The impact of electronic medical record system implementation on HCV screening and continuum of care: a systematic review
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Hepatitis C (HCV) screening is imperative to meet WHO elimination targets including increased detection and reduced mortality. An electronic medical record (EMR) system can be utilized in health care centers to indicate if a patient should be targeted for HCV screening, thus increasing the number of those offered testing. MATERIALS AND METHODS: We examined English language publications reporting on the impact of EMR system utilization on HCV screening and the HCV continuum of care. Relevant papers were identified using multiple search engines to search key terms. Clinical outcomes considered included any or no change in HCV screening rates following EMR system introduction, as well as any or no change in rates of patients progressing along the HCV cascade of care after diagnosis once an EMR system was implemented. RESULTS: From a search pool of 18 studies, 11 meet inclusion criteria and reported on the selected clinical outcomes. Each outcome assessed indicated that use of an EMR system increased the proportion of patients offered and/or receiving HCV testing. We were unable to conclude if an EMR system had an impact on the number of patients progressing along the HCV cascade of care following a positive test result. Overall, all methods of implementation of an EMR system had the same outcome of increasing screening rates. CONCLUSIONS: EMR system utilization had a positive impact on increasing HCV screening. However, the clinical effectiveness of utilizing an EMR system to help eliminate transmission and increase HCV treatment cure rates requires further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".