Decreasing Missed Appointments at a Community Health Center: A Community Collaborative Project
Bibliographic record
Abstract
Introduction: Missed appointments are a problem for health care systems, causing lost revenue and concern for poor health outcomes. This is particularly true at Community Health Centers (CHCs), where clients may already face substantial barriers to optimal care and outcomes. Identified solutions to this problem are limited, and often focus on reminder calls and messages to clients. Methods: This project utilized a unique academic/CHC collaboration to investigate and initiate solutions to their high missed appointment rates. Client phone calls to determine clinic specific needs, monthly team meetings to brainstorm and choose initiatives, engaging stake holders, and phased implementation were the tools used to address the high missed appointment rates within the limitations of the clinic resources available. Results: Within one quarter, missed appointment rates at the clinic dropped by 6%-17% for different appointment types. Conclusion: While the project was interrupted due to the pandemic, early outcomes were promising and the model may be helpful to other CHCs with similar concerns.
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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 | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.016 |
| 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".