Reducing Patient No-Shows: An Initiative at an Integrated Care Teaching Health Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patient no-shows impede the effectiveness and efficiency of health care services delivery. OBJECTIVE: To evaluate a 2-phase intervention to reduce no-show rates at an integrated care community health center that incorporates a teaching program for osteopathic family medicine residents. METHODS: The Elmont Teaching Health Center (ETHC) is 1 of 5 community-based health centers comprising the Long Island Federally Qualified Health Centers. In August 2015, the ETHC implemented a centerwide No-Show Rates Reduction Initiative divided into an assessment phase and implementation phase. The assessment phase identified reasons most frequently cited by patients for no-shows at the ETHC. The implementation phase, initiated in mid-September, addressed these reasons by focusing on reminder call verification, patient education, personal responses to patient calls, institutional awareness, and integration with multiple departments. To assess the initiative, monthly no-show rates were compared by quarter for 2015 and against rates for the previous year. RESULTS: We recorded 27,826 appointments with 6147 no-shows in 2014 and 31,696 appointments with 5690 no-shows in 2015. No-show rates in the first 3 quarters of 2015 (range, 18.2%-20.0%) were slightly lower than the rates in 2014 (20.1%-23.4%) and then changed by an increasingly wide margin in the last quarter of 2015 (15.3%), leading to a significant year (2014, 2015) by quarter (Q1, Q2, Q3, Q4) interaction (P=.004). Also, the change observed in Q4 in 2015 differed significantly from Q1 (P=.017), Q2 (P=.004), and Q3 (P=.027) in 2015, while Q1, Q2, and Q3 in 2015 did not significantly differ from one another. CONCLUSION: No-show rates were successfully reduced after a 2-phase intervention was implemented at 1 health center within a larger health care organization. Future directions include dismantling the individual components of the intervention, evaluating the role of patient volumes in no-show rates, assessing patient outcomes (eg, costs, health) in integrative care settings that treat underserved populations, and evaluating family medicine residents' training on continuity of care and no-show rates.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it