Impact of incentive driven medical home approach on use of preventive services use among healthcare system employees: A case study
Bibliographic record
Abstract
Objective: To describe the initial outcomes of an incentive driven medical home and navigation program on preventive services among healthcare system employees.Methods: Quasi-experimental design examining participation, use of preventive services and adherence to medical guidelines and emergency room use in a five hospital integrated health system. Employees were required to complete a health risk assessment (HRA), visit a Primary Care Provider (PCP) and submit PCP visit screening and biometric results in order to be eligible for the financial incentives. Subsidized lifestyle change intervention and navigation programs were also offered to participants. Descriptive statistics and Chi Square were used to analyze results for the 5,435 employee participants and 3,623 non-participants during thee 1-year intervention.Results: Preventive care visits for participants increased by 35% compared to an increase of 3% for non-participants. Nonadherence to medical guidelines decreased 7% for participants and increased 18% for non-participants. Inappropriate emergency room use overall decreased from 20% to 14%.Conclusions: One year after introduction of the wellness program, preventive visits increased, compliance with medical care increased and inappropriate emergency room visits were reduced.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".