LEVERAGING DIGITAL WORKFLOWS TO TRANSITION THE ORTHOTICS AND PROSTHETICS PROFESSION TOWARD A CLIENT-CENTRIC AND VALUES-BASED CARE MODEL
Bibliographic record
Abstract
The orthotics and prosthetics (O&P) profession has a history of responding to market demands in a reactive rather than proactive manner. This has created significant impacts including shrinkage in scope of practice and constraint in remuneration for professional services due to a fee-for-device third party payer system. Rapid changes in technology and healthcare combined with an outdated device-centric reimbursement system are creating unprecedented challenges that threaten sustainability of the O&P profession. Hence, a reassessment of the value of O&P care, and the O&P workflow process is necessary to inform an update to the value proposition and practice model for sustainability. This article reviews key factors contributing to the current state of O&P, and potential solutions involving an update in practitioner competencies, and the care delivery model (from device-centric to client-centric and values-based). Updates could be achieved by leveraging the use of digital workflows that increase efficiencies and enhance the value of clinical outcomes. Eventually, these updates could enable the O&P profession to elevate the value proposition that aligns with its most important stakeholders: client-patients and third-party reimbursement agencies in a rapidly changing technology and healthcare landscape. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42221/32301 How To Cite: Hovorka C.F. Leveraging digital workflows to transition the Orthotics and Prosthetics profession toward a client-centric and values-based care model. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 2, No.6. https://doi.org/10.33137/cpoj.v6i2.42221 Corresponding Author: Christopher F. Hovorka, PhD, CPO, FAAOPCenter for the Intrepid, Department of Rehabilitation Medicine, Brooke Army Medical Center, San Antonio, TX, USA.E-Mail: hovorkac@gmail.comORCID ID: https://orcid.org/0000-0002-7147-3134
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".