR case contexts within community health centres, including physicians, nurses, medical records officers, administrative staff, and IT/EHR support personnel. The key variables included AI-assisted medical records management, automated documentation support, intelligent record retrieval, error detection and validation, multilingual documentation support, EHR workflow optimization, record accuracy and completeness, service delivery quality, and operational effectiveness. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong positive perceptions of AI-supported records management, with mean scores of 3.98 for AI-assisted medical records management, 4.07 for EHR workflow optimization, 4.12 for record accuracy and completeness, 4.15 for multilingual documentation support, and 4.18 for service delivery quality. Reliability values were high, with Cronbach’s alpha ranging from 0.821 to 0.914. Correlation analysis revealed significant positive relationships between AI-assisted medical records management and EHR workflow optimization (r = 0.71, p < .001), record accuracy and completeness (r = 0.69, p < .001), and service delivery quality (r = 0.71, p < .001). Regression results further confirmed that AI-assisted medical records management significantly predicted EHR workflow optimization (β = 0.59, p < .001), while the overall model explained 46.2% of workflow variance (R² = 0.462, F(4,195) = 41.82, p < .001). The study implies that AI-enabled documentation systems can strengthen workflow efficiency, multilingual recordkeeping, and equitable service delivery in immigrant-focused community 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".