The feasibility of implementing a pay-for-performance program in the treatment of alcohol/drug addiction: Implementation and initial results
Bibliographic record
Abstract
Background: This paper discusses the design and implementation of a pay-for-performance (P4P) initiative within the context of alcohol and drug addiction treatment. Though the use of P4P programs to enhance the quality of health care services has been increasing for certain chronic health conditions, these programs have been underutilized by providers of addiction treatment.Methods: Recently, as part of a new contractual agreement for patient care, a nationally-based alcohol/drug treatment provider collaborated with a major insurance payer to identify a set of metrics related to the quality of care. Selection of the metrics was guided by the Institute for Healthcare Improvement (IHI)’s Triple Aim, with measures representing the patient experience, patient engagement with services, and readmission to treatment services. Prior to the beginning of each contract year, targets for each metric were set based on historic baseline data, and the treatment provider was financially incentivized by the insurance payer to achieve the targets.Results: A higher number of metrics targets were achieved in the second year of the contract compared to the first.Conclusions: The experience of both organizations thus far demonstrates that implementation of P4P initiatives within addiction treatment is feasible, provided that both parties are committed to the success of the endeavor. Future studies should examine the efficacy of these programs with a research-based methodology.
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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 | 0.051 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".