Improving Iowa Research Network Patient Recruitment for an Advance Care Planning Study
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: In February 2019, recruitment began in Iowa Research Network offices for a Patient-Centered Outcomes Research Institute (PCORI) funded Advance Care Planning (ACP) study to be conducted in 7 primary care practice-based research networks across the United States and Canada. The main study trained clinicians and nursing staff in serious illness care conversations and requested they refer eligible patients. Eligible patients were those with serious illness or frailty expected to live 1 to 2 years. Clinicians indicated it was difficult to identify eligible patients. This study aimed to find better methods for increasing patient recruitment for the ACP study. METHODS: Research staff brainstormed and implemented strategies to increase patient referrals from clinicians. Participating offices used Epic for their medical record and the Gagne Index was used to generate a list of eligible patients in Epic SlicerDicer. When patients from the Epic SlicerDicer report appeared on the schedule, clinicians and nursing staff were notified that they might be eligible for ACP. Clinicians and nursing staff were asked to complete a survey identifying their perception of implemented strategies. A Wilcoxon signed-rank test was conducted to compare referral numbers before and after the Gagne Index/Epic SlicerDicer intervention. RESULTS: = .002). Survey results indicated that several strategies facilitated clinician referrals, including patients identified as potentially appropriate on the schedule, quarterly meetings with researchers, and e-mails with a list of potentially eligible patients. CONCLUSIONS: Notifying clinical staff about potential study participants increased patient referrals in this ACP study. Research staff must have time, funding, and patience to support clinical staff who are expected to refer patients to studies.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".