Impact of a value-based insurance design for physical therapy to treat back pain on care utilization and cost
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of a value-based insurance design providing enhanced access to physical therapy (PT) for treatment of back pain on treatment patterns and cost of care. STUDY DESIGN: A retrospective analysis of claims data obtained from Geisinger Health Plan (GHP). In April 2013, GHP began offering "PT bundle" - i.e., a bundle of up to five PT visits for a single one-time copay that can be renewed for another bundle of five PT visits - for its employer-based plan members with back pain. METHODS: A cohort of GHP members who were preauthorized for the PT bundle were compared against a contemporaneous cohort of GHP members who were preauthorized for PT under the standard per-visit copay arrangement between January 2013 and October 2014. RESULTS: Among the PT bundle cohort, the PT visit rate during the first 9 months since the PT preauthorization date had dramatically increased and then gradually decreased in subsequent months. The PT bundle was also associated with 29%-35% short-term reductions in emergency department visits and with 12%-20% reductions in primary care visits after 6 months. No significant impact on hospitalization or cost was observed. CONCLUSION: Implementation of the PT bundle appears to have led to a change in the treatment pattern of back pain that is more consistent with the recommended guidelines to use more conservative management such as PT as the first-line treatment for back pain.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".