Utilizing Patient-Specific Factors to Predict Costs in Home-Based Medicare Part B Outpatient Physical Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The US health care system faces rising costs related to population aging, among other factors. One aspect of the high costs related to aging is Medicare outpatient therapy expenditures, which in 2010 totaled $5.642B for ∼4.7 million beneficiaries. Given the magnitude of these costs and the need to maximize value, this study developed and tested a predictive model of outpatient therapy costs. Retrospective analysis was performed on electronic medical record data from October 31, 2014-September 30, 2016 for 15,468 Medicare cases treated by physical therapists associated with a large, national rehabilitation provider. The analysis was a multiple linear regression of cost per case by 27 predictor variables: age group, sex, recent hospitalization, community vs. facility residence, the 10 states served, time from admission to initial evaluation, initial functional limitation reporting level, functional limitation reporting category, and 9 chronic conditions. The model was designed to be predictive and includes only variables available at the start of a case. The model was statistically significant (P < .0001) but explained only 7.4% of the variance in cost. Of the predictor variables, 16 had statistically significant effects. Those most highly predictive included state in which service was provided (8 of the 16 effects), and 3 variables indicating physical functioning at initial evaluation (initial functional limitation category and level, and residence in community vs. facility). There is need for more research focusing on the effects of specific types of treatment, and also for a more proactive model for outpatient therapy reimbursement that emphasizes prevention as well as treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it