Can Administrative Claim File Review Be Used to Gather Physical Therapy, Occupational Therapy, and Psychology Payment Data and Functional Independence Measure Scores? Implications for Rehabilitation Providers in the Private Health Sector
Bibliographic record
Abstract
PURPOSE: Rehabilitation costs for spinal-cord injury (SCI) are increasingly borne by Canada's private health system. Because of poor outcomes, payers are questioning the value of their expenditures, but there is a paucity of data informing analysis of rehabilitation costs and outcomes. This study evaluated the feasibility of using administrative claim file review to extract rehabilitation payment data and functional status for a sample of persons with work-related SCI. METHODS: Researchers reviewed 28 administrative e-claim files for persons who sustained a work-related SCI between 1996 and 2000. Payment data were extracted for physical therapy (PT), occupational therapy (OT), and psychology services. Functional Independence Measure (FIM) scores were targeted as a surrogate measure for functional outcome. Feasibility was tested using an existing approach for evaluating health services data. RESULTS: The process of administrative e-claim file review was not practical for extraction of the targeted data. CONCLUSIONS: While administrative claim files contain some rehabilitation payment and outcome data, in their present form the data are not suitable to inform rehabilitation services research. A new strategy to standardize collection, recording, and sharing of data in the rehabilitation industry should be explored as a means of promoting best practices.
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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 | 0.109 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".