Reducing Patient No-Shows: An Initiative at an Integrated Care Teaching Health Center
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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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".