CONTEMPLATING HEALTH ECONOMICS, CODING AND REIMBURSEMENT IN ORTHOTICS, PROSTHETICS AND PEDORTHICS
Bibliographic record
Abstract
Reimbursement to U.S. healthcare service providers is largely transitioning from fee for service to fee for value for those clinicians who code using current procedural terminology and through their coding, describe their professional services. The Orthotic, Prosthetic and Pedorthic profession (O&P), currently codes using a system that describes the devices they evaluate for, fabricate, fit and maintain and their professional services are incorporated into their codes. These O&P codes, in contrast to those for other healthcare disciplines, are predominantly product based rather than service based, focusing on product features and function more than clinical service. This editorial manuscript provides a brief overview of the system the US O&P profession uses currently, particularly in the context of other healthcare professions transitioning to value based coding and reimbursement and culminates in a call to action for the profession to academically consider the strengths and weaknesses of the current system relative to alternative systems. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36125/28316 How To Cite: Highsmith MJ, Fantini CM, Smith DG. Contemplating health economics, coding and reimbursement in orthotics, prosthetics and pedorthics. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.5. https://doi.org/10.33137/cpoj.v4i2.36125 Corresponding Author: M. Jason Highsmith, PhD, DPT, CP, FAAOPSchool of Physical Therapy & Rehabilitation Sciences, Morsani College of Medicine, University of South Florida. Florida, USA.E-Mail: mhighsmi@usf.eduORCID ID: https://orcid.org/0000-0001-8361-7345
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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 | 0.035 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".