Feasibility of Using Electronic Health Records for Cascade Monitoring and Cost Estimates in Implementation Science Studies in the Adolescent Trials Network for HIV/AIDS Interventions
Bibliographic record
Abstract
BACKGROUND: One of the most difficult areas in the fight against HIV/AIDS is reaching out to youth aged 13 to 24 years. The proportion of youth living with HIV/AIDS on antiretroviral therapy (ART) and who are virally undetectable is low, highlighting significant challenges for reaching the Joint United Nations Program on HIV targets. OBJECTIVE: This study aimed to assess the feasibility of obtaining key clinical indicators and monitoring treatment, viral suppression, and retention components of the youth HIV treatment cascade in Adolescent Trials Network for HIV/AIDS Interventions clinics using electronic health record (EHR) downloads and to provide baseline characteristics for the study participants. METHODS: EHR data were systematically obtained from multiple clinical sites and used to meaningfully capture clinical characteristics, initiation of antiretrovirals, and retention in care, which are part of the Centers for Disease Control and Prevention's 4 continuum of care measures. In addition, this study used standard cost values attached to Current Procedural Terminology codes to estimate the cost per visit. RESULTS: Only 2 of the 4 Centers for Disease Control and Prevention treatment cascade measures were assessed using routine EHR data. EHR data are not adequate for monitoring HIV testing or linkage to care because denominator data are not available. However, the data work well for measuring ART initiation and adequately for retention in care. The sites were broadly able to provide information for the required data. However, in most cases, these data are insufficient for identifying patterns of missed appointments because such misses are not captured in the EHR system. Sites with good access to data management resources can operate more efficiently for cascade monitoring study purposes. CONCLUSIONS: Data other than EHRs are needed to measure HIV testing and linkage to youth care. EHR data are useful for measuring ART initiation and work moderately well for measuring retention in care. Site data management resources should be part of the selection process when looking for site partners for clinical studies that plan to use EHR data. Study planners should determine the feasibility of additional funding for organizations in need of additional information technology or data management resources.
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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 | 0.356 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".